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Sample records for neural system birth

  1. Learning-induced neural plasticity of speech processing before birth.

    Science.gov (United States)

    Partanen, Eino; Kujala, Teija; Näätänen, Risto; Liitola, Auli; Sambeth, Anke; Huotilainen, Minna

    2013-09-10

    Learning, the foundation of adaptive and intelligent behavior, is based on plastic changes in neural assemblies, reflected by the modulation of electric brain responses. In infancy, auditory learning implicates the formation and strengthening of neural long-term memory traces, improving discrimination skills, in particular those forming the prerequisites for speech perception and understanding. Although previous behavioral observations show that newborns react differentially to unfamiliar sounds vs. familiar sound material that they were exposed to as fetuses, the neural basis of fetal learning has not thus far been investigated. Here we demonstrate direct neural correlates of human fetal learning of speech-like auditory stimuli. We presented variants of words to fetuses; unlike infants with no exposure to these stimuli, the exposed fetuses showed enhanced brain activity (mismatch responses) in response to pitch changes for the trained variants after birth. Furthermore, a significant correlation existed between the amount of prenatal exposure and brain activity, with greater activity being associated with a higher amount of prenatal speech exposure. Moreover, the learning effect was generalized to other types of similar speech sounds not included in the training material. Consequently, our results indicate neural commitment specifically tuned to the speech features heard before birth and their memory representations.

  2. Birth prevalence of neural tube defects and orofacial clefts in India: a systematic review and meta-analysis.

    Directory of Open Access Journals (Sweden)

    Komal Preet Allagh

    Full Text Available In the last two decades, India has witnessed a substantial decrease in infant mortality attributed to infectious disease and malnutrition. However, the mortality attributed to birth defects remains constant. Studies on the prevalence of birth defects such as neural tube defects and orofacial clefts in India have reported inconsistent results. Therefore, we conducted a systematic review of observational studies to document the birth prevalence of neural tube defects and orofacial clefts.A comprehensive literature search for observational studies was conducted in MEDLINE and EMBASE databases using key MeSH terms (neural tube defects OR cleft lip OR cleft palate AND Prevalence AND India. Two reviewers independently reviewed the retrieved studies, and studies satisfying the eligibility were included. The quality of included studies was assessed using selected criteria from STROBE statement.The overall pooled birth prevalence (random effect of neural tube defects in India is 4.5 per 1000 total births (95% CI 4.2 to 4.9. The overall pooled birth prevalence (random effect of orofacial clefts is 1.3 per 1000 total births (95% CI 1.1 to 1.5. Subgroup analyses were performed by region, time period, consanguinity, and gender of newborn.The overall prevalence of neural tube defects from India is high compared to other regions of the world, while that of orofacial clefts is similar to other countries. The majority of studies included in the review were hospital based. The quality of these studies ranged from low to moderate. Further well-designed, high quality community-based observational studies are needed to accurately estimate the burden of neural tube defects and orofacial clefts in India.

  3. A comparison of neural tube defects identified by two independent routine recording systems for congenital malformations in Northern Ireland.

    Science.gov (United States)

    Nevin, N C; McDonald, J R; Walby, A L

    1978-12-01

    The efficiency of two systems for recording congenital malformations has been compared; one system, the Registrar General's Congenital Malformation Notification, is based on registering all malformed infants, and the other, the Child Health System, records all births. In Northern Ireland for three years [1974--1976], using multiple sources of ascertainment, a total of 686 infants with neural tube defects was identified among 79 783 live and stillbirths. The incidence for all neural tube defects in 8 60 per 1 000 births. The Registrar General's Congenital Malformation Notification System identified 83.6% whereas the Child Health System identified only 63.3% of all neural tube defects. Both systems together identified 86.2% of all neural tube defects. The two systems are suitable for monitoring of malformations and the addition of information from the Genetic Counselling Clinics would enhance the data for epidemiological studies.

  4. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  5. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  6. Assessing the prevalence of spina bifida and encephalocele in a Kenyan hospital from 2005-2010: implications for a neural tube defects surveillance system.

    Science.gov (United States)

    Githuku, Jane N; Azofeifa, Alejandro; Valencia, Diana; Ao, Trong; Hamner, Heather; Amwayi, Samuel; Gura, Zeinab; Omolo, Jared; Albright, Leland; Guo, Jing; Arvelo, Wences

    2014-01-01

    Neural tube defects such as anencephaly, spina bifida, and encephalocele are congenital anomalies of the central nervous system. Data on the prevalence of neural tube defects in Kenya are limited. This study characterizes and estimates the prevalence of spina bifida and encephalocele reported in a referral hospital in Kenya from 2005-2010. Cases were defined as a diagnosis of spina bifida or encephalocele. Prevalence was calculated as the number of cases by year and province of residence divided by the total number of live-births per province. From a total of 6,041 surgical records; 1,184 (93%) had reported diagnosis of spina bifida and 88 (7%) of encephalocele. Estimated prevalence of spina bifida and encephalocele from 2005-2010 was 3.3 [95% Confidence Interval (CI): 3.1-3.5] cases per 10,000 live-births. The highest prevalence of cases were reported in 2007 with 4.4 (95% CI: 3.9-5.0) cases per 10,000 live-births. Rift Valley province had the highest prevalence of spina bifida and encephalocele at 6.9 (95% CI: 6.3-7.5) cases per 10,000 live-births from 2005-2010. Prevalence of spina bifida and encephalocele is likely underestimated, as only patients seeking care at the hospital were included. Variations in regional prevalence could be due to referral patterns and healthcare access. Implementation of a neural tube defects surveillance system would provide a more thorough assessment of the burden of neural tube defects in Kenya.

  7. Assessing the prevalence of spina bifida and encephalocele in a Kenyan hospital from 2005–2010: implications for a neural tube defects surveillance system

    Science.gov (United States)

    Githuku, Jane N; Azofeifa, Alejandro; Valencia, Diana; Ao, Trong; Hamner, Heather; Amwayi, Samuel; Gura, Zeinab; Omolo, Jared; Albright, Leland; Guo, Jing; Arvelo, Wences

    2014-01-01

    Introduction Neural tube defects such as anencephaly, spina bifida, and encephalocele are congenital anomalies of the central nervous system. Data on the prevalence of neural tube defects in Kenya are limited. This study characterizes and estimates the prevalence of spina bifida and encephalocele reported in a referral hospital in Kenya from 2005-2010. Methods Cases were defined as a diagnosis of spina bifida or encephalocele. Prevalence was calculated as the number of cases by year and province of residence divided by the total number of live-births per province. Results From a total of 6,041 surgical records; 1,184 (93%) had reported diagnosis of spina bifida and 88 (7%) of encephalocele. Estimated prevalence of spina bifida and encephalocele from 2005-2010 was 3.3 [95% Confidence Interval (CI): 3.1-3.5] cases per 10,000 live-births. The highest prevalence of cases were reported in 2007 with 4.4 (95% CI: 3.9-5.0) cases per 10,000 live-births. Rift Valley province had the highest prevalence of spina bifida and encephalocele at 6.9 (95% CI: 6.3-7.5) cases per 10,000 live-births from 2005-2010. Conclusion Prevalence of spina bifida and encephalocele is likely underestimated, as only patients seeking care at the hospital were included. Variations in regional prevalence could be due to referral patterns and healthcare access. Implementation of a neural tube defects surveillance system would provide a more thorough assessment of the burden of neural tube defects in Kenya. PMID:26113894

  8. Neural Tube Defects

    Science.gov (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  9. Latent iron deficiency at birth influences auditory neural maturation in late preterm and term infants.

    Science.gov (United States)

    Choudhury, Vivek; Amin, Sanjiv B; Agarwal, Asha; Srivastava, L M; Soni, Arun; Saluja, Satish

    2015-11-01

    In utero latent iron deficiency has been associated with abnormal neurodevelopmental outcomes during childhood. Its concomitant effect on auditory neural maturation has not been well studied in late preterm and term infants. The objective was to determine whether in utero iron status is associated with auditory neural maturation in late preterm and term infants. This prospective cohort study was performed at Sir Ganga Ram Hospital, New Delhi, India. Infants with a gestational age ≥34 wk were eligible unless they met the exclusion criteria: craniofacial anomalies, chromosomal disorders, hemolytic disease, multiple gestation, third-trimester maternal infection, chorioamnionitis, toxoplasmosis, other infections, rubella, cytomegalovirus infection, and herpes simplex virus infections (TORCH), Apgar score 75 ng/mL) at birth. Twenty-three infants had latent iron deficiency. Infants with latent iron deficiency had significantly prolonged wave V latencies (7.10 ± 0.68 compared with 6.60 ± 0.66), III-V interpeak latencies (2.37 ± 0.64 compared with 2.07 ± 0.33), and I-V interpeak latencies (5.10 ± 0.57 compared with 4.72 ± 0.56) compared with infants with normal iron status (P neural maturation in infants at ≥34 wk gestational age. This trial was registered at clinicaltrials.gov as NCT02503397. © 2015 American Society for Nutrition.

  10. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  11. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  12. Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

    Science.gov (United States)

    Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

    2015-01-01

    Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

  13. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  14. Birth order dependent growth cone segregation determines synaptic layer identity in the Drosophila visual system.

    Science.gov (United States)

    Kulkarni, Abhishek; Ertekin, Deniz; Lee, Chi-Hon; Hummel, Thomas

    2016-03-17

    The precise recognition of appropriate synaptic partner neurons is a critical step during neural circuit assembly. However, little is known about the developmental context in which recognition specificity is important to establish synaptic contacts. We show that in the Drosophila visual system, sequential segregation of photoreceptor afferents, reflecting their birth order, lead to differential positioning of their growth cones in the early target region. By combining loss- and gain-of-function analyses we demonstrate that relative differences in the expression of the transcription factor Sequoia regulate R cell growth cone segregation. This initial growth cone positioning is consolidated via cell-adhesion molecule Capricious in R8 axons. Further, we show that the initial growth cone positioning determines synaptic layer selection through proximity-based axon-target interactions. Taken together, we demonstrate that birth order dependent pre-patterning of afferent growth cones is an essential pre-requisite for the identification of synaptic partner neurons during visual map formation in Drosophila.

  15. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  16. How the integration of traditional birth attendants with formal health systems can increase skilled birth attendance.

    Science.gov (United States)

    Byrne, Abbey; Morgan, Alison

    2011-11-01

    Forty years of safe motherhood programming has demonstrated that isolated interventions will not reduce maternal mortality sufficiently to achieve MDG 5. Although skilled birth attendants (SBAs) can intervene to save lives, traditional birth attendants (TBAs) are often preferred by communities. Considering the value of both TBAs and SBAs, it is important to review strategies for maximizing their respective strengths. To describe mechanisms to integrate TBAs with the health system to increase skilled birth attendance and examine the components of successful integration. A systematic review of interventions linking TBAs and formal health workers, measuring outcomes of skilled birth attendance, referrals, and facility deliveries. Thirty-three articles met the selection criteria. Mechanisms used for integration included training and supervision of TBAs, collaboration skills for health workers, inclusion of TBAs at health facilities, communication systems, and clear definition of roles. Impact on skilled birth attendance depended on selection of TBAs, community participation, and addressing barriers to access. Successful approaches were context-specific. The integration of TBAs with formal health systems increases skilled birth attendance. The greatest impact is seen when TBA integration is combined with complementary actions to overcome context-specific barriers to contact among SBAs, TBAs, and women. Copyright © 2011 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

  17. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

    The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

  18. Distillability Sudden Birth of Entanglement for Qutrit-Qutrit Systems

    International Nuclear Information System (INIS)

    Huang Jiang; Ali Mazhar

    2014-01-01

    We report the sudden appearance of distillability between two statistically independent reservoirs modelled as qutrit-qutrit systems. This feature of bipartite quantum systems is different from the previously observed phenomenon of entanglement sudden birth. It is found that the states of reservoirs first become bound entangled, thus exhibiting entanglement sudden birth, consequently followed by the sudden birth of distillability, and it is shown that whenever distillability is lost abruptly from principal system, it also necessarily appears abruptly among reservoirs' degrees of freedom. This surprising observation reflects yet another peculiarity of dynamical aspects of quantum entanglement

  19. Neural neworks in a management information systems

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2009-01-01

    Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.

  20. Collaborative Recurrent Neural Networks forDynamic Recommender Systems

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population

  1. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

    Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

    2017-12-01

    Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

  2. Magnitude of Birth Defects in Central and Northwest Ethiopia from 2010-2014: A Descriptive Retrospective Study.

    Directory of Open Access Journals (Sweden)

    Molla Taye

    Full Text Available Birth defects are defined as structural and functional defects that develop during the organogenesis period and present at birth or detected later in life. They are one of the leading causes of infant and child mortality, morbidity, and long term disability. The magnitude of birth defects varies from country to country and from race/ethnicity to race/ethnicity, and about 40-60% of their causes are unknown. The known causes of birth defects are genetic and environmental factors which may be prevented. For various reasons, there is lack of data and research on birth defects in Ethiopia.The major objective of this study is to estimate the magnitude of birth defects in Ethiopia.A hospital based, retrospective, cross sectional, descriptive study was conducted. The subjects were babies/children aged 0-17years who visited selected hospitals between 2010 and 2014. Fourteen hospitals (8 in Addis Ababa, 6 in Amhara Region were selected purposively based on case load. A data retrieving form was developed to extract relevant information from record books.In the hospitals mentioned, 319,776 various medical records of children aged 0-17years were found. Of these, 6,076 (1.9% with 95% CI: 1.85%-1.95% children were diagnosed as having birth defects. The majority (58.5% of the children were male and 41.5% female. A slightly more than half (51.1% of the children were urban dwellers, while 48.9% were from rural areas. Among the participants of the study the proportion of birth defects ranged as follows: orofacial (34.2%, neural tube (30.8%, upper and lower limb (12.8%, cardiovascular system (10.3%, digestive system and abdominal wall (4.8%, unspecified congenital malformations (2.5%, Down syndrome (2%, genitourinary system (2%, head, face, and neck defects (0.4%, and others (0.3%. The trend of birth defects increased linearly over time [Extended Mantel-Haenszel chi square for linear trend = 356.7 (P<0.0001]. About 275 (4.5% of the cases had multiple (associated

  3. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  4. Home birth integration into the health care systems of eleven international jurisdictions.

    Science.gov (United States)

    Comeau, Amanda; Hutton, Eileen K; Simioni, Julia; Anvari, Ella; Bowen, Megan; Kruegar, Samantha; Darling, Elizabeth K

    2018-02-13

    The purpose of this study was to develop assessment criteria that could be used to examine the level of integration of home birth within larger health care systems in developed countries across 11 international jurisdictions. An expert panel developed criteria and a definition to assess home birth integration within health care systems. We selected jurisdictions based on the publications that were eligible for inclusion in our systematic review and meta-analysis on planned place of birth. We sent the authors of the included publications a questionnaire about home birth practitioners and practices in their respective health care system at the time of their studies. We searched published peer-reviewed, non-peer-reviewed, and gray literature, and the websites of professional bodies to document information about home birth integration in each jurisdiction based on our criteria. Where information was lacking, we contacted experts in the field from the relevant jurisdiction. Home birth is well integrated into the health care system in British Columbia (Canada), England, Iceland, the Netherlands, New Zealand, Ontario (Canada), and Washington State (USA). Home birth is less well integrated into the health care system in Australia, Japan, Norway, and Sweden. This paper is the first to propose criteria for the evaluation of home birth integration within larger maternity care systems. Application of these criteria across 11 international jurisdictions indicates differences in the recognition and training of home birth practitioners, in access to hospital facilities, and in the supplies and equipment available at home births, which give rise to variation in the level of integration across different settings. Standardized criteria for the evaluation of systems integration are essential for interpreting planned home birth outcomes that emerge from contextual differences. © 2018 Wiley Periodicals, Inc.

  5. International retrospective cohort study of neural tube defects in relation to folic acid recommendations : are the recommendations working?

    NARCIS (Netherlands)

    Botto, LD; Lisi, A; Robert-Gnansia, E; Erickson, JD; Vollset, SE; Mastroiacovo, P; Botting, B; Cocchi, G; de Vigan, C; de Walle, H; Feijoo, M; Irgens, LM; McDonnell, B; Merlob, P; Ritvanen, A; Scarano, G; Siffel, C; Metneki, J; Stoll, C; Smithells, R; Goujard, J

    2005-01-01

    Objective To evaluate the effectiveness of policies and recommendations on folic acid aimed at reducing the occurrence of neural tube defects. Design Retrospective cohort study of births monitored by birth defect registries. Setting 13 birth defects registries monitoring rates of neural tube defects

  6. System and method for determining stability of a neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

    Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

  7. Epidemiologic study of neural tube defects in Los Angeles County. I. Prevalence at birth based on multiple sources of case ascertainment

    Energy Technology Data Exchange (ETDEWEB)

    Sever, L.E. (Pacific Northwest Lab., Richland, WA); Sanders, M.; Monsen, R.

    1982-01-01

    Epidemiologic studies of the neural tube defects (NTDs), anencephalus and spina bifida, have for the most part been based on single sources of case ascertainment in past studies. The present investigation attempts total ascertainment of NTD cases in the newborn population of Los Angeles County residents for the period 1966 to 1972. Design of the study, sources of data, and estimates of prevalence rates based on single and multiple sources of case ascertainment are here discussed. Anencephalus cases totaled 448, spina bifida 442, and encephalocele 72, giving prevalence rates of 0.52, 0.51, and 0.08 per 1000 total births, respectively, for these neural tube defects - rates considered to be low. The Los Angeles County prevalence rates are compared with those of other recent North American studies and support is provided for earlier suggestions of low rates on the West Coast.

  8. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

    2002-11-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

  9. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

  10. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  11. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  12. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

  13. Analysis of complex systems using neural networks

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  14. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  15. Systemic sclerosis, birth order and parity.

    Science.gov (United States)

    Russo, Paul A J; Lester, Susan; Roberts-Thomson, Peter J

    2014-06-01

    A recent study identified increasing birth order to be a risk factor for the development of systemic sclerosis (SSc). This finding supports the theory that transplacental microchimerism may be a key pathological event in the initiation of SSc. We investigated the relationship between birth order and parity and the age of onset of SSc in South Australia. A retrospective analysis of patient data in the South Australian Scleroderma Register was performed. Data were obtained from a mailed questionnaire. Control data was collected prospectively using a similar questionnaire. The relationship between birth order, family size or parity and risk of subsequent development of SSc was analyzed by mixed effects logistic regression analysis. Three hundred and eighty-seven index probands were identified and compared with 457 controls. Controls were well matched for gender, but not for age. No statistically significant relationship was identified between SSc and birth order, parity in females, family size, age at first pregnancy in females or gender of first child in parous females. Our data suggests that parity, age at first pregnancy and the gender of the first child are not relevant factors in our understanding of the epidemiology and pathogenesis of SSc. Birth order and family size in both genders also appears irrelevant. These results argue against microchimerism as being relevant in the pathogenesis of SSc and add further support to the theory that stochastic events may be important in the etiopathogenesis of SSc. © 2013 Asia Pacific League of Associations for Rheumatology and Wiley Publishing Asia Pty Ltd.

  16. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  18. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  19. Short-term synaptic plasticity and heterogeneity in neural systems

    Science.gov (United States)

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  20. Representation of neural networks as Lotka-Volterra systems

    International Nuclear Information System (INIS)

    Moreau, Yves; Vandewalle, Joos; Louies, Stephane; Brenig, Leon

    1999-01-01

    We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models--also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoied. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network

  1. The Consequences of Chorioamnionitis: Preterm Birth and Effects on Development

    Directory of Open Access Journals (Sweden)

    Robert Galinsky

    2013-01-01

    Full Text Available Preterm birth is a major cause of perinatal mortality and long-term morbidity. Chorioamnionitis is a common cause of preterm birth. Clinical chorioamnionitis, characterised by maternal fever, leukocytosis, tachycardia, uterine tenderness, and preterm rupture of membranes, is less common than subclinical/histologic chorioamnionitis, which is asymptomatic and defined by inflammation of the chorion, amnion, and placenta. Chorioamnionitis is often associated with a fetal inflammatory response. The fetal inflammatory response syndrome (FIRS is defined by increased systemic inflammatory cytokine concentrations, funisitis, and fetal vasculitis. Clinical and epidemiological studies have demonstrated that FIRS leads to poor cardiorespiratory, neurological, and renal outcomes. These observations are further supported by experimental studies that have improved our understanding of the mechanisms responsible for these outcomes. This paper outlines clinical and experimental studies that have improved our current understanding of the mechanisms responsible for chorioamnionitis-induced preterm birth and explores the cellular and physiological mechanisms underlying poor cardiorespiratory, neural, retinal, and renal outcomes observed in preterm infants exposed to chorioamnionitis.

  2. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Teratology: from science to birth defects prevention.

    Science.gov (United States)

    Rasmussen, Sonja A; Erickson, J David; Reef, Susan E; Ross, Danielle S

    2009-01-01

    One of the goals of birth defects research is to better understand risk or preventive factors for birth defects so that strategies for prevention can be developed. In this article, we have selected four areas of birth defects research that have led to the development of prevention strategies. These areas include rubella virus as a cause of congenital rubella syndrome, folic acid as a preventive factor for neural tube defects, cytomegalovirus infection as a cause of birth defects and developmental disabilities, and alcohol as a cause of fetal alcohol spectrum disorders. For each of these areas, we review key clinical and research findings that led to the identification of the risk or preventive factor, milestones in the development of prevention strategies, and the progress made thus far toward prevention.

  4. Review: the role of neural crest cells in the endocrine system.

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

    The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.

  5. Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

    Science.gov (United States)

    2010-09-24

    system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

  6. Frequency-difference-dependent stochastic resonance in neural systems

    Science.gov (United States)

    Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

    2017-08-01

    Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

  7. Use of neural networks in the analysis of complex systems

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms) to some of the problems of complex engineering systems has the potential to enhance the safety reliability and operability of these systems. The work described here deals with complex systems or parts of such systems that can be isolated from the total system. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network. The neural networks are usually simulated on modern high-speed computers that carry out the calculations serially. However, it is possible to implement neural networks using specially designed microchips where the network calculations are truly carried out in parallel, thereby providing virtually instantaneous outputs for each set of inputs. Specific applications described include: Diagnostics: State of the Plant; Hybrid System for Transient Identification; Detection of Change of Mode in Complex Systems; Sensor Validation; Plant-Wide Monitoring; Monitoring of Performance and Efficiency; and Analysis of Vibrations. Although the specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  8. Novel perspectives of neural stem cell differentiation: from neurotransmitters to therapeutics.

    Science.gov (United States)

    Trujillo, Cleber A; Schwindt, Telma T; Martins, Antonio H; Alves, Janaína M; Mello, Luiz Eugênio; Ulrich, Henning

    2009-01-01

    In the past years, many reports have described the existence of neural progenitor and stem cells in the adult central nervous system capable of generating new neurons, astrocytes, and oligodendrocytes. This discovery has overturned the central assumption in the neuroscience field, of no new neurons being originated in the brain after birth and provided the fundaments to understand the molecular basis of neural differentiation and to develop new therapies for neural tissue repair. Although the mechanisms underlying cell fate during neural development are not yet understood, the importance of intrinsic and extrinsic factors and of an appropriate microenvironment is well known. In this context, emerging evidence strongly suggests that glial cells play a key role in controlling multiple steps of neurogenesis. Those cells, of particular radial glia, are important for migration, cell specification, and integration of neurons into a functional neural network. This review aims to present an update in the neurogenesis area and highlight the modulation of neural stem cell differentiation by neurotransmitters, growth factors, and their receptors, with possible applications for cell therapy strategies of neurological disorders.

  9. Microfluidic systems for stem cell-based neural tissue engineering.

    Science.gov (United States)

    Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R

    2016-07-05

    Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.

  10. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

  11. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  12. Epidemiologic study of neural tube defects in Los Angeles County. II. Etiologic factors in an area with low prevalence at birth

    Energy Technology Data Exchange (ETDEWEB)

    Sever, L.E.

    1982-01-01

    Epidemiologic characteristics of neural tube defect (NTD) births occurring in Los Angeles County, California, residents during the period 1966-1972 are presented. The prevalence at birth was 0.52/1000 births for anencephalus, 0.51/1000 for spina bifida, and 0.08/1000 for encephalocele, rates considered to be low for a predominantly white population. We hypothesized that environmental (nongenetic) factors are of less etiologic importance in a low-prevalence population than in areas or time periods with high prevalence. We tested that hypothesis by examining epidemiologic characteristics of NTDs in Los Angeles County and comparing them with high-prevalence populations. The data did not support a major etiologic role for environmental factors: (1) no significant differences between rates by month of birth or conception; (2) no significant association with maternal age or parity for anencephalus; for spina bifida a significant maternal age effect (P < 0.01) and for encephalocele a parity effect (P < 0.02); and (3) no significant relationship with father's occupational class for either anencephalus or encephalocele but a marginally significant (P < 0.05) inverse association for spina bifida when a statistic based on ordinal relationships was used. Findings supporting the importance of genetic factors in etiology included: (1) a high percentage of males; (2) a higher twin concordance rate than in high-prevalence populations; and (3) an anencephalus rate among blacks comparable with rates for blacks in other United States populations. Our findings in conjunction with those from other areas and times of low prevalence suggest environmental factors play a relatively insignificant role in the etiology of NTDs in such populations.

  13. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  14. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  15. Decoupling control of vehicle chassis system based on neural network inverse system

    Science.gov (United States)

    Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

    2018-06-01

    Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

  16. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  17. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  18. Neural computing thermal comfort index for HVAC systems

    International Nuclear Information System (INIS)

    Atthajariyakul, S.; Leephakpreeda, T.

    2005-01-01

    The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model

  19. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  20. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

  1. Agricultural Compounds in Water and Birth Defects.

    Science.gov (United States)

    Brender, Jean D; Weyer, Peter J

    2016-06-01

    Agricultural compounds have been detected in drinking water, some of which are teratogens in animal models. The most commonly detected agricultural compounds in drinking water include nitrate, atrazine, and desethylatrazine. Arsenic can also be an agricultural contaminant, although arsenic often originates from geologic sources. Nitrate has been the most studied agricultural compound in relation to prenatal exposure and birth defects. In several case-control studies published since 2000, women giving birth to babies with neural tube defects, oral clefts, and limb deficiencies were more likely than control mothers to be exposed to higher concentrations of drinking water nitrate during pregnancy. Higher concentrations of atrazine in drinking water have been associated with abdominal defects, gastroschisis, and other defects. Elevated arsenic in drinking water has also been associated with birth defects. Since these compounds often occur as mixtures, it is suggested that future research focus on the impact of mixtures, such as nitrate and atrazine, on birth defects.

  2. Global Burden of Neural Tube Defects, Risk Factors, and Prevention

    Directory of Open Access Journals (Sweden)

    Joseph E

    2014-11-01

    Full Text Available Neural tube defects (NTDs, serious birth defects of the brain and spine usually resulting in death or paralysis, affect an estimated 300,000 births each year worldwide. Although the majority of NTDs are preventable with adequate folic acid consumption during the preconception period and throughout the first few weeks of gestation, many populations, in particular those in low and middle resource settings, do not have access to fortified foods or vitamin supplements containing folic acid. Further, accurate birth defects surveillance data, which could help inform mandatory fortification and other NTD prevention initiatives, are lacking in many of these settings. The burden of birth defects in South East Asia is among the highest in the world. Expanding global neural tube defects prevention initiatives can support the achievement of the United Nations Millennium Development Goal 4 to reduce child mortality, a goal which many countries in South East Asia are currently not poised to reach, and the 63rd World Health Assembly Resolution on birth defects. More work is needed to develop and implement mandatory folic acid fortification policies, as well as supplementation programs in countries where the reach of fortification is limited.

  3. A New Controller to Enhance PV System Performance Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Roshdy A AbdelRassoul

    2017-06-01

    Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.

  4. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  5. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  6. Neural neworks in a management information systems

    OpenAIRE

    Jana Weinlichová; Michael Štencl

    2009-01-01

    For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of ma...

  7. Birth weight and order as risk factors for childhood central nervous system tumors.

    Science.gov (United States)

    MacLean, Jane; Partap, Sonia; Reynolds, Peggy; Von Behren, Julie; Fisher, Paul Graham

    2010-09-01

    To determine whether birth characteristics related to maternal-fetal health in utero are associated with the development of childhood central nervous system tumors. We identified, from the California Cancer Registry, 3733 children under age 15 diagnosed with childhood central nervous system tumors between 1988 and 2006 and linked these cases to their California birth certificates. Four controls per case, matched on birth date and sex, were randomly selected from the same birth files. We evaluated associations of multiple childhood CNS tumor subtypes with birth weight and birth order. Low birth weight was associated with a reduced risk of low-grade gliomas (OR=0.67; 95% CI, 0.46 to 0.97) and high birth weight was associated with increased risk of high-grade gliomas (OR=1.57; 95% CI, 1.16 to 2.12). High birth order (fourth or higher) was associated with decreased risk of low-grade gliomas (OR=0.75; 95% CI, 0.56 to 0.99) and increased risk of high-grade gliomas (OR=1.32; 95% CI, 1.01 to 1.72 for second order). Factors that drive growth in utero may increase the risk of low-grade gliomas. There may be a similar relationship in high-grade gliomas, although other factors, such as early infection, may modify this association. Additional investigation is warranted to validate and further define these findings. Copyright (c) 2010 Mosby, Inc. All rights reserved.

  8. Vein matching using artificial neural network in vein authentication systems

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  9. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  10. Prevalence at birth of congenital malformations in communities near the Hanford site

    International Nuclear Information System (INIS)

    Sever, L.E.; Hessol, N.A.; Gilbert, E.S.; McIntyre, J.M.

    1988-01-01

    The authors examined the prevalence of congenital malformations among births in Benton and Franklin counties, in southeastern Washington State, from 1968 through 1980. The Hanford Site is in this area and serves as a major employer. In addition, various agriculturally and chemically related activities are in the area. Hospital and vital records were used to identify 454 malformation cases among 23,319 births; this yielded a malformation rate of 19.6 per 1000 births, a rate similar to those reported in other studies. The rates of specific malformations ascertained during the first year of life were compared with combined rates from the states of Washington, Oregon, and Idaho from the Birth Defects Monitoring Program. Among defects that would be expected to be comparably ascertained, a statistically significant elevated rate of neural tube defects was observed (1.72 per 1000 births vs. 0.99 per 1000). Rates of cleft lip were significantly lower in Benton and Franklin counties than in the Birth Defects Monitoring Program (0.59 per 1,000 vs. 1.17 per 1000). For congenital heart defects, pyloric stenosis, and Down syndrome, which are often not diagnosed in the newborn period, Birth Defects Monitoring Program data did not offer appropriate comparisons. The rates of these defects did not appear to be elevated in relation to rates found in other relevant populations. When rates of neural tube defects were compared with those in populations other than the Birth Defects Monitoring Program, the Benton and Franklin county rates were still considered to be elevated. The increased bicounty rate cannot be explained by employment of the parents at Hanford or by the impact of plant emissions on the local population

  11. Radial basis function neural network for power system load-flow

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  12. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  13. High speed digital interfacing for a neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Bahr Andreas

    2016-09-01

    Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

  14. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

    Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

    1991-01-01

    The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

  15. The association of birth model with resilience variables and birth experience: Home versus hospital birth.

    Science.gov (United States)

    Handelzalts, Jonathan E; Zacks, Arni; Levy, Sigal

    2016-05-01

    to study home, natural hospital, and medical hospital births, and the association of these birth models to resilience and birth experience. cross-section retrospective design. participants were recruited via an online survey system. Invitations to participate were posted in five different Internet forums for women on maternity leave, from September 2014 to August 2015. the sample comprised 381 post partum healthy women above the age of 20, during their maternity leave. Of the participants: 22% gave birth at home, 32% gave birth naturally in a hospital, and 46% of the participants had a medical birth at the hospital. life Orientation Test Revised (LOT-R), General Self-Efficacy Scale, Sense of Mastery Scale, Childbirth Experience Questionnaire (CEQ). women having had natural births, whether at home or at the hospital, significantly differed from women having had medical births in all aspects of the birth experience, even when controlling for age and optimism. Birth types contributed to between 14% and 24% of the explained variance of the various birth experience aspects. home and natural hospital births were associated with a better childbirth experience. Optimism was identified as a resilience factor, associated both with preference as well as with childbirth experience. physically healthy and resilient women could be encouraged to explore the prospect of home or natural hospital births as a means to have a more positive birth experience. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Fault diagnosis system of electromagnetic valve using neural network filter

    International Nuclear Information System (INIS)

    Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

    2008-01-01

    This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

  17. Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

    International Nuclear Information System (INIS)

    Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

    2004-01-01

    In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

  18. Three neural network based sensor systems for environmental monitoring

    International Nuclear Information System (INIS)

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field

  19. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  20. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  1. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  2. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  3. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  4. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  5. Neural systems analysis of decision making during goal-directed navigation.

    Science.gov (United States)

    Penner, Marsha R; Mizumori, Sheri J Y

    2012-01-01

    The ability to make adaptive decisions during goal-directed navigation is a fundamental and highly evolved behavior that requires continual coordination of perceptions, learning and memory processes, and the planning of behaviors. Here, a neurobiological account for such coordination is provided by integrating current literatures on spatial context analysis and decision-making. This integration includes discussions of our current understanding of the role of the hippocampal system in experience-dependent navigation, how hippocampal information comes to impact midbrain and striatal decision making systems, and finally the role of the striatum in the implementation of behaviors based on recent decisions. These discussions extend across cellular to neural systems levels of analysis. Not only are key findings described, but also fundamental organizing principles within and across neural systems, as well as between neural systems functions and behavior, are emphasized. It is suggested that studying decision making during goal-directed navigation is a powerful model for studying interactive brain systems and their mediation of complex behaviors. Copyright © 2011. Published by Elsevier Ltd.

  6. Intelligent neural network and fuzzy logic control of industrial and power systems

    Science.gov (United States)

    Kuljaca, Ognjen

    The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

  7. Integrated evolutionary computation neural network quality controller for automated systems

    Energy Technology Data Exchange (ETDEWEB)

    Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

    1999-06-01

    With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

  8. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

  9. Neural networks for feedback feedforward nonlinear control systems.

    Science.gov (United States)

    Parisini, T; Zoppoli, R

    1994-01-01

    This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

  10. Reliability analysis of a consecutive r-out-of-n: F system based on neural networks

    International Nuclear Information System (INIS)

    Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

    2009-01-01

    In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.

  11. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

    Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  12. Developmental neurotoxicity and autism: A potential link between indoor neuroactive pollutants and the curious birth order risk factor.

    Science.gov (United States)

    Gray, Wesley A; Billock, Vincent A

    2017-11-01

    Epidemiological and demographic studies find an increased risk of autism among first-borns. Toxicological studies show that some semi-volatile substances found in infant products produce adverse effects in neural and endocrine systems of animals, including behavioral and developmental effects. Several factors elevate the exposure of human infants to these chemicals. The highest exposures found in infants are comparable to the exposures that induce neural toxicity in animals. A review of these literatures suggests a linking hypothesis that could bridge the epidemiological and toxicological lines of evidence: an infant's exposure to neuroactive compounds emitted by infant products is increased by product newness and abundance; exposure is likely maximized for first-born children in families that can afford new products. Exposure is reduced for subsequently-born children who reuse these now neuroactive-depleted products. The presence of neuroactive chemical emissions from infant products has implications for birth-order effects and for other curious risk factors in autism, including gender, socioeconomic status, and season-of-birth risk factors. Copyright © 2017 ISDN. Published by Elsevier Ltd. All rights reserved.

  13. [Evaluation of data on live birth certificates from the Information System on Live Births (SINASC) in Campinas, São Paulo, 2009].

    Science.gov (United States)

    Gabriel, Guilherme Paiva; Chiquetto, Letícia; Morcillo, André Moreno; Ferreira, Maria do Carmo; Bazan, Ivan Gilberto M; Daolio, Luísa Dias; Lemos, Jéssica J Rocha; Carniel, Emília de Faria

    2014-09-01

    To assess the completeness and reliability of the Information System on Live Births (Sinasc) data. A cross-sectional analysis of the reliability and completeness of Sinasc's data was performed using a sample of Live Birth Certificate (LBC) from 2009, related to births from Campinas, Southeast Brazil. For data analysis, hospitals were grouped according to category of service (Unified National Health System, private or both), 600 LBCs were randomly selected and the data were collected in LBC-copies through mothers and newborns' hospital records and by telephone interviews. The completeness of LBCs was evaluated, calculating the percentage of blank fields, and the LBCs agreement comparing the originals with the copies was evaluated by Kappa and intraclass correlation coefficients. The percentage of completeness of LBCs ranged from 99.8%-100%. For the most items, the agreement was excellent. However, the agreement was acceptable for marital status, maternal education and newborn infants' race/color, low for prenatal visits and presence of birth defects, and very low for the number of deceased children. The results showed that the municipality Sinasc is reliable for most of the studied variables. Investments in training of the professionals are suggested in an attempt to improve system capacity to support planning and implementation of health activities for the benefit of maternal and child population. Copyright © 2014 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

  14. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  15. the z-transform applied to a birth-death process having varying birth

    African Journals Online (AJOL)

    DEPT OF AGRICULTURAL ENGINEERING

    model can be used to study practical queuing and birth-death systems where the arrival, birth, ser- vice and death rates ... for systems operating in fading environments (Hueda and ... mobile computing (Lee et al., 1999) and the transmission ...

  16. Container-code recognition system based on computer vision and deep neural networks

    Science.gov (United States)

    Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

    2018-04-01

    Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

  17. Temporal neural networks and transient analysis of complex engineering systems

    Science.gov (United States)

    Uluyol, Onder

    A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

  18. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  19. Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran

    Directory of Open Access Journals (Sweden)

    Mahmoud Akbarian

    2015-07-01

    Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP neural network with scaled conjugate gradient (trainscg back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth among pregnant women with lupus by using identified effective variables.

  20. Neural mechanisms of selective attention in the somatosensory system.

    Science.gov (United States)

    Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

    2016-09-01

    Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

  1. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  3. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

  4. review of current evidence for folate in the prevention of neural tube ...

    African Journals Online (AJOL)

    The incidence of neural tube defects (NTD) among black South. Africans living in urban areas is low compared with reports of _". I\\TTD incidence in rural areasY A NTD incidence of 0.95 per -;:-. 1000 live births was reported in Cape Town,l while an incidence of 0.99 per 1 000 live births was reported in a study performed at ...

  5. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  6. Pulse Oximetry Screening Adapted to a System with Home Births: The Dutch Experience

    Directory of Open Access Journals (Sweden)

    Ilona C. Narayen

    2018-03-01

    Full Text Available Neonatal screening for critical congenital heart defects is proven to be safe, accurate, and cost-effective. The screening has been implemented in many countries across all continents in the world. However, screening for critical congenital heart defects after home births had not been studied widely yet. The Netherlands is known for its unique perinatal care system with a high rate of home births (18% and early discharge after an uncomplicated delivery in hospital. We report a feasibility, accuracy, and acceptability study performed in the Dutch perinatal care system. Screening newborns for critical congenital heart defects using pulse oximetry is feasible after home births and early discharge, and acceptable to mothers. The accuracy of the test is comparable to other early-screening settings, with a moderate sensitivity and high specificity.

  7. Efficient decoding with steady-state Kalman filter in neural interface systems.

    Science.gov (United States)

    Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

    2011-02-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

  8. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  9. Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

    Science.gov (United States)

    Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

    2016-11-14

    In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

  10. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  11. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  12. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    International Nuclear Information System (INIS)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

    2006-01-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  13. Compact holographic optical neural network system for real-time pattern recognition

    Science.gov (United States)

    Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

    1996-08-01

    One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

  14. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  15. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  16. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  17. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  18. PLANNED HOME BIRTH: A REVIEW

    OpenAIRE

    Tamara Serdinšek; Iztok Takač

    2016-01-01

    Background: Home birth is as old as humanity, but still most middle- and high-income countries consider hospitals as the safest birth settings, as complications regarding birth are highly unpredictable. Despite this there are a few countries in which home birth in integrated into official healthcare system (the Netherlands, United Kingdom, Canada etc.). Home births can be divided into unplanned and planned, and the latter can be further categorized by the presence of the birth attendants. Thi...

  19. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    International Nuclear Information System (INIS)

    Tsai, Tai Ming; Wang, Wei Hui

    2009-01-01

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  20. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

    2009-01-15

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  1. Neural network-based expert system for severe accident management

    International Nuclear Information System (INIS)

    Klopp, G.T.; Silverman, E.B.

    1992-01-01

    This paper presents the results of the second phase of a three-phase Severe Accident Management expert system program underway at Commonwealth Edison Company (CECo). Phase I successfully demonstrated the feasibility of Artificial Neural Networks to support several of the objectives of severe accident management. Simulated accident scenarios were generated by the Modular Accident Analysis Program (MAAP) code currently in use by CECo as part of their Individual Plant Evaluations (IPE)/Accident Management Program. The primary objectives of the second phase were to develop and demonstrate four capabilities of neural networks with respect to nuclear power plant severe accident monitoring and prediction. The results of this work would form the foundation of a demonstration system which included expert system performance features. These capabilities included the ability to: (1) Predict the time available prior to support plate (and reactor vessel) failure; (2) Calculate the time remaining until recovery actions were too late to prevent core damage; (3) Predict future parameter values of each of the MAAP parameter variables; and (4) Detect simulated sensor failure and provide best-value estimates for further processing in the presence of a sensor failure. A variety of accident scenarios for the Zion and Dresden plants were used to train and test the neural network expert system. These included large and small break LOCAs as well as a range of transient events. 3 refs., 1 fig., 1 tab

  2. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  3. Separate Influences of Birth Order and Gravidity/Parity on the Development of Systemic Sclerosis

    Science.gov (United States)

    COCKRILL, TONYA; del JUNCO, DEBORAH J.; ARNETT, FRANK C.; ASSASSI, SHERVIN; TAN, FILEMON K.; McNEARNEY, TERRY; FISCHBACH, MICHAEL; PERRY, MARILYN; MAYES, MAUREEN D.

    2010-01-01

    Objective Birth order has been valuable in revealing the role of environmental influences on the risk of developing certain diseases such as allergy and atopy. In addition, pregnancy has profound effects on the immune system such as short-term effects that permit fetal survival as well as longer-term effects that could influence late-onset diseases. In order to better evaluate these influences, we studied the association of birth order and gravidity/parity as risk factors for systemic sclerosis (SSc; scleroderma). Methods Data regarding SSc cases and their unaffected sibling controls were obtained from the Scleroderma Family Registry and DNA Repository. The case-sibling design was used to minimize confounding due to differences in age, race, ethnicity, or calendar time. The gravidity/parity analysis was based on sibships with at least one SSc-affected and one unaffected sister. Results Birth order was examined in 974 sibships, comparing SSc cases (n = 987) with their unaffected siblings (n = 3,088). The risk of scleroderma increased with increasing birth order (odds ratio [OR] 1.25, 95% confidence interval [95% CI] 1.06–1.50 for birth order 2–5; OR 2.22, 95% CI 1.57–3.15 for birth order 6–9; and OR 3.53, 95% CI 1.68–7.45 for birth order 10–15). Gravidity/parity was analyzed in 168 sibships (256 unaffected sisters, 172 SSc cases). We found an association between a history of one or more pregnancies and SSc (OR 2.8). Conclusion Birth order and pregnancy were independently associated with a higher risk of developing SSc. These findings suggest that immune development in early childhood and/or pregnancy-associated events, including but not limited to microchimerism, plays a role in SSc susceptibility. PMID:20391489

  4. Separate influences of birth order and gravidity/parity on the development of systemic sclerosis.

    Science.gov (United States)

    Cockrill, Tonya; del Junco, Deborah J; Arnett, Frank C; Assassi, Shervin; Tan, Filemon K; McNearney, Terry; Fischbach, Michael; Perry, Marilyn; Mayes, Maureen D

    2010-03-01

    Birth order has been valuable in revealing the role of environmental influences on the risk of developing certain diseases such as allergy and atopy. In addition, pregnancy has profound effects on the immune system such as short-term effects that permit fetal survival as well as longer-term effects that could influence late-onset diseases. In order to better evaluate these influences, we studied the association of birth order and gravidity/parity as risk factors for systemic sclerosis (SSc; scleroderma). Data regarding SSc cases and their unaffected sibling controls were obtained from the Scleroderma Family Registry and DNA Repository. The case-sibling design was used to minimize confounding due to differences in age, race, ethnicity, or calendar time. The gravidity/parity analysis was based on sibships with at least one SSc-affected and one unaffected sister. Birth order was examined in 974 sibships, comparing SSc cases (n = 987) with their unaffected siblings (n = 3,088). The risk of scleroderma increased with increasing birth order (odds ratio [OR] 1.25, 95% confidence interval [95% CI] 1.06-1.50 for birth order 2-5; OR 2.22, 95% CI 1.57-3.15 for birth order 6-9; and OR 3.53, 95% CI 1.68-7.45 for birth order 10-15). Gravidity/parity was analyzed in 168 sibships (256 unaffected sisters, 172 SSc cases). We found an association between a history of one or more pregnancies and SSc (OR 2.8). Birth order and pregnancy were independently associated with a higher risk of developing SSc. These findings suggest that immune development in early childhood and/or pregnancy-associated events, including but not limited to microchimerism, plays a role in SSc susceptibility.

  5. Real-time camera-based face detection using a modified LAMSTAR neural network system

    Science.gov (United States)

    Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

    2003-03-01

    This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

  6. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

    Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

  7. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  8. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems.

    Science.gov (United States)

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K Y Michael; Zhou, Changsong

    2016-01-08

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

  9. A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

    OpenAIRE

    Xiangyu He; Shanghong He

    2014-01-01

    Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

  10. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

    Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  11. Neural system prediction and identification challenge.

    Science.gov (United States)

    Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

    2013-01-01

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  12. Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

    Directory of Open Access Journals (Sweden)

    Xiao-Li Li

    2014-01-01

    Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

  13. Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Gu, Z.; Rizy, D.T.

    1996-02-01

    A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

  14. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    International Nuclear Information System (INIS)

    Wei Duqu; Luo Xiaoshu; Zou Yanli

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

  15. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    Directory of Open Access Journals (Sweden)

    David Breuer

    2014-03-01

    Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

  16. Cancer and birth defects surveillance system for communities around the Savannah River Site: Phase 2 -- Birth defects. Technical progress report, year 01

    International Nuclear Information System (INIS)

    Dunbar, J.B.

    1995-10-01

    The Savannah River Region Health Information System Birth Defects Registry (SRRHIS-BDR) began on September 30, 1994. As with the SRRHIS Cancer Registry, surveillance of the 12 Georgia counties was subcontracted to Emory University School of Public Health. Collaborative efforts between the Medical University of South Carolina (MUSC) and Emory University staffs have been characterized by warm relationships and commitment to developing a state of the art registry. As a result of early planning efforts, the authors were able to actually activate the data collection. As of the end of September 1995, partial data from the 1994 birth cohort and up-to-date data for the 1995 birth cohort had been collected on the South Carolina side. The Georgia Staff started later and have not yet caught up to the 1994 level. South Carolina was able to start earlier because they were fortunate to quickly recruit an abstractor. Also, by the end of the first year, an innovative automated data entry system for laptop computers was developed by the computer staff to facilitate and improve data collection

  17. Cancer and birth defects surveillance system for communities around the Savannah River Site: Phase 2 -- Birth defects. Technical progress report, year 01

    Energy Technology Data Exchange (ETDEWEB)

    Dunbar, J.B.

    1995-10-01

    The Savannah River Region Health Information System Birth Defects Registry (SRRHIS-BDR) began on September 30, 1994. As with the SRRHIS Cancer Registry, surveillance of the 12 Georgia counties was subcontracted to Emory University School of Public Health. Collaborative efforts between the Medical University of South Carolina (MUSC) and Emory University staffs have been characterized by warm relationships and commitment to developing a state of the art registry. As a result of early planning efforts, the authors were able to actually activate the data collection. As of the end of September 1995, partial data from the 1994 birth cohort and up-to-date data for the 1995 birth cohort had been collected on the South Carolina side. The Georgia Staff started later and have not yet caught up to the 1994 level. South Carolina was able to start earlier because they were fortunate to quickly recruit an abstractor. Also, by the end of the first year, an innovative automated data entry system for laptop computers was developed by the computer staff to facilitate and improve data collection.

  18. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    International Nuclear Information System (INIS)

    Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  19. Is it good to be too light? Birth weight thresholds in hospital reimbursement systems.

    Science.gov (United States)

    Reif, Simon; Wichert, Sebastian; Wuppermann, Amelie

    2018-02-02

    Birth weight manipulation has been documented in per-case hospital reimbursement systems, in which hospitals receive more money for otherwise equal newborns with birth weight just below compared to just above specific birth weight thresholds. As hospitals receive more money for cases with weight below the thresholds, having a (reported) weight below a threshold could benefit the newborn. Also, these reimbursement thresholds overlap with diagnostic thresholds that have been shown to affect the quantity and quality of care that newborns receive. Based on the universe of hospital births in Germany from the years 2005-2011, we investigate whether weight below reimbursement relevant thresholds triggers different quantity and quality of care. We find that this is not the case, suggesting that hospitals' financial incentives with respect to birth weight do not directly impact the care that newborns receive. Copyright © 2018 Elsevier B.V. All rights reserved.

  20. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

    Science.gov (United States)

    Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

  1. Adaptive neural networks control for camera stabilization with active suspension system

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-08-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  2. Anomalous dependence of population growth on the birth rate in the plant-herbivore system

    International Nuclear Information System (INIS)

    Cui, Xue M.; Han, Seung K.; Chung, Jean S.

    2010-01-01

    We performed a simulation of the two-species plant-herbivore system by using the agent-based NetLogo program and constructed a dynamic model of populations consistent with the simulation results. The dynamic model is a three-dimensional system including the mean energy of the herbivore in addition to two variables denoting the populations of plants and herbivores. A steady-state analysis of the dynamic model shows that the dependence of the herbivore population on the birth and the death rates observed from the agent model is consistent with the prediction of the dynamic model. Especially, the anomalous dependence of the herbivore population on the birth rate, where the population decreases with the birth rate for small death rate, is consistently explained by a phase plane analysis of the dynamic model.

  3. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  4. Xenopus reduced folate carrier regulates neural crest development epigenetically.

    Directory of Open Access Journals (Sweden)

    Jiejing Li

    Full Text Available Folic acid deficiency during pregnancy causes birth neurocristopathic malformations resulting from aberrant development of neural crest cells. The Reduced folate carrier (RFC is a membrane-bound receptor for facilitating transfer of reduced folate into the cells. RFC knockout mice are embryonic lethal and develop multiple malformations, including neurocristopathies. Here we show that XRFC is specifically expressed in neural crest tissues in Xenopus embryos and knockdown of XRFC by specific morpholino results in severe neurocristopathies. Inhibition of RFC blocked the expression of a series of neural crest marker genes while overexpression of RFC or injection of 5-methyltetrahydrofolate expanded the neural crest territories. In animal cap assays, knockdown of RFC dramatically reduced the mono- and trimethyl-Histone3-K4 levels and co-injection of the lysine methyltransferase hMLL1 largely rescued the XRFC morpholino phenotype. Our data revealed that the RFC mediated folate metabolic pathway likely potentiates neural crest gene expression through epigenetic modifications.

  5. Neural systems for preparatory control of imitation.

    Science.gov (United States)

    Cross, Katy A; Iacoboni, Marco

    2014-01-01

    Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.

  6. NMDA Receptor Signaling Is Important for Neural Tube Formation and for Preventing Antiepileptic Drug-Induced Neural Tube Defects.

    Science.gov (United States)

    Sequerra, Eduardo B; Goyal, Raman; Castro, Patricio A; Levin, Jacqueline B; Borodinsky, Laura N

    2018-05-16

    Failure of neural tube closure leads to neural tube defects (NTDs), which can have serious neurological consequences or be lethal. Use of antiepileptic drugs (AEDs) during pregnancy increases the incidence of NTDs in offspring by unknown mechanisms. Here we show that during Xenopus laevis neural tube formation, neural plate cells exhibit spontaneous calcium dynamics that are partially mediated by glutamate signaling. We demonstrate that NMDA receptors are important for the formation of the neural tube and that the loss of their function induces an increase in neural plate cell proliferation and impairs neural cell migration, which result in NTDs. We present evidence that the AED valproic acid perturbs glutamate signaling, leading to NTDs that are rescued with varied efficacy by preventing DNA synthesis, activating NMDA receptors, or recruiting the NMDA receptor target ERK1/2. These findings may prompt mechanistic identification of AEDs that do not interfere with neural tube formation. SIGNIFICANCE STATEMENT Neural tube defects are one of the most common birth defects. Clinical investigations have determined that the use of antiepileptic drugs during pregnancy increases the incidence of these defects in the offspring by unknown mechanisms. This study discovers that glutamate signaling regulates neural plate cell proliferation and oriented migration and is necessary for neural tube formation. We demonstrate that the widely used antiepileptic drug valproic acid interferes with glutamate signaling and consequently induces neural tube defects, challenging the current hypotheses arguing that they are side effects of this antiepileptic drug that cause the increased incidence of these defects. Understanding the mechanisms of neurotransmitter signaling during neural tube formation may contribute to the identification and development of antiepileptic drugs that are safer during pregnancy. Copyright © 2018 the authors 0270-6474/18/384762-12$15.00/0.

  7. On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

    Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

  8. A neural network approach to the study of dynamics and structure of molecular systems

    International Nuclear Information System (INIS)

    Getino, C.; Sumpter, B.G.; Noid, D.W.

    1994-01-01

    Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

  9. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  10. Color Image Encryption Algorithm Based on TD-ERCS System and Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Kun Zhang

    2015-01-01

    Full Text Available In order to solve the security problem of transmission image across public networks, a new image encryption algorithm based on TD-ERCS system and wavelet neural network is proposed in this paper. According to the permutation process and the binary XOR operation from the chaotic series by producing TD-ERCS system and wavelet neural network, it can achieve image encryption. This encryption algorithm is a reversible algorithm, and it can achieve original image in the rule inverse process of encryption algorithm. Finally, through computer simulation, the experiment results show that the new chaotic encryption algorithm based on TD-ERCS system and wavelet neural network is valid and has higher security.

  11. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

  12. c-Myc Enhances Sonic Hedgehog-Induced Medulloblastoma Formation from Nestin-Expressing Neural Progenitors in Mice

    Directory of Open Access Journals (Sweden)

    Ganesh Rao

    2003-05-01

    Full Text Available Medulloblastomas are malignant brain tumors that arise in the cerebella of children. The presumed cellsof-origin are undifferentiated precursors of granule neurons that occupy the external granule layer (EGL of the developing cerebellum. The overexpression of proteins that normally stimulate proliferation of neural progenitor cells may initiate medulloblastoma formation. Two known mitogens for neural progenitors are the c-Myc oncoprotein and Sonic hedgehog (Shh, a crucial determinant of embryonic pattern formation in the central nervous system. We modeled the ability of c-Myc and Shh to induce medulloblastoma in mice using the RCAS/tv-a system, which allows postnatal gene transfer and expression in a cell type-specific manner. We targeted the expression of Shh and c-Myc to nestin-expressing neural progenitor cells by injecting replication-competent ALV splice acceptor (RCAS vectors into the cerebella of newborn mice. Following injection with RCAS-Shh alone, 3/32 (9% mice developed medulloblastomas and 5/32 showed multifocal hyperproliferation of the EGL, possibly a precursor stage of medulloblastoma. Following injection with RCAS-Shh plus RCAS-Myc, 9/39 (23% mice developed medulloblastomas. We conclude that nestin-expressing neural progenitors, present in the cerebellum at birth, can act as the cells-of-origin for medulloblastoma, and that c-Myc cooperates with Shh to enhance tumorigenicity.

  13. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  14. How age of bilingual exposure can change the neural systems for language in the developing brain: a functional near infrared spectroscopy investigation of syntactic processing in monolingual and bilingual children.

    Science.gov (United States)

    Jasinska, K K; Petitto, L A

    2013-10-01

    Is the developing bilingual brain fundamentally similar to the monolingual brain (e.g., neural resources supporting language and cognition)? Or, does early-life bilingual language experience change the brain? If so, how does age of first bilingual exposure impact neural activation for language? We compared how typically-developing bilingual and monolingual children (ages 7-10) and adults recruit brain areas during sentence processing using functional Near Infrared Spectroscopy (fNIRS) brain imaging. Bilingual participants included early-exposed (bilingual exposure from birth) and later-exposed individuals (bilingual exposure between ages 4-6). Both bilingual children and adults showed greater neural activation in left-hemisphere classic language areas, and additionally, right-hemisphere homologues (Right Superior Temporal Gyrus, Right Inferior Frontal Gyrus). However, important differences were observed between early-exposed and later-exposed bilinguals in their earliest-exposed language. Early bilingual exposure imparts fundamental changes to classic language areas instead of alterations to brain regions governing higher cognitive executive functions. However, age of first bilingual exposure does matter. Later-exposed bilinguals showed greater recruitment of the prefrontal cortex relative to early-exposed bilinguals and monolinguals. The findings provide fascinating insight into the neural resources that facilitate bilingual language use and are discussed in terms of how early-life language experiences can modify the neural systems underlying human language processing. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

  15. Identification of complex systems by artificial neural networks. Applications to mechanical frictions

    International Nuclear Information System (INIS)

    Dominguez, Manuel

    1998-01-01

    In the frame of complex systems modelization, we describe in this report the contribution of neural networks to mechanical friction modelization. This thesis is divided in three parts, each one corresponding to every stage of the realized work. The first part takes stock of the properties of neural networks by replacing them in the statistic frame of learning theory (particularly: non-linear and non-parametric regression models) and by showing the existing links with other more 'classic' techniques from automatics. We show then how identification models can be integrated in the neural networks description as a larger nonlinear model class. A methodology of neural networks use have been developed. We focused on validation techniques using correlation functions for non-linear systems, and on the use of regularization methods. The second part deals with the problematic of friction in mechanical systems. Particularly, we present the main current identified physical phenomena, which are integrated in advanced friction modelization. Characterization of these phenomena allows us to state a priori knowledge to be used in the identification stage. We expose some of the most well-known friction models: Dahl's model, Reset Integrator and Canuda's dynamical model, which are then used in simulation studies. The last part links the former one by illustrating a real-world application: an electric jack from SFIM-Industries, used in the Very Large Telescope (VLT) control scheme. This part begins with physical system presentation. The results are compared with more 'classic' methods. We finish using neural networks compensation scheme in closed-loop control. (author) [fr

  16. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  17. The ctenophore genome and the evolutionary origins of neural systems

    NARCIS (Netherlands)

    Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

    2014-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

  18. System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

    Science.gov (United States)

    Delgado-Restituto, Manuel; Rodriguez-Perez, Alberto; Darie, Angela; Soto-Sanchez, Cristina; Fernandez-Jover, Eduardo; Rodriguez-Vazquez, Angel

    2017-04-01

    This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330 μW.

  19. Prevalência de defeitos de fechamento de tubo neural no Vale do Paraíba, São Paulo Prevalence of neural tube defects in Vale do Paraíba, São Paulo, Brazil

    Directory of Open Access Journals (Sweden)

    Luiz Fernando C. Nascimento

    2008-12-01

    Full Text Available OBJETIVO: Estimar a prevalência de defeitos de fechamento do tubo neural no Vale do Paraíba paulista e identificar possíveis fatores maternos e neonatais associados a tais defeitos. MÉTODOS: Realizou-se um estudo transversal com dados secundários obtidos na Secretaria Estadual da Saúde referentes aos nascimentos ocorridos em 2004 no Vale do Paraíba paulista, que compreende 35 municípios e conta com população de 2 milhões de habitantes. Anencefalia, encefalocele e espina bífida (mielocele e mielomeningocele foram considerados defeitos de fechamento do tubo neural. As variáveis maternas foram: idade, escolaridade, cor da pele, número de consultas no pré-natal, número de filhos vivos e relato de óbito fetal prévio. As variáveis relativas ao recém-nascido foram: peso, idade gestacional e escore de Apgar. Realizou-se comparação das médias por meio do teste t de Student e obtiveram-se os valores das razões de chance com intervalos de confiança de 95%. RESULTADOS: Foram analisados 33.653 nascidos vivos. Trinta e oito recém-nascidos com o defeito foram encontrados (1,13/1.000 nascidos vivos, sendo 23 casos de espina bífida. Houve associação com baixo peso ao nascimento, prematuridade e menores escores de Apgar de cinco minutos. CONCLUSÕES: A prevalência desta anomalia foi inferior à de outros estudos nacionais e sua presença esteve associada ao baixo peso, à prematuridade e à baixa vitalidade ao nascer.OBJECTIVE: To estimate the prevalence of neural tube defects in Vale do Paraíba, São Paulo, Brazil, and to identify possible maternal and neonatal variables associated with these defects. METHODS: This cross-sectional study used secondary records of the Health Department of São Paulo State related live births during 2004 in Vale do Paraíba, São Paulo, Brazil. This region has 35 cities and 2 million inhabitants. Anencephaly, encephalocele and spina bifida (myelocele and myelomeningocele were considered as neural tube

  20. Differences between otolith- and semicircular canal-activated neural circuitry in the vestibular system.

    Science.gov (United States)

    Uchino, Yoshio; Kushiro, Keisuke

    2011-12-01

    In the last two decades, we have focused on establishing a reliable technique for focal stimulation of vestibular receptors to evaluate neural connectivity. Here, we summarize the vestibular-related neuronal circuits for the vestibulo-ocular reflex, vestibulocollic reflex, and vestibulospinal reflex arcs. The focal stimulating technique also uncovered some hidden neural mechanisms. In the otolith system, we identified two hidden neural mechanisms that enhance otolith receptor sensitivity. The first is commissural inhibition, which boosts sensitivity by incorporating inputs from bilateral otolith receptors, the existence of which was in contradiction to the classical understanding of the otolith system but was observed in the utricular system. The second mechanism, cross-striolar inhibition, intensifies the sensitivity of inputs from both sides of receptive cells across the striola in a single otolith sensor. This was an entirely novel finding and is typically observed in the saccular system. We discuss the possible functional meaning of commissural and cross-striolar inhibition. Finally, our focal stimulating technique was applied to elucidate the different constructions of axonal projections from each vestibular receptor to the spinal cord. We also discuss the possible function of the unique neural connectivity observed in each vestibular receptor system. Copyright © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  1. A novel neural-wavelet approach for process diagnostics and complex system modeling

    Science.gov (United States)

    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

  2. Shades of grey; Assessing the contribution of the magno- and parvocellular systems to neural processing of the retinal input in the human visual system from the influence of neural population size and its discharge activity on the VEP.

    Science.gov (United States)

    Marcar, Valentine L; Baselgia, Silvana; Lüthi-Eisenegger, Barbara; Jäncke, Lutz

    2018-03-01

    Retinal input processing in the human visual system involves a phasic and tonic neural response. We investigated the role of the magno- and parvocellular systems by comparing the influence of the active neural population size and its discharge activity on the amplitude and latency of four VEP components. We recorded the scalp electric potential of 20 human volunteers viewing a series of dartboard images presented as a pattern reversing and pattern on-/offset stimulus. These patterns were designed to vary both neural population size coding the temporal- and spatial luminance contrast property and the discharge activity of the population involved in a systematic manner. When the VEP amplitude reflected the size of the neural population coding the temporal luminance contrast property of the image, the influence of luminance contrast followed the contrast response function of the parvocellular system. When the VEP amplitude reflected the size of the neural population responding to the spatial luminance contrast property the image, the influence of luminance contrast followed the contrast response function of the magnocellular system. The latencies of the VEP components examined exhibited the same behavior across our stimulus series. This investigation demonstrates the complex interplay of the magno- and parvocellular systems on the neural response as captured by the VEP. It also demonstrates a linear relationship between stimulus property, neural response, and the VEP and reveals the importance of feedback projections in modulating the ongoing neural response. In doing so, it corroborates the conclusions of our previous study.

  3. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Outcomes of independent midwifery attended births in birth centres and home births: a retrospective cohort study in Japan.

    Science.gov (United States)

    Kataoka, Yaeko; Eto, Hiromi; Iida, Mariko

    2013-08-01

    blood loss over 500mL (RR1.28; 95%CI 1.07 to 1.53) and over 1000mL (RR1.75; 95%CI 1.04 to 2.82) compared to women birthing at home. our results for birth outcomes with independent midwives at birth centres and home births in Japan indicated a high degree of safety and evidence-based practice. This study had some limitations because of its incomplete data and low response rate. However, this is one of the few studies that reported outcomes of Japanese independent midwives and the safety of their practice. A birth registry system would provide us with more accurate and complete information of all childbirths with which to evaluate the safety of independent Japanese midwives. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  6. Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

    Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

  7. Maternal residential proximity to chlorinated solvent emissions and birth defects in offspring: a case-control study.

    Science.gov (United States)

    Brender, Jean D; Shinde, Mayura U; Zhan, F Benjamin; Gong, Xi; Langlois, Peter H

    2014-11-19

    Some studies have noted an association between maternal occupational exposures to chlorinated solvents and birth defects in offspring, but data are lacking on the potential impact of industrial air emissions of these solvents on birth defects. With data from the Texas Birth Defects Registry for births occurring in 1996-2008, we examined the relation between maternal residential proximity to industrial air releases of chlorinated solvents and birth defects in offspring of 60,613 case-mothers and 244,927 control-mothers. Maternal residential exposures to solvent emissions were estimated with metrics that took into account residential distances to industrial sources and annual amounts of chemicals released. Logistic regression was used to generate odds ratios and 95% confidence intervals for the associations between residential proximity to emissions of 14 chlorinated solvents and selected birth defects, including neural tube, oral cleft, limb deficiency, and congenital heart defects. All risk estimates were adjusted for year of delivery and maternal age, education, race/ethnicity, and public health region of residence. Relative to exposure risk values of 0, neural tube defects were associated with maternal residential exposures (exposure risk values >0) to several types of chlorinated solvents, most notably carbon tetrachloride (adjusted odds ratio [aOR] 1.42, 95% confidence interval [CI] 1.09, 1.86); chloroform (aOR 1.40, 95% CI 1.04, 1.87); ethyl chloride (aOR 1.39, 95% CI 1.08, 1.79); 1,1,2-trichloroethane (aOR 1.56, 95% CI 1.11, 2.18); and 1,2,3-trichloropropane (aOR 1.49, 95% CI 1.08, 2.06). Significant associations were also noted between a few chlorinated solvents and oral cleft, limb deficiency, and congenital heart defects. We observed stronger associations between some emissions and neural tube, oral cleft, and heart defects in offspring of mothers 35 years or older, such as spina bifida with carbon tetrachloride (aOR 2.49, 95% CI 1.09, 5.72), cleft palate

  8. The New Rich and Their Unplanned Births: Stratified Reproduction under China's Birth-planning Policy.

    Science.gov (United States)

    Shi, Lihong

    2017-12-01

    This article explores the creation and ramifications of a stratified reproductive system under China's state control of reproduction. Within this system, an emerging group of "new rich" are able to circumvent birth regulations and have unplanned births because of their financial capabilities and social networks. While China's birth-planning policy is meant to be enforced equally for all couples, the unequal access to wealth and bureaucratic power as a result of China's widening social polarization has created disparate reproductive rights and experiences. This article identifies three ways in which reproductive privileges are created. It further explores how a stratified reproductive system under state population control reinforces social polarization. While many socially marginalized couples are unable to register their unplanned children for citizenship status and social benefits, the new rich are able to legitimate their births and transfer their privilege and status to their children, thus reproducing a new generation of elites. © 2016 by the American Anthropological Association.

  9. Neural processing of speech in children is influenced by bilingual experience

    Science.gov (United States)

    Krizman, Jennifer; Slater, Jessica; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Language experience fine-tunes how the auditory system processes sound. For example, bilinguals, relative to monolinguals, have more robust evoked responses to speech that manifest as stronger neural encoding of the fundamental frequency (F0) and greater across-trial consistency. However, it is unknown whether such enhancements increase with increasing second language experience. We predict that F0 amplitude and neural consistency scale with dual-language experience during childhood, such that more years of bilingual experience leads to more robust F0 encoding and greater neural consistency. To test this hypothesis, we recorded auditory brainstem responses to the synthesized syllables ‘ba’ and ‘ga’ in two groups of bilingual children who were matched for age at test (8.4+/−0.67 years) but differed in their age of second language acquisition. One group learned English and Spanish simultaneously from birth (n=13), while the second group learned the two languages sequentially (n=15), spending on average their first four years as monolingual Spanish speakers. We find that simultaneous bilinguals have a larger F0 response to ‘ba’ and ‘ga’ and a more consistent response to ‘ba’ compared to sequential bilinguals. We also demonstrate that these neural enhancements positively relate with years of bilingual experience. These findings support the notion that bilingualism enhances subcortical auditory processing. PMID:25445377

  10. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model....

  11. Analysis of the DWPF glass pouring system using neural networks

    International Nuclear Information System (INIS)

    Calloway, T.B. Jr.; Jantzen, C.M.

    1997-01-01

    Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of ± 0.35 inwc ( 2 = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R 2 = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers

  12. DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Timur Kartbayev

    2017-03-01

    Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

  13. Developing a database management system to support birth defects surveillance in Florida.

    Science.gov (United States)

    Salemi, Jason L; Hauser, Kimberlea W; Tanner, Jean Paul; Sampat, Diana; Correia, Jane A; Watkins, Sharon M; Kirby, Russell S

    2010-01-01

    The value of any public health surveillance program is derived from the ways in which data are managed and used to improve the public's health. Although birth defects surveillance programs vary in their case volume, budgets, staff, and objectives, the capacity to operate efficiently and maximize resources remains critical to long-term survival. The development of a fully-integrated relational database management system (DBMS) can enrich a surveillance program's data and improve efficiency. To build upon the Florida Birth Defects Registry--a statewide registry relying solely on linkage of administrative datasets and unconfirmed diagnosis codes-the Florida Department of Health provided funding to the University of South Florida to develop and pilot an enhanced surveillance system in targeted areas with a more comprehensive approach to case identification and diagnosis confirmation. To manage operational and administrative complexities, a DBMS was developed, capable of managing transmission of project data from multiple sources, tracking abstractor time during record reviews, offering tools for defect coding and case classification, and providing reports to DBMS users. Since its inception, the DBMS has been used as part of our surveillance projects to guide the receipt of over 200 case lists and review of 12,924 fetuses and infants (with associated maternal records) suspected of having selected birth defects in over 90 birthing and transfer facilities in Florida. The DBMS has provided both anticipated and unexpected benefits. Automation of the processes for managing incoming case lists has reduced clerical workload considerably, while improving accuracy of working lists for field abstraction. Data quality has improved through more effective use of internal edits and comparisons with values for other data elements, while simultaneously increasing abstractor efficiency in completion of case abstraction. We anticipate continual enhancement to the DBMS in the future

  14. Sliding mode synchronization controller design with neural network for uncertain chaotic systems

    Energy Technology Data Exchange (ETDEWEB)

    Mou Chen [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)], E-mail: chenmou@nuaa.edu.cn; Jiang Changsheng; Bin Jiang; Wu Qingxian [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)

    2009-02-28

    A sliding mode synchronization controller is presented with RBF neural network for two chaotic systems in this paper. The compound disturbance of the synchronization error system consists of nonlinear uncertainties and exterior disturbances of chaotic systems. Based on RBF neural networks, a compound disturbance observer is proposed and the update law of parameters is given to monitor the compound disturbance. The synchronization controller is given based on the output of the compound disturbance observer. The designed controller can make the synchronization error convergent to zero and overcome the disruption of the uncertainty and the exterior disturbance of the system. Finally, an example is given to demonstrate the availability of the proposed synchronization control method.

  15. Three-dimensional hydrogel cell culture systems for modeling neural tissue

    Science.gov (United States)

    Frampton, John

    Two-dimensional (2-D) neural cell culture systems have served as physiological models for understanding the cellular and molecular events that underlie responses to physical and chemical stimuli, control sensory and motor function, and lead to the development of neurological diseases. However, the development of three-dimensional (3-D) cell culture systems will be essential for the advancement of experimental research in a variety of fields including tissue engineering, chemical transport and delivery, cell growth, and cell-cell communication. In 3-D cell culture, cells are provided with an environment similar to tissue, in which they are surrounded on all sides by other cells, structural molecules and adhesion ligands. Cells grown in 3-D culture systems display morphologies and functions more similar to those observed in vivo, and can be cultured in such a way as to recapitulate the structural organization and biological properties of tissue. This thesis describes a hydrogel-based culture system, capable of supporting the growth and function of several neural cell types in 3-D. Alginate hydrogels were characterized in terms of their biomechanical and biochemical properties and were functionalized by covalent attachment of whole proteins and peptide epitopes. Methods were developed for rapid cross-linking of alginate hydrogels, thus permitting the incorporation of cells into 3-D scaffolds without adversely affecting cell viability or function. A variety of neural cell types were tested including astrocytes, microglia, and neurons. Cells remained viable and functional for longer than two weeks in culture and displayed process outgrowth in 3-D. Cell constructs were created that varied in cell density, type and organization, providing experimental flexibility for studying cell interactions and behavior. In one set of experiments, 3-D glial-endothelial cell co-cultures were used to model blood-brain barrier (BBB) structure and function. This co-culture system was

  16. ISC feedforward control of gasoline engine. Adaptive system using neural network; Jidoshayo gasoline engine no ISC feedforward seigyo. Neural network wo mochiita tekioka

    Energy Technology Data Exchange (ETDEWEB)

    Kinugawa, N; Morita, S; Takiyama, T [Osaka City University, Osaka (Japan)

    1997-10-01

    For fuel economy and a good driver`s feeling, it is necessary for idle-speed to keep at a constant low speed. But keeping low speed has danger of engine stall when the engine torque is disturbed by the alternator, and so on. In this paper, adaptive feedforward idle-speed control system against electrical loads was investigated. This system was based on the reversed tansfer functions of the object system, and a neural network was used to adapt this system for aging. Then, this neural network was also used for creating feedforward table map. Good experimental results were obtained. 2 refs., 11 figs.

  17. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  18. Gross congenital malformation at birth in a government hospital.

    Science.gov (United States)

    Sachdeva, Sandeep; Nanda, Smiti; Bhalla, Kapil; Sachdeva, Ruchi

    2014-01-01

    A hospital-based cross-sectional study was undertaken to determine proportion of gross congenital malformation (GCMF) occurring at intramural births. Rate of GCMF was found to be 16.4/1000 consecutive singleton births (>28 weeks) with three leading malformation as anencephaly (44.68%), talipes equinovarus (17.02%) and meningomyelocele (10.63%). Higher risk of malformed births were noticed amongst un-booked (2.07%) in-comparison to booked (1.01%) mothers; women with low level of education (up to 8 years [2.14%] vs. at least 9 years of schooling [0.82%]); gravida status of at least 3 (2.69%) followed by 1 (1.43%) and 2 (1.0%) respectively; pre-term (5.13%) vs. term (0.66%); cesarean section (4.36%) versus vaginal delivery (0.62%). Mortality was significantly higher among congenitally malformed (17.35%) than normal (0.34%) newborns. With-in study limitation, emergence of neural tube defect as the single largest category of congenital malformation indicates maternal malnutrition (especially folic acid) that needs appropriate attention and management.

  19. Development of the disable software reporting system on the basis of the neural network

    Science.gov (United States)

    Gavrylenko, S.; Babenko, O.; Ignatova, E.

    2018-04-01

    The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

  20. Search for the Missing lncs: Gene Regulatory Networks in Neural Crest Development and Long Non-coding RNA Biomarkers of Hirschsprung's Disease

    Science.gov (United States)

    Hirschsprung’s disease (HSCR), a birth defect characterized by variable aganglionosis of the gut, affects about 1 in 5000 births, and is a consequence of abnormal development of neural crest cells, from which enteric ganglia derive. In the companion article in this issue (Shen et...

  1. A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

    International Nuclear Information System (INIS)

    Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

    2006-01-01

    In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

  2. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

    Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  3. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  4. Economic implications of home births and birth centers: a structured review.

    Science.gov (United States)

    Henderson, Jane; Petrou, Stavros

    2008-06-01

    attributed to differences in health care systems, differences in methods used, and differences in costs included. Further economic research that involves detailed bottom-up costing of alternative options for place of birth and measures multiple outcomes, including women's preferences, would help address the question of whether out-of-hospital birth is beneficial in economic terms.

  5. Dynamics of a neural system with a multiscale architecture

    Science.gov (United States)

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

    The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

  6. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  7. Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

    Science.gov (United States)

    Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

  8. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  9. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  10. Application of neural networks to software quality modeling of a very large telecommunications system.

    Science.gov (United States)

    Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

    1997-01-01

    Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

  11. Hybrid information privacy system: integration of chaotic neural network and RSA coding

    Science.gov (United States)

    Hsu, Ming-Kai; Willey, Jeff; Lee, Ting N.; Szu, Harold H.

    2005-03-01

    Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and fault-tolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.

  12. Neural systems supporting and affecting economically relevant behavior

    Directory of Open Access Journals (Sweden)

    Braeutigam S

    2012-05-01

    Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system

  13. Why do women choose an unregulated birth worker to birth at home in Australia: a qualitative study.

    Science.gov (United States)

    Rigg, Elizabeth Christine; Schmied, Virginia; Peters, Kath; Dahlen, Hannah Grace

    2017-03-28

    In Australia the choice to birth at home is not well supported and only 0.4% of women give birth at home with a registered midwife. Recent changes to regulatory requirements for midwives have become more restrictive and there is no insurance product that covers private midwives for intrapartum care at home. Freebirth (planned birth at home with no registered health professional) with an unregulated birth worker who is not a registered midwife or doctor (e.g. Doula, ex-midwife, lay midwife etc.) appears to have increased in Australia. The aim of this study is to explore the reasons why women choose to give birth at home with an unregulated birth worker (UBW) from the perspective of women and UBWs. Nine participants (five women who had UBWs at their birth and four UBWs who had themselves used UBWs in the past for their births) were interviewed in-depth and the data analysed using thematic analysis. Four themes were found: 'A traumatising system', 'An inflexible system'; 'Getting the best of both worlds' and 'Treated with love and respect versus the mechanical arm on the car assembly line'. Women interviewed for this study either experienced or were exposed to mainstream care, which they found traumatising. They were not able to access their preferred birth choices, which caused them to perceive the system as inflexible. They interpreted this as having no choice when choice was important to them. The motivation then became to seek alternative options of care that would more appropriately meet their needs, and help avoid repeated trauma through mainstream care. Women who engaged UBWs viewed them as providing the best of both worlds - this was birthing at home with a knowledgeable person who was unconstrained by rules or regulations and who respected and supported the woman's philosophical view of birth. Women perceived UBWs as not only the best opportunity to achieve a natural birth but also as providing 'a safety net' in case access to emergency care was required.

  14. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    Science.gov (United States)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  15. Population-Based Surveillance of Birth Defects Potentially Related to Zika Virus Infection - 15 States and U.S. Territories, 2016.

    Science.gov (United States)

    Delaney, Augustina; Mai, Cara; Smoots, Ashley; Cragan, Janet; Ellington, Sascha; Langlois, Peter; Breidenbach, Rebecca; Fornoff, Jane; Dunn, Julie; Yazdy, Mahsa; Scotto-Rosato, Nancy; Sweatlock, Joseph; Fox, Deborah; Palacios, Jessica; Forestieri, Nina; Leedom, Vinita; Smiley, Mary; Nance, Amy; Lake-Burger, Heather; Romitti, Paul; Fall, Carrie; Prado, Miguel Valencia; Barton, Jerusha; Bryan, J Michael; Arias, William; Brown, Samara Viner; Kimura, Jonathan; Mann, Sylvia; Martin, Brennan; Orantes, Lucia; Taylor, Amber; Nahabedian, John; Akosa, Amanda; Song, Ziwei; Martin, Stacey; Ramlal, Roshan; Shapiro-Mendoza, Carrie; Isenburg, Jennifer; Moore, Cynthia A; Gilboa, Suzanne; Honein, Margaret A

    2018-01-26

    Zika virus infection during pregnancy can cause serious birth defects, including microcephaly and brain abnormalities (1). Population-based birth defects surveillance systems are critical to monitor all infants and fetuses with birth defects potentially related to Zika virus infection, regardless of known exposure or laboratory evidence of Zika virus infection during pregnancy. CDC analyzed data from 15 U.S. jurisdictions conducting population-based surveillance for birth defects potentially related to Zika virus infection.* Jurisdictions were stratified into the following three groups: those with 1) documented local transmission of Zika virus during 2016; 2) one or more cases of confirmed, symptomatic, travel-associated Zika virus disease reported to CDC per 100,000 residents; and 3) less than one case of confirmed, symptomatic, travel-associated Zika virus disease reported to CDC per 100,000 residents. A total of 2,962 infants and fetuses (3.0 per 1,000 live births; 95% confidence interval [CI] = 2.9-3.2) (2) met the case definition. † In areas with local transmission there was a non-statistically significant increase in total birth defects potentially related to Zika virus infection from 2.8 cases per 1,000 live births in the first half of 2016 to 3.0 cases in the second half (p = 0.10). However, when neural tube defects and other early brain malformations (NTDs) § were excluded, the prevalence of birth defects strongly linked to congenital Zika virus infection increased significantly, from 2.0 cases per 1,000 live births in the first half of 2016 to 2.4 cases in the second half, an increase of 29 more cases than expected (p = 0.009). These findings underscore the importance of surveillance for birth defects potentially related to Zika virus infection and the need for continued monitoring in areas at risk for Zika.

  16. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

    Science.gov (United States)

    Ding, Lei; Xiao, Lin; Liao, Bolin; Lu, Rongbo; Peng, Hua

    2017-01-01

    To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

  17. Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

    Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

  18. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

    Science.gov (United States)

    Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

    2017-10-01

    Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

  19. PLANNED HOME BIRTH: A REVIEW

    Directory of Open Access Journals (Sweden)

    Tamara Serdinšek

    2016-05-01

    Full Text Available Background: Home birth is as old as humanity, but still most middle- and high-income countries consider hospitals as the safest birth settings, as complications regarding birth are highly unpredictable. Despite this there are a few countries in which home birth in integrated into official healthcare system (the Netherlands, United Kingdom, Canada etc.. Home births can be divided into unplanned and planned, and the latter can be further categorized by the presence of the birth attendants. This review focuses on planned home births, which are differently represented throughout the world. In the United States 0.6-1.0% of all children are born at home, in the United Kingdom 2-3%, in Canada 1.6% and in the Netherlands 20-30%. For Slovenia, the number of planned home births is unknown; however, in 2010 0.1% of children were born outside medical facilities.Conclusions: The safety of home birth in still under the debate. While research confirms smaller number of obstetric interventions and some complications in mothers who give birth at home, the data regarding the neonatal and perinatal mortality and morbidity is still conflicting. This confirms the need for large multicentric trials in this field. Current home birth guidelines emphasize that women should be well informed regarding the possible advantages and disadvantages of home births. In addition, the emphasis is on definition of selection criteria for home birth, indications for intrapartal transfer to the hospital and appropriate education of birth attendants. 

  20. A neural network method for solving a system of linear variational inequalities

    International Nuclear Information System (INIS)

    Lan Hengyou; Cui Yishun

    2009-01-01

    In this paper, we transmute the solution for a new system of linear variational inequalities to an equilibrium point of neural networks, and by using analytic technique, some sufficient conditions are presented. Further, the estimation of the exponential convergence rates of the neural networks is investigated. The new and useful results obtained in this paper generalize and improve the corresponding results of recent works.

  1. Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

    Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

  2. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  3. The role of neural networks in nuclear power plant safety systems

    International Nuclear Information System (INIS)

    Boger, Z.

    1993-01-01

    Neural networks (NN) techniques have been applied in recent years to many systems by researchers in the nuclear power industry, mainly for modeling and sensor validation. Recent results are reviewed, including new directions in applications to control systems, safety analysis, and ''virtual'' instruments. As new fast learning algorithms become available, large systems may be learned effectively, even with few training examples. The nuclear industry hesitates to include NN in safety related systems, but it seems that the obstacles could be overcome with the demonstration of successful applications, even from other industries. Coupling of full-scale reactor simulators, as fault database generators, with neural networks learning should be explored. The integration of Expert System technology with NN should improve the Validation and Verification tasks, and also help overcome psychological barriers. It may prove that the potential of NN to help operators, compared with the existing and proposed alternatives, outweigh the risks. (author). 58 refs, 2 figs

  4. An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode.

    Science.gov (United States)

    Shon, Ahnsei; Chu, Jun-Uk; Jung, Jiuk; Kim, Hyungmin; Youn, Inchan

    2017-12-21

    Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

  5. An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode

    Directory of Open Access Journals (Sweden)

    Ahnsei Shon

    2017-12-01

    Full Text Available Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC-compliant power transmission circuit, a medical implant communication service (MICS-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

  6. Interleukin-6 Regulates Adult Neural Stem Cell Numbers during Normal and Abnormal Post-natal Development

    Directory of Open Access Journals (Sweden)

    Mekayla A. Storer

    2018-05-01

    Full Text Available Summary: Circulating systemic factors can regulate adult neural stem cell (NSC biology, but the identity of these circulating cues is still being defined. Here, we have focused on the cytokine interleukin-6 (IL-6, since increased circulating levels of IL-6 are associated with neural pathologies such as autism and bipolar disorder. We show that IL-6 promotes proliferation of post-natal murine forebrain NSCs and that, when the IL-6 receptor is inducibly knocked out in post-natal or adult neural precursors, this causes a long-term decrease in forebrain NSCs. Moreover, a transient circulating surge of IL-6 in perinatal or adult mice causes an acute increase in neural precursor proliferation followed by long-term depletion of adult NSC pools. Thus, IL-6 signaling is both necessary and sufficient for adult NSC self-renewal, and acute perturbations in circulating IL-6, as observed in many pathological situations, have long-lasting effects on the size of adult NSC pools. : In this report, Storer and colleagues demonstrate that the circulating cytokine IL-6, which is elevated in humans in different pathological situations, can perturb neural stem cell biology after birth. They show that IL-6 signaling is essential for self-renewal and maintenance of post-natal and adult NSCs in the murine forebrain under normal homeostatic conditions. Keywords: interleukin-6, neural stem cell, adult neurogenesis, CNS cytokines, postnatal brain development, stem cell depletion, neural stem cell niche, circulating stem cell factors, olfactory bulb

  7. CDC Vital Signs: Preventing Repeat Teen Births

    Science.gov (United States)

    ... control after they have given birth. Although teen birth rates have been falling for the last two decades, ... effective forms of birth control. SOURCE: National Vital Statistics System, teens, ages 15–19, 2010 Larger image ...

  8. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

    2006-01-01

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  9. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  10. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

  11. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  12. Neural tube defects in Malaysia: data from the Malaysian National Neonatal Registry.

    Science.gov (United States)

    Boo, Nem-Yun; Cheah, Irene G S; Thong, Meow-Keong

    2013-10-01

    This study aimed to determine the prevalence and early outcome of neural tube defects (NTDs) in Malaysia. This prospective study included all neonates with NTDs (spina bifida, anencephaly, encephalocoele) born in 2009 in 32 Malaysian hospitals in the Malaysian National Neonatal Network. The prevalence of NTDs was 0.42 per 1000 live births, being highest among the indigenous people of Sarawak (1.09 per 1000 live births) and lowest among Malaysians of Chinese descent (0.09 per 1000 live births). The most common type of NTDs was anencephaly (0.19 per 1000 live births), followed by spina bifida (0.11 per 1000 live births) and encephalocoele (0.07 per 1000 live births). Majority of the infants with anencephaly (94.5%, n = 51), 45.8% (n = 11) with encephalocoele and 9.5% (n = 4) with spina bifida died. The median duration of hospital stay was 4 (range: 0-161) days. NTDs were common in Malaysia. Mortality was high. Long-term monitoring of NTD prevalence following folic fortification of food is recommended.

  13. [Inclusion of traditional birth attendants in the public health care system in Brazil: reflecting on challenges].

    Science.gov (United States)

    Gusman, Christine Ranier; Viana, Ana Paula de Andrade Lima; Miranda, Margarida Araújo Barbosa; Pedrosa, Mayane Vilela; Villela, Wilza Vieira

    2015-05-01

    The present article describes an experience with traditional birth attendants carried out in the state of Tocantins, Brazil, between 2010 and 2014. The experience was part of a diagnostic project to survey home deliveries in the state of Tocantins and set up a registry of traditional birth attendants for the Health Ministry's Working with Traditional Birth Attendants Program (PTPT). The project aimed to articulate the home deliveries performed by traditional birth attendants to the local health care systems (SUS). Sixty-seven active traditional birth attendants were identified in the state of Tocantins, and 41 (39 indigenous) participated in workshops. During these workshops, they discussed their realities, difficulties, and solutions in the context of daily adversities. Birth attendants were also trained in the use of biomedical tools and neonatal resuscitation. Based on these experiences, the question came up regarding the true effectiveness of the strategy to include traditional birth attendants in the SUS. The present article discusses this theme with support from the relevant literature. The dearth of systematic studies focusing on the impact of PTPT actions on the routine of traditional birth attendants, including perinatal outcomes and remodeling of health practices in rural, riverfront, former slave, forest, and indigenous communities, translates into a major gap in terms of the knowledge regarding the effectiveness of such initiatives.

  14. A wireless transmission neural interface system for unconstrained non-human primates.

    Science.gov (United States)

    Fernandez-Leon, Jose A; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J; Hansen, Bryan J; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

  15. A wireless transmission neural interface system for unconstrained non-human primates

    Science.gov (United States)

    Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

  16. Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

    Science.gov (United States)

    Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

    2018-02-22

    Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

  17. Maternal systemic or cord blood inflammation is associated with birth anthropometry in a Tanzanian prospective cohort.

    Science.gov (United States)

    Wilkinson, A L; Pedersen, S H; Urassa, M; Michael, D; Andreasen, A; Todd, J; Kinung'hi, S M; Changalucha, J; McDermid, J M

    2017-01-01

    HIV infection is associated with chronic systemic inflammation, with or without antiretroviral therapy. Consequences for foetal growth are not understood, particularly in settings where multiple maternal infections and malnutrition are common. The study was designed to examine maternal systemic circulating and umbilical cord blood cytokine concentrations in relation to birth anthropometry in a Tanzanian prospective cohort. A 9-plex panel of maternal plasma cytokines in HIV-positive (n = 44) and HIV-negative (n = 70) mothers and the same cytokines in umbilical cord blood collected at delivery was assayed. Linear regression modelled associations between maternal or cord blood cytokines and birth anthropometry. Health indicators (haemoglobin, mid-upper-arm circumference, body mass index) in HIV-positive mothers without considerable immunosuppression did not differ from HIV-negative women. Despite this, HIV-exposed infants had lower birthweight and length. Subgroup analyses indicated that HIV management using HAART was associated with lower plasma TNF-α, as were longer durations of any antiretroviral therapy (≥2 months). Greater maternal plasma TNF-α was associated with earlier delivery (-1.7 weeks, P = 0.039) and lower birthweights (-287 g; P = 0.020), while greater umbilical cord TNF-α (-1.43 cm; P = 0.036) and IL-12p70 (-2.4 cm; P = 0.008) were associated with shorter birth length. Birthweight was inversely associated with cord IL-12p70 (-723 g; P = 0.001) and IFN-γ (-482 g, P = 0.007). Maternal cytokines during pregnancy did not correlate with umbilical cord cytokines at delivery. Systemic inflammation identified in maternal plasma or umbilical cord blood was associated with poorer birth anthropometrics in HIV-exposed and HIV-unexposed infants. Controlling maternal and/or foetal systemic inflammation may improve birth anthropometry. © 2016 The Authors. Tropical Medicine & International Health Published by John Wiley & Sons Ltd.

  18. Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

    Science.gov (United States)

    Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

    1993-01-01

    This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

  19. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  20. Neural net based determination of generator-shedding requirements in electric power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States)

    1992-09-01

    This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)

  1. Towards an Irritable Bowel Syndrome Control System Based on Artificial Neural Networks

    Science.gov (United States)

    Podolski, Ina; Rettberg, Achim

    To solve health problems with medical applications that use complex algorithms is a trend nowadays. It could also be a chance to help patients with critical problems caused from nerve irritations to overcome them and provide a better living situation. In this paper a system for monitoring and controlling the nerves from the intestine is described on a theoretical basis. The presented system could be applied to the irritable bowel syndrome. For control a neural network is used. The advantages for using a neural network for the control of irritable bowel syndrome are the adaptation and learning. These two aspects are important because the syndrome behavior varies from patient to patient and have also concerning the time a lot of variations with respect to each patient. The developed neural network is implemented and can be simulated. Therefore, it can be shown how the network monitor and control the nerves for individual input parameters.

  2. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

    Science.gov (United States)

    Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

    2014-09-01

    Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

  3. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  4. A neurally inspired musical instrument classification system based upon the sound onset.

    Science.gov (United States)

    Newton, Michael J; Smith, Leslie S

    2012-06-01

    Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

  5. Preterm Birth

    Science.gov (United States)

    ... for Health Care Providers For Health Care Providers: Electronic Nicotine Delivery Systems and Pregnancy CDC Activities Resources ... births and improving neonatal outcomes. View the archived presentation and publication Related Links Is It Worth It? ...

  6. Stellar Image Interpretation System using Artificial Neural Networks: Unipolar Function Case

    Directory of Open Access Journals (Sweden)

    F. I. Younis

    2001-01-01

    Full Text Available An artificial neural network based system for interpreting astronomical images has been developed. The system is based on feed-forward Artificial Neural Networks (ANNs with error back-propagation learning. Knowledge about images of stars, cosmic ray events and noise found in images is used to prepare two sets of input patterns to train and test our approach. The system has been developed and implemented to scan astronomical digital images in order to segregate stellar images from other entities. It has been coded in C language for users of personal computers. An astronomical image of a star cluster from other objects is undertaken as a test case. The obtained results are found to be in very good agreement with those derived from the DAOPHOTII package, which is widely used in the astronomical community. It is proved that our system is simpler, much faster and more reliable. Moreover, no prior knowledge, or initial data from the frame to be analysed is required.

  7. Optimization of workflow scheduling in Utility Management System with hierarchical neural network

    Directory of Open Access Journals (Sweden)

    Srdjan Vukmirovic

    2011-08-01

    Full Text Available Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

  8. Neural Computations in a Dynamical System with Multiple Time Scales.

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

  9. Neural systems and hormones mediating attraction to infant and child faces

    Directory of Open Access Journals (Sweden)

    Lizhu eLuo

    2015-07-01

    Full Text Available We find infant faces highly attractive as a result of specific features which Konrad Lorenz termed Kindchenschema or baby schema, and this is considered to be an important adaptive trait for promoting protective and caregiving behaviors in adults, thereby increasing the chances of infant survival. This review first examines the behavioral support for this effect and physical and behavioral factors which can influence it. It next reviews the increasing number of neuroimaging and electrophysiological studies investigating the neural circuitry underlying this baby schema effect in both parents and non-parents of both sexes. Next it considers potential hormonal contributions to the baby schema effect in both sexes and then neural effects associated with reduced responses to infant cues in post-partum depression, anxiety and drug taking. Overall the findings reviewed reveal a very extensive neural circuitry involved in our perception of cutenessin infant faces with enhanced activation compared to adult faces being found in brain regions involved in face perception, attention, emotion, empathy, memory, reward and attachment, theory of mind and also control of motor responses.Both mothers and fathers also show evidence for enhanced responses in these same neural systems when viewing their own as opposed to another child. Furthermore, responses to infant cues in many of these neural systems are reduced in mothers with post-partum depression or anxiety or have taken addictive drugs throughout pregnancy. In general reproductively active women tend to rate infant faces as cuter than men, which may reflect both heightened attention to relevant cues and a stronger activation in their brain reward circuitry. Perception of infant cuteness may also be influenced by reproductive hormones with the hypothalamic neuropeptide oxytocin being most strongly associated to date with increased attention andattractionto infant cues in both sexes.

  10. Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

    International Nuclear Information System (INIS)

    Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

    2007-01-01

    Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependent and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy

  11. The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data

    Directory of Open Access Journals (Sweden)

    Ravit Arav-Boger

    2008-01-01

    Full Text Available A large number of CMV strains has been reported to circulate in the human population, and the biological significance of these strains is currently an active area of research. The analysis of complex genetic information may be limited using conventional phylogenetic techniques. We constructed artificial neural networks to determine their feasibility in predicting the outcome of congenital CMV disease (defined as presence of CMV symptoms at birth based on two data sets: 54 sequences of CMV gene UL144 obtained from 54 amniotic fluids of women who contracted acute CMV infection during their pregnancy, and 80 sequences of 4 genes (US28, UL144, UL146 and UL147 obtained from urine, saliva or blood of 20 congenitally infected infants that displayed different outcomes at birth. When data from all four genes was used in the 20-infants’ set, the artificial neural network model accurately identified outcome in 90% of cases. While US28 and UL147 had low yield in predicting outcome, UL144 and UL146 predicted outcome in 80% and 85% respectively when used separately. The model identified specific nucleotide positions that were highly relevant to prediction of outcome. The artificial neural network classified genotypes in agreement with classic phylogenetic analysis. We suggest that artificial neural networks can accurately and efficiently analyze sequences obtained from larger cohorts to determine specific outcomes.The ANN training and analysis code is commercially available from Optimal Neural Informatics (Pikesville, MD.

  12. AN ARTIFICIAL NEURAL NETWORK EVALUATION OF TUBERCULOSIS USING GENETIC AND PHYSIOLOGICAL PATIENT DATA

    International Nuclear Information System (INIS)

    Griffin, William O.; Darsey, Jerry A.; Hanna, Josh; Razorilova, Svetlana; Kitaev, Mikhael; Alisherov, Avtandiil; Tarasenko, Olga

    2010-01-01

    When doctors see more cases of patients with tell-tale symptoms of a disease, it is hoped that they will be able to recognize an infection administer treatment appropriately, thereby speeding up recovery for sick patients. We hope that our studies can aid in the detection of tuberculosis by using a computer model called an artificial neural network. Our model looks at patients with and without tuberculosis (TB). The data that the neural network examined came from the following: patient' age, gender, place, of birth, blood type, Rhesus (Rh) factor, and genes of the human Leukocyte Antigens (HLA) system (9q34.1) present in the Major Histocompatibility Complex. With availability in genetic data and good research, we hope to give them an advantage in the detection of tuberculosis. We try to mimic the doctor's experience with a computer test, which will learn from patient data the factors that contribute to TB.

  13. Neural convergence for language comprehension and grammatical class production in highly proficient bilinguals is independent of age of acquisition.

    Science.gov (United States)

    Consonni, Monica; Cafiero, Riccardo; Marin, Dario; Tettamanti, Marco; Iadanza, Antonella; Fabbro, Franco; Perani, Daniela

    2013-05-01

    In bilinguals, native (L1) and second (L2) languages are processed by the same neural resources that can be modulated by age of second language acquisition (AOA), proficiency level, and daily language exposure and usage. AOA seems to particularly affect grammar processing, where a complete neural convergence has been shown only in bilinguals with parallel language acquisition from birth. Despite the fact that proficiency-related neuroanatomical differences have been well documented in language comprehension (LC) and production, few reports have addressed the influence of language exposure. A still unanswered question pertains to the role of AOA, when proficiency is comparably high across languages, with respect to its modulator effects both on LC and production. Here, we evaluated with fMRI during sentence comprehension and verb and noun production tasks, two groups of highly proficient bilinguals only differing in AOA. One group learned Italian and Friulian in parallel from birth, whereas the second group learned Italian between 3 and 6 years. All participants were highly exposed to both languages, but more to Italian than Friulian. The results indicate a complete overlap of neural activations for the comprehension of both languages, not only in bilinguals from birth, but also in late bilinguals. A slightly extra activation in the left thalamus for the less-exposed language confirms that exposure may affect language processing. Noteworthy, we report for the first time that, when proficiency and exposure are kept high, noun and verb production recruit the same neural networks for L1 and L2, independently of AOA. These results support the neural convergence hypothesis. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

    Science.gov (United States)

    Aftab, Muhammad Saleheen; Shafiq, Muhammad

    2015-11-01

    This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Influence of birth order, birth weight, colostrum and serum immunoglobulin G on neonatal piglet survival.

    Science.gov (United States)

    Cabrera, Rafael A; Lin, Xi; Campbell, Joy M; Moeser, Adam J; Odle, Jack

    2012-12-23

    Intake of colostrum after birth is essential to stimulate intestinal growth and function, and to provide systemic immunological protection via absorption of Immunoglobulin G (IgG). The birth order and weight of 745 piglets (from 75 litters) were recorded during a one-week period of farrowing. Only pigs weighing greater than 0.68 kg birth weight were chosen for the trial. Sow colostrum was collected during parturition, and piglets were bled between 48 and 72 hours post-birth. Piglet serum IgG and colostral IgG concentrations were determined by radial immunodiffusion. Sow parity had a significant (P birth order accounted for another 4% of the variation observed in piglet serum IgG concentration (P birth weight had no detectable effect. Piglet serum IgG concentration had both a linear (P Birth order had no detectable effect on survival, but birth weight had a positive linear effect (P birth had a 68% survival rate, and those weighing 1.6 kg (n = 158) had an 89% survival. We found that the combination of sow colostrum IgG concentration and birth order can account for 10% of the variation of piglet serum IgG concentration and that piglets with less than 1,000 mg/dl IgG serum concentration and weight of 0.9 kg at birth had low survival rate when compared to their larger siblings. The effective management of colostrum uptake in neonatal piglets in the first 24 hrs post-birth may potentially improve survival from birth to weaning.

  16. Birth Satisfaction Scale/Birth Satisfaction Scale-Revised (BSS/BSS-R): A large scale United States planned home birth and birth centre survey

    OpenAIRE

    Fleming, Susan E.; Donovan-Batson, Colleen.; Burduli, Ekaterina.; Barbosa-Leiker, Celestina.; Hollins Martin, Caroline J.; Martin, Colin R.

    2016-01-01

    Objective:\\ud to explore the prevalence of birth satisfaction for childbearing women planning to birth in their home or birth centers in the United States. Examining differences in birth satisfaction of the home and birth centers; and those who birthed in a hospital using the 30-item Birth Satisfaction Scale (BSS) and the 10-item Birth Satisfaction Scale-Revised (BSS-R).\\ud Study design:\\ud a quantitative survey using the BSS and BSS-R were employed. Additional demographic data were collected...

  17. Describing the Prevalence of Neural Tube Defects Worldwide: A Systematic Literature Review.

    Science.gov (United States)

    Zaganjor, Ibrahim; Sekkarie, Ahlia; Tsang, Becky L; Williams, Jennifer; Razzaghi, Hilda; Mulinare, Joseph; Sniezek, Joseph E; Cannon, Michael J; Rosenthal, Jorge

    2016-01-01

    Folate-sensitive neural tube defects (NTDs) are an important, preventable cause of morbidity and mortality worldwide. There is a need to describe the current global burden of NTDs and identify gaps in available NTD data. We conducted a systematic review and searched multiple databases for NTD prevalence estimates and abstracted data from peer-reviewed literature, birth defects surveillance registries, and reports published between January 1990 and July 2014 that had greater than 5,000 births and were not solely based on mortality data. We classified countries according to World Health Organization (WHO) regions and World Bank income classifications. The initial search yielded 11,614 results; after systematic review we identified 160 full text manuscripts and reports that met the inclusion criteria. Data came from 75 countries. Coverage by WHO region varied in completeness (i.e., % of countries reporting) as follows: African (17%), Eastern Mediterranean (57%), European (49%), Americas (43%), South-East Asian (36%), and Western Pacific (33%). The reported NTD prevalence ranges and medians for each region were: African (5.2-75.4; 11.7 per 10,000 births), Eastern Mediterranean (2.1-124.1; 21.9 per 10,000 births), European (1.3-35.9; 9.0 per 10,000 births), Americas (3.3-27.9; 11.5 per 10,000 births), South-East Asian (1.9-66.2; 15.8 per 10,000 births), and Western Pacific (0.3-199.4; 6.9 per 10,000 births). The presence of a registry or surveillance system for NTDs increased with country income level: low income (0%), lower-middle income (25%), upper-middle income (70%), and high income (91%). Many WHO member states (120/194) did not have any data on NTD prevalence. Where data are collected, prevalence estimates vary widely. These findings highlight the need for greater NTD surveillance efforts, especially in lower-income countries. NTDs are an important public health problem that can be prevented with folic acid supplementation and fortification of staple foods.

  18. Describing the Prevalence of Neural Tube Defects Worldwide: A Systematic Literature Review.

    Directory of Open Access Journals (Sweden)

    Ibrahim Zaganjor

    Full Text Available Folate-sensitive neural tube defects (NTDs are an important, preventable cause of morbidity and mortality worldwide. There is a need to describe the current global burden of NTDs and identify gaps in available NTD data.We conducted a systematic review and searched multiple databases for NTD prevalence estimates and abstracted data from peer-reviewed literature, birth defects surveillance registries, and reports published between January 1990 and July 2014 that had greater than 5,000 births and were not solely based on mortality data. We classified countries according to World Health Organization (WHO regions and World Bank income classifications. The initial search yielded 11,614 results; after systematic review we identified 160 full text manuscripts and reports that met the inclusion criteria. Data came from 75 countries. Coverage by WHO region varied in completeness (i.e., % of countries reporting as follows: African (17%, Eastern Mediterranean (57%, European (49%, Americas (43%, South-East Asian (36%, and Western Pacific (33%. The reported NTD prevalence ranges and medians for each region were: African (5.2-75.4; 11.7 per 10,000 births, Eastern Mediterranean (2.1-124.1; 21.9 per 10,000 births, European (1.3-35.9; 9.0 per 10,000 births, Americas (3.3-27.9; 11.5 per 10,000 births, South-East Asian (1.9-66.2; 15.8 per 10,000 births, and Western Pacific (0.3-199.4; 6.9 per 10,000 births. The presence of a registry or surveillance system for NTDs increased with country income level: low income (0%, lower-middle income (25%, upper-middle income (70%, and high income (91%.Many WHO member states (120/194 did not have any data on NTD prevalence. Where data are collected, prevalence estimates vary widely. These findings highlight the need for greater NTD surveillance efforts, especially in lower-income countries. NTDs are an important public health problem that can be prevented with folic acid supplementation and fortification of staple foods.

  19. Consensus-based distributed cooperative learning from closed-loop neural control systems.

    Science.gov (United States)

    Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

    2015-02-01

    In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

  20. A novel three-dimensional system to study interactions between endothelial cells and neural cells of the developing central nervous system

    Directory of Open Access Journals (Sweden)

    Milner Richard

    2007-01-01

    Full Text Available Abstract Background During angiogenesis in the developing central nervous system (CNS, endothelial cells (EC detach from blood vessels growing on the brain surface, and migrate into the expanding brain parenchyma. Brain angiogenesis is regulated by growth factors and extracellular matrix (ECM proteins secreted by cells of the developing CNS. In addition, recent evidence suggests that EC play an important role in establishing the neural stem cell (NSC niche. Therefore, two-way communication between EC and neural cells is of fundamental importance in the developing CNS. To study the interactions between brain EC and neural cells of the developing CNS, a novel three-dimensional (3-D murine co-culture system was developed. Fluorescent-labelled brain EC were seeded onto neurospheres; floating cellular aggregates that contain NSC/neural precursor cells (NPC and smaller numbers of differentiated cells. Using this system, brain EC attachment, survival and migration into neurospheres was evaluated and the role of integrins in mediating the early adhesive events addressed. Results Brain EC attached, survived and migrated deep into neurospheres over a 5-day period. Neurospheres express the ECM proteins fibronectin and laminin, and brain EC adhesion to neurospheres was inhibited by RGD peptides and antibodies specific for the β1, but not the α6 integrin subunit. Conclusion A novel 3-D co-culture system for analysing the interactions between EC and neural cells of the developing CNS is presented. This system could be used to investigate the reciprocal influence of EC and NSC/NPC; to examine how NSC/NPC influence cerebral angiogenesis, and conversely, to examine how EC regulate the maintenance and differentiation of NSC/NPC. Using this system it is demonstrated that EC attachment to neurospheres is mediated by the fibronectin receptor, α5β1 integrin.

  1. Neural network regulation driven by autonomous neural firings

    Science.gov (United States)

    Cho, Myoung Won

    2016-07-01

    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  2. Neural multigrid for gauge theories and other disordered systems

    International Nuclear Information System (INIS)

    Baeker, M.; Kalkreuter, T.; Mack, G.; Speh, M.

    1992-09-01

    We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a 'neural computations' point of view into large scale simulations: First, the system must learn how to do the simulations efficiently, then do the simulation (fast). The method can also be used to provide smooth interpolation kernels which are needed in multigrid Monte Carlo updates. (orig.)

  3. Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

    Science.gov (United States)

    Niu, Ben; Li, Lu

    2018-06-01

    This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

  4. Can the UK's birth registration system better serve the interests of those born following collaborative assisted reproduction?

    Science.gov (United States)

    Crawshaw, Marilyn A; Blyth, Eric D; Feast, Julia

    2017-06-01

    Current birth registration systems fail to serve adequately the interests of those born as a result of gamete and embryo donation and surrogacy. In the UK, changes to the birth registration system have been piecemeal, reactive and situation-specific and no information is recorded about gamete donors. Birth registration has thereby become a statement of legal parentage and citizenship only, without debate as to whether it should serve any wider functions. This sits uneasily with the increasingly accepted human right to know one's genetic and gestational as well as legal parents, and the duty of the State to facilitate that right. This commentary sets out one possible model for reform to better ensure that those affected become aware of, and/or have access to, knowledge about their origins and that such information is stored and released effectively without compromising individual privacy. Among other features, our proposal links the birth registration system and the information stored in the Human Fertilization and Embryology Authority's Register of Information, although further work than we have been able to undertake here is necessary to ensure a better fit where cross-border treatment services or informal arrangements have been involved. The time for debate and reform is well overdue.

  5. Controllable entanglement sudden birth of Heisenberg spins

    International Nuclear Information System (INIS)

    Zheng Qiang; Zhi Qijun; Zhang Xiaoping; Ren Zhongzhou

    2011-01-01

    We investigate the Entanglement Sudden Birth (ESB) of two Heisenberg spins A and B. The third controller, qutrit C is introduced, which only has the Dzyaloshinskii-Moriya (DM) spin-orbit interaction with qubit B. We find that the DM interaction is necessary to induce the Entanglement Sudden Birth of the system qubits A and B, and the initial states of the system qubits and the qutrit C are also important to control its Entanglement Sudden Birth. (authors)

  6. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1992-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems

  7. Catholics vs. Protestants - Birth and Tax

    DEFF Research Database (Denmark)

    Gøtze, Michael

    2008-01-01

    Danish Supreme Court Decision, Protestant State Church, Religious Minority, Birth Registration, Family Law, Taxation System, Discrimination, European Human Rights Law, Constitutional Law, Law and Religion Udgivelsesdato: 28. July......Danish Supreme Court Decision, Protestant State Church, Religious Minority, Birth Registration, Family Law, Taxation System, Discrimination, European Human Rights Law, Constitutional Law, Law and Religion Udgivelsesdato: 28. July...

  8. Influence of birth order, birth weight, colostrum and serum immunoglobulin G on neonatal piglet survival

    Directory of Open Access Journals (Sweden)

    Cabrera Rafael A

    2012-12-01

    Full Text Available Abstract Background Intake of colostrum after birth is essential to stimulate intestinal growth and function, and to provide systemic immunological protection via absorption of Immunoglobulin G (IgG. The birth order and weight of 745 piglets (from 75 litters were recorded during a one-week period of farrowing. Only pigs weighing greater than 0.68 kg birth weight were chosen for the trial. Sow colostrum was collected during parturition, and piglets were bled between 48 and 72 hours post-birth. Piglet serum IgG and colostral IgG concentrations were determined by radial immunodiffusion. Results Sow parity had a significant (P Conclusion We found that the combination of sow colostrum IgG concentration and birth order can account for 10% of the variation of piglet serum IgG concentration and that piglets with less than 1,000 mg/dl IgG serum concentration and weight of 0.9 kg at birth had low survival rate when compared to their larger siblings. The effective management of colostrum uptake in neonatal piglets in the first 24 hrs post-birth may potentially improve survival from birth to weaning.

  9. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

    Energy Technology Data Exchange (ETDEWEB)

    Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

    2009-09-15

    Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

  10. Semi-empirical neural network models of controlled dynamical systems

    Directory of Open Access Journals (Sweden)

    Mihail V. Egorchev

    2017-12-01

    Full Text Available A simulation approach is discussed for maneuverable aircraft motion as nonlinear controlled dynamical system under multiple and diverse uncertainties including knowledge imperfection concerning simulated plant and its environment exposure. The suggested approach is based on a merging of theoretical knowledge for the plant with training tools of artificial neural network field. The efficiency of this approach is demonstrated using the example of motion modeling and the identification of the aerodynamic characteristics of a maneuverable aircraft. A semi-empirical recurrent neural network based model learning algorithm is proposed for multi-step ahead prediction problem. This algorithm sequentially states and solves numerical optimization subproblems of increasing complexity, using each solution as initial guess for subsequent subproblem. We also consider a procedure for representative training set acquisition that utilizes multisine control signals.

  11. Home birth after hospital birth: women's choices and reflections.

    Science.gov (United States)

    Bernhard, Casey; Zielinski, Ruth; Ackerson, Kelly; English, Jessica

    2014-01-01

    The number of US women choosing home birth is increasing. Little is known about women who choose home birth after having experienced hospital birth; therefore, the purpose of this research was to explore reasons why these women choose home birth and their perceptions regarding their birth experiences. Qualitative description was the research design, whereby focus groups were conducted with women who had hospital births and subsequently chose home birth. Five focus groups were conducted (N = 20), recorded, and transcribed verbatim. Qualitative content analysis was undertaken allowing themes to emerge. Five themes emerged from the women's narratives: 1) choices and empowerment: with home birth, women felt they were given real choices rather than perceived choices, giving them feelings of empowerment; 2) interventions and interruptions: women believed things were done that were not helpful to the birth process, and there were interruptions associated with their hospital births; 3) disrespect and dismissal: participants believed that during hospital birth, providers were more focused on the laboring woman's uterus, with some experiencing dismissal from their hospital provider when choosing to birth at home; 4) birth space: giving birth in their own home, surrounded by people they chose, created a peaceful and calm environment; and 5) connection: women felt connected to their providers, families, newborns, and bodies during their home birth. For most participants, dissatisfaction with hospital birth influenced their subsequent decision to choose home birth. Despite experiencing challenges associated with this decision, women expressed satisfaction with their home birth. © 2014 by the American College of Nurse-Midwives.

  12. Birth Satisfaction Scale/Birth Satisfaction Scale-Revised (BSS/BSS-R): A large scale United States planned home birth and birth centre survey.

    Science.gov (United States)

    Fleming, Susan E; Donovan-Batson, Colleen; Burduli, Ekaterina; Barbosa-Leiker, Celestina; Hollins Martin, Caroline J; Martin, Colin R

    2016-10-01

    to explore the prevalence of birth satisfaction for childbearing women planning to birth in their home or birth centers in the United States. Examining differences in birth satisfaction of the home and birth centers; and those who birthed in a hospital using the 30-item Birth Satisfaction Scale (BSS) and the 10-item Birth Satisfaction Scale-Revised (BSS-R). a quantitative survey using the BSS and BSS-R were employed. Additional demographic data were collected using electronic linkages (Qualtrics ™ ). a convenience sample of childbearing women (n=2229) who had planned to birth in their home or birth center from the US (United States) participated. Participants were recruited via professional and personal contacts, primarily their midwives. the total 30-item BSS score mean was 128.98 (SD 16.92) and the 10-item BSS-R mean score was 31.94 (SD 6.75). Sub-scale mean scores quantified the quality of care provision, women's personal attributes, and stress experienced during labour. Satisfaction was higher for women with vaginal births compared with caesareans deliveries. In addition, satisfaction was higher for women who had both planned to deliver in a home or a birth centre, and who had actually delivered in a home or a birth center. total and subscale birth satisfaction scores were positive and high for the overall sample IMPLICATIONS FOR PRACTICE: the BSS and the BSS-R provide a robust tool to quantify women's experiences of childbirth between variables such as birth types, birth settings and providers. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Associations between maternal periconceptional exposure to secondhand tobacco smoke and major birth defects.

    Science.gov (United States)

    Hoyt, Adrienne T; Canfield, Mark A; Romitti, Paul A; Botto, Lorenzo D; Anderka, Marlene T; Krikov, Sergey V; Tarpey, Morgan K; Feldkamp, Marcia L

    2016-11-01

    While associations between secondhand smoke and a few birth defects (namely, oral clefts and neural tube defects) have been noted in the scientific literature, to our knowledge, there is no single or comprehensive source of population-based information on its associations with a range of birth defects among nonsmoking mothers. We utilized data from the National Birth Defects Prevention Study, a large population-based multisite case-control study, to examine associations between maternal reports of periconceptional exposure to secondhand smoke in the household or workplace/school and major birth defects. The multisite National Birth Defects Prevention Study is the largest case-control study of birth defects to date in the United States. We selected cases from birth defect groups having >100 total cases, as well as all nonmalformed controls (10,200), from delivery years 1997 through 2009; 44 birth defects were examined. After excluding cases and controls from multiple births and whose mothers reported active smoking or pregestational diabetes, we analyzed data on periconceptional secondhand smoke exposure-encompassing the period 1 month prior to conception through the first trimester. For the birth defect craniosynostosis, we additionally examined the effect of exposure in the second and third trimesters as well due to the potential sensitivity to teratogens for this defect throughout pregnancy. Covariates included in all final models of birth defects with ≥5 exposed mothers were study site, previous live births, time between estimated date of delivery and interview date, maternal age at estimated date of delivery, race/ethnicity, education, body mass index, nativity, household income divided by number of people supported by this income, periconceptional alcohol consumption, and folic acid supplementation. For each birth defect examined, we used logistic regression analyses to estimate both crude and adjusted odds ratios and 95% confidence intervals for both

  14. Creative-Dynamics Approach To Neural Intelligence

    Science.gov (United States)

    Zak, Michail A.

    1992-01-01

    Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

  15. A neural network approach to the study of internal energy flow in molecular systems

    International Nuclear Information System (INIS)

    Sumpter, B.G.; Getino, C.; Noid, D.W.

    1992-01-01

    Neural networks are used to develop a new technique for efficient analysis of data obtained from molecular-dynamics calculations and is applied to the study of mode energy flow in molecular systems. The methodology is based on teaching an appropriate neural network the relationship between phase-space points along a classical trajectory and mode energies for stretch, bend, and torsion vibrations. Results are discussed for reactive and nonreactive classical trajectories of hydrogen peroxide (H 2 O 2 ) on a semiempirical potential-energy surface. The neural-network approach is shown to produce reasonably accurate values for the mode energies, with average errors between 1% and 12%, and is applicable to any region within the 24-dimensional phase space of H 2 O 2 . In addition, the generic knowledge learned by the neural network allows calculations to be made for other molecular systems. Results are discussed for a series of tetratomic molecules: H 2 X 2 , X=C, N, O, Si, S, or Se, and preliminary results are given for energy flow predictions in macromolecules

  16. Attendance at prenatal care and adverse birth outcomes in China: A follow-up study based on Maternal and Newborn's Health Monitoring System.

    Science.gov (United States)

    Huang, Aiqun; Wu, Keye; Zhao, Wei; Hu, Huanqing; Yang, Qi; Chen, Dafang

    2018-02-01

    to evaluate the independent association between attendance at prenatal care and adverse birth outcomes in China, measured either as the occurrence of preterm birth or low birth weight. a follow-up study. the data was collected from maternal and newborn's health monitoring system at 6 provinces in China. all pregnant women registered in the system at their first prenatal care visit. We included 40152 registered pregnant women who had delivered between October 2013 and September 2014. attendance at prenatal care was evaluated using Kessner index. χ 2 tests were used to examine the correlations between demographic characteristics and preterm birth or low birth weight. The associations between attendance at prenatal care and birth outcomes were explored using multilevel mixed-effects logistic regression models. the prevalence for preterm birth and low birth weight was 3.31% and 2.55%. The null models showed region clustering on birth outcomes. Compared with women who received adequate prenatal care, those with intermediate prenatal care (adjusted OR 1.62, 95%CI 1.37-1.92) or inadequate prenatal care (adjusted OR 2.78, 95%CI 2.24-3.44) had significantly increased risks for preterm birth, and women with intermediate prenatal care (adjusted OR 1.31, 95%CI 1.10-1.55) or inadequate prenatal care (adjusted OR 1.70, 95%CI 1.32-2.19) had significantly increased risks for low birth weight. We found very significant dose-response patterns for both preterm birth (p-trendprenatal care in China has independent effects on both preterm birth and low birth weight. Appropriate timing and number of prenatal care visits can help to reduce the occurrence of preterm birth or low birth weight. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Olfactory systems and neural circuits that modulate predator odor fear

    Directory of Open Access Journals (Sweden)

    Lorey K. Takahashi

    2014-03-01

    Full Text Available When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS and accessory olfactory systems (AOS detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray, paraventricular nucleus of the hypothalamus, and the medial amygdala appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal stress hormone secretion. The medial amygdala also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus appear prominently involve in predator odor fear behavior. The basolateral amygdala, medial hypothalamic nuclei, and medial prefrontal cortex are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate

  18. Olfactory systems and neural circuits that modulate predator odor fear

    Science.gov (United States)

    Takahashi, Lorey K.

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  19. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  20. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  1. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    International Nuclear Information System (INIS)

    Wang, L; Zhang, Y Y; Ding, L

    2006-01-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module

  2. Assessing bottled water nitrate concentrations to evaluate total drinking water nitrate exposure and risk of birth defects.

    Science.gov (United States)

    Weyer, Peter J; Brender, Jean D; Romitti, Paul A; Kantamneni, Jiji R; Crawford, David; Sharkey, Joseph R; Shinde, Mayura; Horel, Scott A; Vuong, Ann M; Langlois, Peter H

    2014-12-01

    Previous epidemiologic studies of maternal exposure to drinking water nitrate did not account for bottled water consumption. The objective of this National Birth Defects Prevention Study (NBDPS) (USA) analysis was to assess the impact of bottled water use on the relation between maternal exposure to drinking water nitrate and selected birth defects in infants born during 1997-2005. Prenatal residences of 1,410 mothers reporting exclusive bottled water use were geocoded and mapped; 326 bottled water samples were collected and analyzed using Environmental Protection Agency Method 300.0. Median bottled water nitrate concentrations were assigned by community; mothers' overall intake of nitrate in mg/day from drinking water was calculated. Odds ratios for neural tube defects, limb deficiencies, oral cleft defects, and heart defects were estimated using mixed-effects models for logistic regression. Odds ratios (95% CIs) for the highest exposure group in offspring of mothers reporting exclusive use of bottled water were: neural tube defects [1.42 (0.51, 3.99)], limb deficiencies [1.86 (0.51, 6.80)], oral clefts [1.43 (0.61, 3.31)], and heart defects [2.13, (0.87, 5.17)]. Bottled water nitrate had no appreciable impact on risk for birth defects in the NBDPS.

  3. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

    A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

  4. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

  5. Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Świłło S.J.

    2013-12-01

    Full Text Available The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

  6. Birth tourism: socio-demographic and statistical aspects

    Directory of Open Access Journals (Sweden)

    Anatoly V. Korotkov

    2016-01-01

    Full Text Available The purpose of the study is to research birth tourism issue. The article gives the socio-demographic and statistical aspects of research problems of birth inbound tourism in the Russian Federation. Following the literature analysis, the degree of study for birth tourism lags behind its actual size. Currently, the media has accumulated a significant amount of information on birth tourism in Russia, that requires processing, systematization and understanding that can and should become an independent area of study of sociologists and demographers to develop recommendations for the management of socio-demographic processes in birth tourism in our country. It is necessary to identify the problems that will inevitably arise. At present, this process is almost not regulated.These problems are complex, it requires the joint efforts of sociologists and demographers. However, it is impossible to obtain reliable results and to develop management decisions without attention to the statistical aspect of this problem. It is necessary to create methodological support for collecting and information processing and model development of the birth tourism. At the initial stage it is necessary to identify the direction and objectives of the analysis to determine the factors in the development of this process, to develop a hierarchical system of statistical indicators, to receive the information, needed for calculating of specific indicators.The complex research of the birth tourism issues should be based on the methodology of sociology, demography and statistics, including statistical observation, interviews with residents, structure analysis and birth tourism concentration in the country, the analysis of the dynamics, classification of factors and reasons, the grouping of regions for the development of the studied processes and, of course, the development of economic-statistical indicators.The article reveals the problem of the significant influence of the

  7. The BirthPlace collaborative practice model: results from the San Diego Birth Center Study.

    Science.gov (United States)

    Swartz; Jackson; Lang; Ecker; Ganiats; Dickinson; Nguyen

    1998-07-01

    Objective: The search for quality, cost-effective health care programs in the United States is now a major focus in the era of health care reform. New programs need to be evaluated as alternatives are developed in the health care system. The BirthPlace program provides comprehensive perinatal services with certified nurse-midwives and obstetricians working together in an integrated collaborative practice serving a primarily low-income population. Low-risk women are delivered by nurse-midwives in a freestanding birth center (The BirthPlace), which is one component of a larger integrated health network. All others are delivered by team obstetricians at the affiliated tertiary hospital. Wellness, preventive measures, early intervention, and family involvement are emphasized. The San Diego Birth Center Study is a 4-year research project funded by the U.S. Federal Agency for Health Care Policy and Research (#R01-HS07161) to evaluate this program. The National Birth Center Study (NEJM, 1989; 321(26): 1801-11) described the advantages and safety of freestanding birth centers. However, a prospective cohort study with a concurrent comparison group of comparable risk had not been conducted on a collaborative practice-freestanding birth center model to address questions of safety, cost, and patient satisfaction.Methods: The specific aims of this study are to compare this collaborative practice model to the traditional model of perinatal health care (physician providers and hospital delivery). A prospective cohort study comparing these two health care models was conducted with a final expected sample size of approximately 2,000 birth center and 1,350 traditional care subjects. Women were recruited from both the birth center and traditional care programs (private physicians offices and hospital based clinics) at the beginning of prenatal care and followed through the end of the perinatal period. Prenatal, intrapartum, postpartum and infant morbidity and mortality are being

  8. Memristor-based neural networks

    International Nuclear Information System (INIS)

    Thomas, Andy

    2013-01-01

    The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

  9. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  10. Construction of HMI Network System for Individualized Maternity Intervention Service against Birth Defects in Community

    Institute of Scientific and Technical Information of China (English)

    Xu-huai HU

    2007-01-01

    The paper expounds the community maternity service system against birth defects,from the viewpoint of individualized service in family planning. We have utilized modern information technology to develop health management information (HMI) network with individualized maternity, and to establish the community service system for intervention of birth defects. The service system applied the concept of modern health management information to implementing informational management for screening,treatment, following up, outcome monitoring, so as to provide a base for promotion of health, diagnosis, treatment as well as scientific research, with the prenatal screening of Down's syndrome as a model. The introduction to informational network during the processes of service has been carried out with regards to its composition, function and application, while introducing the effects of computerized case record individualized in prevention, management and research of Down's syndrome.

  11. CloudScan - A Configuration-Free Invoice Analysis System Using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Palm, Rasmus Berg; Winther, Ole; Laws, Florian

    2017-01-01

    We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts....... The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline...... logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts...

  12. Theory of Neural Information Processing Systems

    International Nuclear Information System (INIS)

    Galla, Tobias

    2006-01-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10 11 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  13. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Delayed development of neural language organization in very preterm born children.

    Science.gov (United States)

    Mürner-Lavanchy, Ines; Steinlin, Maja; Kiefer, Claus; Weisstanner, Christian; Ritter, Barbara Catherine; Perrig, Walter; Everts, Regula

    2014-01-01

    This study investigates neural language organization in very preterm born children compared to control children and examines the relationship between language organization, age, and language performance. Fifty-six preterms and 38 controls (7-12 y) completed a functional magnetic resonance imaging language task. Lateralization and signal change were computed for language-relevant brain regions. Younger preterms showed a bilateral language network whereas older preterms revealed left-sided language organization. No age-related differences in language organization were observed in controls. Results indicate that preterms maintain atypical bilateral language organization longer than term born controls. This might reflect a delay of neural language organization due to very premature birth.

  15. Multi-dimensional design window search system using neural networks in reactor core design

    International Nuclear Information System (INIS)

    Kugo, Teruhiko; Nakagawa, Masayuki

    2000-02-01

    In the reactor core design, many parametric survey calculations should be carried out to decide an optimal set of basic design parameter values. They consume a large amount of computation time and labor in the conventional way. To support directly design work, we investigate a procedure to search efficiently a design window, which is defined as feasible design parameter ranges satisfying design criteria and requirements, in a multi-dimensional space composed of several basic design parameters. We apply the present method to the neutronics and thermal hydraulics fields and develop the multi-dimensional design window search system using it. The principle of the present method is to construct the multilayer neural network to simulate quickly a response of an analysis code through a training process, and to reduce computation time using the neural network without parametric study using analysis codes. The system works on an engineering workstation (EWS) with efficient man-machine interface for pre- and post-processing. This report describes the principle of the present method, the structure of the system, the guidance of the usages of the system, the guideline for the efficient training of neural networks, the instructions of the input data for analysis calculation and so on. (author)

  16. Apoptosis in neural crest cells by functional loss of APC tumor suppressor gene

    Science.gov (United States)

    Hasegawa, Sumitaka; Sato, Tomoyuki; Akazawa, Hiroshi; Okada, Hitoshi; Maeno, Akiteru; Ito, Masaki; Sugitani, Yoshinobu; Shibata, Hiroyuki; Miyazaki, Jun-ichi; Katsuki, Motoya; Yamauchi, Yasutaka; Yamamura, Ken-ichi; Katamine, Shigeru; Noda, Tetsuo

    2002-01-01

    Apc is a gene associated with familial adenomatous polyposis coli (FAP) and its inactivation is a critical step in colorectal tumor formation. The protein product, adenomatous polyposis coli (APC), acts to down-regulate intracellular levels of β-catenin, a key signal transducer in the Wnt signaling. Conditional targeting of Apc in the neural crest of mice caused massive apoptosis of cephalic and cardiac neural crest cells at about 11.5 days post coitum, resulting in craniofacial and cardiac anomalies at birth. Notably, the apoptotic cells localized in the regions where β-catenin had accumulated. In contrast to its role in colorectal epithelial cells, inactivation of APC leads to dysregulation of β-catenin/Wnt signaling with resultant apoptosis in certain tissues including neural crest cells. PMID:11756652

  17. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  18. Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems

    International Nuclear Information System (INIS)

    Souza, Rose Mary G.P.; Moreira, Joao M.L.

    2006-01-01

    This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the

  19. Birth of Identity: Understanding the Value and Policy Considerations of Using Birth Certificates for Identity Resolution

    Science.gov (United States)

    Duncan, Jeffrey Dean

    2015-01-01

    Exchanging patient-specific information across heterogeneous information systems is a critical but increasingly complex and expensive challenge. Lacking a universal unique identifier for healthcare, patient records must be linked using combinations of identity attributes such as name, date of birth, and sex. A state's birth certificate registry…

  20. Development of a hybrid system of artificial neural networks and ...

    African Journals Online (AJOL)

    Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

  1. Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

    International Nuclear Information System (INIS)

    Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

    2015-01-01

    This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

  2. Defining and describing birth centres in the Netherlands - a component study of the Dutch Birth Centre Study.

    Science.gov (United States)

    Hermus, M A A; Boesveld, I C; Hitzert, M; Franx, A; de Graaf, J P; Steegers, E A P; Wiegers, T A; van der Pal-de Bruin, K M

    2017-07-03

    During the last decade, a rapid increase of birth locations for low-risk births, other than conventional obstetric units, has been seen in the Netherlands. Internationally some of such locations are called birth centres. The varying international definitions for birth centres are not directly applicable for use within the Dutch obstetric system. A standard definition for a birth centre in the Netherlands is lacking. This study aimed to develop a definition of birth centres for use in the Netherlands, to identify these centres and to describe their characteristics. International definitions of birth centres were analysed to find common descriptions. In July 2013 the Dutch Birth Centre Questionnaire was sent to 46 selected Dutch birth locations that might qualify as birth centre. Questions included: location, reason for establishment, women served, philosophies, facilities that support physiological birth, hotel-facilities, management, environment and transfer procedures in case of referral. Birth centres were visited to confirm the findings from the Dutch Birth Centre Questionnaire and to measure distance and time in case of referral to obstetric care. From all 46 birth locations the questionnaires were received. Based on this information a Dutch definition of a birth centre was constructed. This definition reads: "Birth centres are midwifery-managed locations that offer care to low risk women during labour and birth. They have a homelike environment and provide facilities to support physiological birth. Community midwives take primary professional responsibility for care. In case of referral the obstetric caregiver takes over the professional responsibility of care." Of the 46 selected birth locations 23 fulfilled this definition. Three types of birth centres were distinguished based on their location in relation to the nearest obstetric unit: freestanding (n = 3), alongside (n = 14) and on-site (n = 6). Transfer in case of referral was necessary for all

  3. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  4. A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

    OpenAIRE

    Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

    2016-01-01

    A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

  5. A low-cost multichannel wireless neural stimulation system for freely roaming animals

    Science.gov (United States)

    Alam, Monzurul; Chen, Xi; Fernandez, Eduardo

    2013-12-01

    Objectives. Electrical stimulation of nerve tissue and recording of neural activity are the basis of many therapies and neural prostheses. Conventional stimulation systems have a number of practical limitations, especially in experiments involving freely roaming subjects. Our main objective was to develop a modular, versatile and inexpensive multichannel wireless system able to overcome some of these constraints. Approach. We have designed and implemented a new multichannel wireless neural stimulator based on commercial components. The system is small (2 cm × 4 cm × 0.5 cm) and light in weight (9 g) which allows it to be easily carried in a small backpack. To test and validate the performance and reliability of the whole system we conducted several bench tests and in vivo experiments. Main results. The performance and accuracy of the stimulator were comparable to commercial threaded systems. Stimulation sequences can be constructed on-the-fly with 251 selectable current levels (from 0 to 250 µA) with 1 µA step resolution. The pulse widths and intervals can be as long as 65 ms in 2 µs time resolution. The system covers approximately 10 m of transmission range in a regular laboratory environment and 100 m in free space (line of sight). Furthermore it provides great flexibility for experiments since it allows full control of the stimulator and the stimulation parameters in real time. When there is no stimulation, the device automatically goes into low-power sleep mode to preserve battery power. Significance. We introduce the design of a powerful multichannel wireless stimulator assembled from commercial components. Key features of the system are their reliability, robustness and small size. The system has a flexible design that can be modified straightforwardly to tailor it to any specific experimental need. Furthermore it can be effortlessly adapted for use with any kind of multielectrode arrays.

  6. Can the UK’s birth registration system better serve the interests of those born following collaborative assisted reproduction?

    Directory of Open Access Journals (Sweden)

    Marilyn A Crawshaw

    2017-06-01

    Full Text Available Current birth registration systems fail to serve adequately the interests of those born as a result of gamete and embryo donation and surrogacy. In the UK, changes to the birth registration system have been piecemeal, reactive and situation-specific and no information is recorded about gamete donors. Birth registration has thereby become a statement of legal parentage and citizenship only, without debate as to whether it should serve any wider functions. This sits uneasily with the increasingly accepted human right to know one’s genetic and gestational as well as legal parents, and the duty of the State to facilitate that right. This commentary sets out one possible model for reform to better ensure that those affected become aware of, and/or have access to, knowledge about their origins and that such information is stored and released effectively without compromising individual privacy. Among other features, our proposal links the birth registration system and the information stored in the Human Fertilization and Embryology Authority’s Register of Information, although further work than we have been able to undertake here is necessary to ensure a better fit where cross-border treatment services or informal arrangements have been involved. The time for debate and reform is well overdue.

  7. General birth-death processes: probabilities, inference, and applications

    OpenAIRE

    Crawford, Forrest Wrenn

    2012-01-01

    A birth-death process is a continuous-time Markov chain that counts the number of particles in a system over time. Each particle can give birth to another particle or die, and the rate of births and deaths at any given time depends on how many extant particles there are. Birth-death processes are popular modeling tools in evolution, population biology, genetics, epidemiology, and ecology. Despite the widespread interest in birth-death models, no efficient method exists to evaluate the fini...

  8. Cesarean Birth

    Science.gov (United States)

    ... QUESTIONS LABOR, DELIVERY, AND POSTPARTUM CARE FAQ006 Cesarean Birth (C-section) • What is cesarean birth? • What are the reasons for cesarean birth? • Is a cesarean birth necessary if I have ...

  9. Hybrid case-neural network (CNN) diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

  10. The use of neural networks in the D0 data acquisition system

    International Nuclear Information System (INIS)

    Cutts, D.; Hoftun, J.S.; Sornborger, A.; Astur, R.V.; Johnson, C.R.; Zeller, R.T.

    1989-01-01

    We discuss the possible application of algorithms derived from neural networks to the D0 experiment. The D0 data acquisition system is based on a large farm of MicroVAXes, each independently performing real-time event filtering. A new generation of multiport memories in each MicroVAX node will enable special function processors to have direct access to event data. We describe an exploratory study of back propagation neural networks, such as might be configured in the nodes, for more efficient event filtering. 9 refs., 3 figs., 1 tab

  11. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  12. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  13. Baseline Prevalence of Birth Defects Associated with Congenital Zika Virus Infection - Massachusetts, North Carolina, and Atlanta, Georgia, 2013-2014.

    Science.gov (United States)

    Cragan, Janet D; Mai, Cara T; Petersen, Emily E; Liberman, Rebecca F; Forestieri, Nina E; Stevens, Alissa C; Delaney, Augustina; Dawson, April L; Ellington, Sascha R; Shapiro-Mendoza, Carrie K; Dunn, Julie E; Higgins, Cathleen A; Meyer, Robert E; Williams, Tonya; Polen, Kara N D; Newsome, Kim; Reynolds, Megan; Isenburg, Jennifer; Gilboa, Suzanne M; Meaney-Delman, Dana M; Moore, Cynthia A; Boyle, Coleen A; Honein, Margaret A

    2017-03-03

    Zika virus infection during pregnancy can cause serious brain abnormalities, but the full range of adverse outcomes is unknown (1). To better understand the impact of birth defects resulting from Zika virus infection, the CDC surveillance case definition established in 2016 for birth defects potentially related to Zika virus infection* (2) was retrospectively applied to population-based birth defects surveillance data collected during 2013-2014 in three areas before the introduction of Zika virus (the pre-Zika years) into the World Health Organization's Region of the Americas (Americas) (3). These data, from Massachusetts (2013), North Carolina (2013), and Atlanta, Georgia (2013-2014), included 747 infants and fetuses with one or more of the birth defects meeting the case definition (pre-Zika prevalence = 2.86 per 1,000 live births). Brain abnormalities or microcephaly were the most frequently recorded (1.50 per 1,000), followed by neural tube defects and other early brain malformations † (0.88), eye abnormalities without mention of a brain abnormality (0.31), and other consequences of central nervous system (CNS) dysfunction without mention of brain or eye abnormalities (0.17). During January 15-September 22, 2016, the U.S. Zika Pregnancy Registry (USZPR) reported 26 infants and fetuses with these same defects among 442 completed pregnancies (58.8 per 1,000) born to mothers with laboratory evidence of possible Zika virus infection during pregnancy (2). Although the ascertainment methods differed, this finding was approximately 20 times higher than the proportion of one or more of the same birth defects among pregnancies during the pre-Zika years. These data demonstrate the importance of population-based surveillance for interpreting data about birth defects potentially related to Zika virus infection.

  14. Seasonality of birth in children with central nervous system tumours in Denmark, 1970-2003

    DEFF Research Database (Denmark)

    Schmidt, L S; Grell, Kathrine; Frederiksen, K

    2008-01-01

    We investigated possible seasonal variation of births among children central nervous system tumour in Denmark (N=1640), comparing them with 2,582,714 children born between 1970 and 2003. No such variation was seen overall, but ependymoma showed seasonal variation....

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

    Science.gov (United States)

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

    2015-01-01

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

  16. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2017-01-01

    Full Text Available In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

  18. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  19. Study on algorithm of process neural network for soft sensing in sewage disposal system

    Science.gov (United States)

    Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

    2006-11-01

    A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

  20. The multisensory approach to birth and aromatherapy.

    Science.gov (United States)

    Gutteridge, Kathryn

    2014-05-01

    The birth environment continues to be a subject of midwifery discourse within theory and practice. This article discusses the birth environment from the perspective of understanding the aromas and aromatherapy for the benefit of women and midwives The dynamic between the olfactory system and stimulation of normal birth processes proves to be fascinating. By examining other health models of care we can incorporate simple but powerful methods that can shape clinical outcomes. There is still more that midwives can do by using aromatherapy in the context of a multisensory approach to make birth environments synchronise with women's potential to birth in a positive way.

  1. The neural system of metacognition accompanying decision-making in the prefrontal cortex

    Science.gov (United States)

    Qiu, Lirong; Su, Jie; Ni, Yinmei; Bai, Yang; Zhang, Xuesong; Li, Xiaoli

    2018-01-01

    Decision-making is usually accompanied by metacognition, through which a decision maker monitors uncertainty regarding a decision and may then consequently revise the decision. These metacognitive processes can occur prior to or in the absence of feedback. However, the neural mechanisms of metacognition remain controversial. One theory proposes an independent neural system for metacognition in the prefrontal cortex (PFC); the other, that metacognitive processes coincide and overlap with the systems used for the decision-making process per se. In this study, we devised a novel “decision–redecision” paradigm to investigate the neural metacognitive processes involved in redecision as compared to the initial decision-making process. The participants underwent a perceptual decision-making task and a rule-based decision-making task during functional magnetic resonance imaging (fMRI). We found that the anterior PFC, including the dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were more extensively activated after the initial decision. The dACC activity in redecision positively scaled with decision uncertainty and correlated with individual metacognitive uncertainty monitoring abilities—commonly occurring in both tasks—indicating that the dACC was specifically involved in decision uncertainty monitoring. In contrast, the lFPC activity seen in redecision processing was scaled with decision uncertainty reduction and correlated with individual accuracy changes—positively in the rule-based decision-making task and negatively in the perceptual decision-making task. Our results show that the lFPC was specifically involved in metacognitive control of decision adjustment and was subject to different control demands of the tasks. Therefore, our findings support that a separate neural system in the PFC is essentially involved in metacognition and further, that functions of the PFC in metacognition are dissociable. PMID:29684004

  2. The equilibrium of neural firing: A mathematical theory

    Energy Technology Data Exchange (ETDEWEB)

    Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

    2014-12-15

    Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

  3. Neural Computations in a Dynamical System with Multiple Time Scales

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  4. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    Huang, Darong; Bai, Xing-Rong

    Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

  5. Examination of neural systems sub-serving facebook "addiction".

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine

    2014-12-01

    Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.

  6. The Danish Medical Birth Register

    DEFF Research Database (Denmark)

    Bliddal, Mette; Broe, Anne; Pottegård, Anton

    2018-01-01

    The Danish Medical Birth Register was established in 1973. It is a key component of the Danish health information system. The register enables monitoring of the health of pregnant women and their offspring, it provides data for quality assessment of the perinatal care in Denmark, and it is used...... on all births in Denmark and comprises primarily of data from the Danish National Patient Registry supplemented with forms on home deliveries and stillbirths. It contains information on maternal age provided by the Civil Registration System. Information on pre-pregnancy body mass index and smoking...

  7. Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Seyed Rohollah Hosseini Vaez

    2017-12-01

    Full Text Available In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM FRP rods is emerging as a promising technology for increasing flexural and shear strength of deficient concrete, masonry and timber members. An artificial neural network is an information processing tool that is inspired by the way biological nervous systems (such as the brain process the information. The key element of this tool is the novel structure of the information processing system. In engineering applications, a neural network can be a vector mapper which maps an input vector to an output one. In the present study, a new approach is developed to predict the behavior of strengthened concrete beam using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as elastic modulus of the FRP reinforcement, the ratio of the steel longitudinal reinforcement, dimensions of the beam section, the ratio of the NSM-FRP reinforcement and characteristics of concrete, the output node was the flexural strength of beams. The idealized neural network was employed to generate empirical charts and equations to be used in design. The aim of this study is to investigate the behavior of strengthened RC beam using artificial neural networks.

  8. Birth Defects

    Science.gov (United States)

    A birth defect is a problem that happens while a baby is developing in the mother's body. Most birth defects happen during the first 3 months of ... in the United States is born with a birth defect. A birth defect may affect how the ...

  9. Possible association of first and high birth order of pregnant women with the risk of isolated congenital abnormalities in Hungary - a population-based case-matched control study.

    Science.gov (United States)

    Csermely, Gyula; Susánszky, Éva; Czeizel, Andrew E; Veszprémi, Béla

    2014-08-01

    In epidemiological studies at the estimation of risk factors in the origin of specified congenital abnormalities in general birth order (parity) is considered as confounder. The aim of this study was to analyze the possible association of first and high (four or more) birth order with the risk of congenital abnormalities in a population-based case-matched control data set. The large dataset of the Hungarian Case-Control Surveillance of Congenital Abnormalities included 21,494 cases with different isolated congenital abnormality and their 34,311 matched controls. First the distribution of birth order was compared of 24 congenital abnormality groups and their matched controls. In the second step the possible association of first and high birth order with the risk of congenital abnormalities was estimated. Finally some subgroups of neural-tube defects, congenital heart defects and abdominal wall's defects were evaluated separately. A higher risk of spina bifida aperta/cystica, esophageal atresia/stenosis and clubfoot was observed in the offspring of primiparous mothers. Of 24 congenital abnormality groups, 14 had mothers with larger proportion of high birth order. Ear defects, congenital heart defects, cleft lip± palate and obstructive defects of urinary tract had a linear trend from a lower proportion of first born cases to the larger proportion of high birth order. Birth order showed U-shaped distribution of neural-tube defects and clubfoot, i.e. both first and high birth order had a larger proportion in cases than in their matched controls. Birth order is a contributing factor in the origin of some isolated congenital abnormalities. The higher risk of certain congenital abnormalities in pregnant women with first or high birth order is worth considering in the clinical practice, e.g. ultrasound scanning. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.

    Science.gov (United States)

    Michaels, Jonathan A; Dann, Benjamin; Scherberger, Hansjörg

    2016-11-01

    Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

  11. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1991-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab

  12. New Solutions to the Firing Squad Synchronization Problems for Neural and Hyperdag P Systems

    Directory of Open Access Journals (Sweden)

    Michael J. Dinneen

    2009-11-01

    Full Text Available We propose two uniform solutions to an open question: the Firing Squad Synchronization Problem (FSSP, for hyperdag and symmetric neural P systems, with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively, where e_c is the eccentricity of the commander cell of the dag or digraph underlying these P systems. The first and fast solution is based on a novel proposal, which dynamically extends P systems with mobile channels. The second solution is substantially longer, but is solely based on classical rules and static channels. In contrast to the previous solutions, which work for tree-based P systems, our solutions synchronize to any subset of the underlying digraph; and do not require membrane polarizations or conditional rules, but require states, as typically used in hyperdag and neural P systems.

  13. Neural network based expert system for fault diagnosis of particle accelerators

    International Nuclear Information System (INIS)

    Dewidar, M.M.

    1997-01-01

    Particle accelerators are generators that produce beams of charged particles, acquiring different energies, depending on the accelerator type. The MGC-20 cyclotron is a cyclic particle accelerator used for accelerating protons, deuterons, alpha particles, and helium-3 to different energies. Its applications include isotope production, nuclear reaction, and mass spectroscopy studies. It is a complicated machine, it consists of five main parts, the ion source, the deflector, the beam transport system, the concentric and harmonic coils, and the radio frequency system. The diagnosis of this device is a very complex task. it depends on the conditions of 27 indicators of the control panel of the device. The accurate diagnosis can lead to a high system reliability and save maintenance costs. so an expert system for the cyclotron fault diagnosis is necessary to be built. In this thesis , a hybrid expert system was developed for the fault diagnosis of the MGC-20 cyclotron. Two intelligent techniques, multilayer feed forward back propagation neural network and the rule based expert system, are integrated as a pre-processor loosely coupled model to build the proposed hybrid expert system. The architecture of the developed hybrid expert system consists of two levels. The first level is two feed forward back propagation neural networks, used for isolating the faulty part of the cyclotron. The second level is the rule based expert system, used for troubleshooting the faults inside the isolated faulty part. 4-6 tabs., 4-5 figs., 36 refs

  14. Combining neural networks and signed particles to simulate quantum systems more efficiently

    Science.gov (United States)

    Sellier, Jean Michel

    2018-04-01

    Recently a new formulation of quantum mechanics has been suggested which describes systems by means of ensembles of classical particles provided with a sign. This novel approach mainly consists of two steps: the computation of the Wigner kernel, a multi-dimensional function describing the effects of the potential over the system, and the field-less evolution of the particles which eventually create new signed particles in the process. Although this method has proved to be extremely advantageous in terms of computational resources - as a matter of fact it is able to simulate in a time-dependent fashion many-body systems on relatively small machines - the Wigner kernel can represent the bottleneck of simulations of certain systems. Moreover, storing the kernel can be another issue as the amount of memory needed is cursed by the dimensionality of the system. In this work, we introduce a new technique which drastically reduces the computation time and memory requirement to simulate time-dependent quantum systems which is based on the use of an appropriately tailored neural network combined with the signed particle formalism. In particular, the suggested neural network is able to compute efficiently and reliably the Wigner kernel without any training as its entire set of weights and biases is specified by analytical formulas. As a consequence, the amount of memory for quantum simulations radically drops since the kernel does not need to be stored anymore as it is now computed by the neural network itself, only on the cells of the (discretized) phase-space which are occupied by particles. As its is clearly shown in the final part of this paper, not only this novel approach drastically reduces the computational time, it also remains accurate. The author believes this work opens the way towards effective design of quantum devices, with incredible practical implications.

  15. BIRTH ORDER AND ANDROPHILIC MALE-TO-FEMALE TRANSSEXUALISM IN BRAZIL.

    Science.gov (United States)

    Vanderlaan, Doug P; Blanchard, Ray; Zucker, Kenneth J; Massuda, Raffael; Fontanari, Anna Martha Vaitses; Borba, André Oliveira; Costa, Angelo Bradelli; Schneider, Maiko Abel; Mueller, Andressa; Soll, Bianca Machado Borba; Schwarz, Karine; Da Silva, Dhiordan Cardoso; Lobato, Maria Inês Rodrigues

    2017-07-01

    Previous research has indicated that biological older brothers increase the odds of androphilia in males. This finding has been termed the fraternal birth order effect. The maternal immune hypothesis suggests that this effect reflects the progressive immunization of some mothers to male-specific antigens involved in fetal male brain masculinization. Exposure to these antigens, as a result of carrying earlier-born sons, is hypothesized to produce maternal immune responses towards later-born sons, thus leading to female-typical neural development of brain regions underlying sexual orientation. Because this hypothesis posits mechanisms that have the potential to be active in any situation where a mother gestates repeated male fetuses, a key prediction is that the fraternal birth order effect should be observable in diverse populations. The present study assessed the association between sexual orientation and birth order in androphilic male-to-female transsexuals in Brazil, a previously unexamined population. Male-to-female transsexuals who reported attraction to males were recruited from a specialty gender identity service in southern Brazil (n=118) and a comparison group of gynephilic non-transsexual men (n=143) was recruited at the same hospital. Logistic regression showed that the transsexual group had significantly more older brothers and other siblings. These effects were independent of one another and consistent with previous studies of birth order and male sexual orientation. The presence of the fraternal birth order effect in the present sample provides further evidence of the ubiquity of this effect and, therefore, lends support to the maternal immune hypothesis as an explanation of androphilic sexual orientation in some male-to-female transsexuals.

  16. Neurophysiology and neural engineering: a review.

    Science.gov (United States)

    Prochazka, Arthur

    2017-08-01

    Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

  17. Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

    Science.gov (United States)

    Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

    2017-08-29

    This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

  18. Cost-effectiveness of planned birth in a birth centre compared with alternative planned places of birth: Results of the Dutch Birth Centre study

    NARCIS (Netherlands)

    M.F. Hitzert (Marit); M.A.A. Hermus (Marieke A.A.); Boesveld, I.I.C. (Inge I.C.); A. Franx (Arie); K.M. van der Pal-De Bruin (Karin); E.A.P. Steegers (Eric); Van Den Akker-Van Marle, E.M.E. (Eiske M.E.)

    2017-01-01

    textabstractObjectives To estimate the cost-effectiveness of a planned birth in a birth centre compared with alternative planned places of birth for low-risk women. In addition, a distinction has been made between different types of locations and integration profiles of birth centres. Design

  19. Folic acid supplementation influences the distribution of neural tube defect subtypes : A registry-based study

    NARCIS (Netherlands)

    Bergman, J. E. H.; Otten, E.; Verheij, J. B. G. M.; de Walle, H. E. K.

    Periconceptional folic acid (FA) reduces neural tube defect (NTD) risk, but seems to have a varying effect per NTD subtype. We aimed to study the effect of FA supplementation on NTD subtype distribution using data from EUROCAT Northern Netherlands. We included all birth types with non-syndromal NTDs

  20. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

  1. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

    2015-02-01

    This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

  2. Quasi-Birth-Death Processes, Tree-Like QBDs, Probabilistic 1-Counter Automata, and Pushdown Systems

    NARCIS (Netherlands)

    K. Etessami (Kousha); D.K. Wojtczak (Dominik); M. Yannakakis

    2010-01-01

    textabstractWe begin by observing that (discrete-time) Quasi-Birth-Death Processes (QBDs) are equivalent, in a precise sense, to probabilistic 1-Counter Automata (p1CAs), and both Tree-Like QBDs (TL-QBDs) and Tree-Structured QBDs (TS-QBDs) are equivalent to both probabilistic Pushdown Systems

  3. Committee Opinion No. 697: Planned Home Birth.

    Science.gov (United States)

    2017-04-01

    In the United States, approximately 35,000 births (0.9%) per year occur in the home. Approximately one fourth of these births are unplanned or unattended. Although the American College of Obstetricians and Gynecologists believes that hospitals and accredited birth centers are the safest settings for birth, each woman has the right to make a medically informed decision about delivery. Importantly, women should be informed that several factors are critical to reducing perinatal mortality rates and achieving favorable home birth outcomes. These factors include the appropriate selection of candidates for home birth; the availability of a certified nurse-midwife, certified midwife or midwife whose education and licensure meet International Confederation of Midwives' Global Standards for Midwifery Education, or physician practicing obstetrics within an integrated and regulated health system; ready access to consultation; and access to safe and timely transport to nearby hospitals. The Committee on Obstetric Practice considers fetal malpresentation, multiple gestation, or prior cesarean delivery to be an absolute contraindication to planned home birth.

  4. Committee Opinion No. 669: Planned Home Birth.

    Science.gov (United States)

    2016-08-01

    In the United States, approximately 35,000 births (0.9%) per year occur in the home. Approximately one fourth of these births are unplanned or unattended. Although the American College of Obstetricians and Gynecologists believes that hospitals and accredited birth centers are the safest settings for birth, each woman has the right to make a medically informed decision about delivery. Importantly, women should be informed that several factors are critical to reducing perinatal mortality rates and achieving favorable home birth outcomes. These factors include the appropriate selection of candidates for home birth; the availability of a certified nurse-midwife, certified midwife or midwife whose education and licensure meet International Confederation of Midwives' Global Standards for Midwifery Education, or physician practicing obstetrics within an integrated and regulated health system; ready access to consultation; and access to safe and timely transport to nearby hospitals. The Committee on Obstetric Practice considers fetal malpresentation, multiple gestation, or prior cesarean delivery to be an absolute contraindication to planned home birth.

  5. Cost-effectiveness of planned birth in a birth centre compared with alternative planned places of birth: results of the Dutch Birth Centre study

    NARCIS (Netherlands)

    Hitzert, M.; Hermus, M.M.; Boesveld, I.I.; Franx, A.; Pal-de Bruin, K.K. van der; Steegers, E.E.; Akker-van Marle, E.M. van den

    2017-01-01

    Objectives To estimate the cost-effectiveness of a planned birth in a birth centre compared with alternative planned places of birth for low-risk women. In addition, a distinction has been made between different types of locations and integration profiles of birth centres. Design Economic evaluation

  6. Cost-effectiveness of planned birth in a birth centre compared with alternative planned places of birth : Results of the Dutch Birth Centre study

    NARCIS (Netherlands)

    Hitzert, Marit F.; Hermus, Marieke A. A.; Boesveld, Inge I.C.; Franx, Arie; van der Pal-de Bruin, Karin M.; Steegers, Eric A. P.; Van Den Akker-Van Marle, Eiske M.E.

    2017-01-01

    Objectives To estimate the cost-effectiveness of a planned birth in a birth centre compared with alternative planned places of birth for low-risk women. In addition, a distinction has been made between different types of locations and integration profiles of birth centres. Design Economic evaluation

  7. Neural Tube Defect in Alive Neonates: Incidence Rate and Predisposing Factors

    Directory of Open Access Journals (Sweden)

    F Haghollahi

    2008-06-01

    Full Text Available Background: Neural Tube Defect (NTD characterized by failure of neural tube to close properly be the second most common born defect after congenital heart disease. The most prevalent forms of NTD are Anencephaly and Spinal-bifida. Many factors are involved in this anomaly. New researches suggest environmental factors like radiation, hyperthermia, Vitamin A and acid folic deficiency, anti epileptic drug like Carbamazepine, Phenobarbital, phenytoin, Folic acid antagonist like Sulfasalazine, Triametherine and systemic disease like diabet mellitus, obesity, genetic factors, the most schance 40 to 70 percentages.Methods: In this survey cross sectional study was conducted in five hospitals depend to Tehran university during three years. Study subject identified through review of admission and discharge at major hospital through regular contact with newborn nurseries and birth hospital.Results: In 38473 reported cases, 143 cases have neural tube defect. Among NTD cases, 11.9% of mothers had medical diseases in their previous history such as diabetes mellitus, epilepsy-psychiatric, and disorder-heart diseases. In this study group, 5.6% have preclampsia during pregnancy period. The most common NTD anomaly in this study was anencephaly and meningomyelocele that was different from studies in literature.Conclusion: NTD result from failure of neural tube close threats fetus health up to 28 days after conception. When is often prior to the recognition of pregnancy since many pregnancy are unplanned NTD prevention is best achieve by adequate daily folic acid intake thought of reproductive ages .educational effort to promote daily intake of folic acid supplemental by women of reproductive age and NTD risk factor should be done. Early diagnostic procedure for high risk pregnancy advised.

  8. Neuroimaging of decoding and language comprehension in young very low birth weight (VLBW) adolescents: Indications for compensatory mechanisms.

    Science.gov (United States)

    van Ettinger-Veenstra, Helene; Widén, Carin; Engström, Maria; Karlsson, Thomas; Leijon, Ingemar; Nelson, Nina

    2017-01-01

    In preterm children with very low birth weight (VLBW ≤ 1500 g), reading problems are often observed. Reading comprehension is dependent on word decoding and language comprehension. We investigated neural activation-within brain regions important for reading-related to components of reading comprehension in young VLBW adolescents in direct comparison to normal birth weight (NBW) term-born peers, with the use of functional magnetic resonance imaging (fMRI). We hypothesized that the decoding mechanisms will be affected by VLBW, and expect to see increased neural activity for VLBW which may be modulated by task performance and cognitive ability. The study investigated 13 (11 included in fMRI) young adolescents (ages 12 to 14 years) born preterm with VLBW and in 13 NBW controls (ages 12-14 years) for performance on the Block Design and Vocabulary subtests of the Wechsler Intelligence Scale for Children; and for semantic, orthographic, and phonological processing during an fMRI paradigm. The VLBW group showed increased phonological activation in left inferior frontal gyrus, decreased orthographic activation in right supramarginal gyrus, and decreased semantic activation in left inferior frontal gyrus. Block Design was related to altered right-hemispheric activation, and VLBW showed lower WISC Block Design scores. Left angular gyrus showed activation increase specific for VLBW with high accuracy on the semantic test. Young VLBW adolescents showed no accuracy and reaction time performance differences on our fMRI language tasks, but they did exhibit altered neural activation during these tasks. This altered activation for VLBW was observed as increased activation during phonological decoding, and as mainly decreased activation during orthographic and semantic processing. Correlations of neural activation with accuracy on the semantic fMRI task and with decreased WISC Block Design performance were specific for the VLBW group. Together, results suggest compensatory

  9. Identification of chaotic systems by neural network with hybrid learning algorithm

    International Nuclear Information System (INIS)

    Pan, S.-T.; Lai, C.-C.

    2008-01-01

    Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods

  10. On control of Hopf bifurcation in time-delayed neural network system

    International Nuclear Information System (INIS)

    Zhou Shangbo; Liao Xiaofeng; Yu Juebang; Wong Kwokwo

    2005-01-01

    The control of Hopf bifurcations in neural network systems is studied in this Letter. The asymptotic stability theorem and the relevant corollary for linearized nonlinear dynamical systems are proven. In particular, a novel method for analyzing the local stability of a dynamical system with time-delay is suggested. For the time-delayed system consisting of one or two neurons, a washout filter based control model is proposed and analyzed. By employing the stability theorems derived, we investigate the stability of a control system and state the relevant theorems for choosing the parameters of the stabilized control system

  11. A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

    Directory of Open Access Journals (Sweden)

    Farshid Keynia

    2011-03-01

    Full Text Available Short-term load forecast (STLF is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.

  12. Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

    Science.gov (United States)

    Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

    2006-01-01

    The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

  13. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  14. Neural-network hybrid control for antilock braking systems.

    Science.gov (United States)

    Lin, Chih-Min; Hsu, C F

    2003-01-01

    The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

  15. Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

    Science.gov (United States)

    Mitani, S; Okamoto, H

    1991-05-01

    Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

  16. Waste incineration and adverse birth and neonatal outcomes: a systematic review.

    Science.gov (United States)

    Ashworth, Danielle C; Elliott, Paul; Toledano, Mireille B

    2014-08-01

    Public concern about potential health risks associated with incineration has prompted studies to investigate the relationship between incineration and risk of cancer, and more recently, birth outcomes. We conducted a systematic review of epidemiologic studies evaluating the relationship between waste incineration and the risk of adverse birth and neonatal outcomes. Literature searches were performed within the MEDLINE database, through PubMed and Ovid interfaces, for the search terms; incineration, birth, reproduction, neonatal, congenital anomalies and all related terms. Here we discuss and critically evaluate the findings of these studies. A comprehensive literature search yielded fourteen studies, encompassing a range of outcomes (including congenital anomalies, birth weight, twinning, stillbirths, sex ratio and infant death), exposure assessment methods and study designs. For congenital anomalies most studies reported no association with proximity to or emissions from waste incinerators and "all anomalies", but weak associations for neural tube and heart defects and stronger associations with facial clefts and urinary tract defects. There is limited evidence for an association between incineration and twinning and no evidence of an association with birth weight, stillbirths or sex ratio, but this may reflect the sparsity of studies exploring these outcomes. The current evidence-base is inconclusive and often limited by problems of exposure assessment, possible residual confounding, lack of statistical power with variability in study design and outcomes. However, we identified a number of higher quality studies reporting significant positive relationships with broad groups of congenital anomalies, warranting further investigation. Future studies should address the identified limitations in order to help improve our understanding of any potential adverse birth outcomes associated with incineration, particularly focussing on broad groups of anomalies, to inform

  17. Birth weight and stuttering: Evidence from three birth cohorts.

    Science.gov (United States)

    McAllister, Jan; Collier, Jacqueline

    2014-03-01

    Previous studies have produced conflicting results with regard to the association between birth weight and developmental stuttering. This study sought to determine whether birth weight was associated with childhood and/or adolescent stuttering in three British birth cohort samples. Logistic regression analyses were carried out on data from the Millenium Cohort Study (MCS), British Cohort Study (BCS70) and National Child Development Study (NCDS), whose initial cohorts comprised over 56,000 individuals. The outcome variables were parent-reported stuttering in childhood or in adolescence; the predictors, based on prior research, were birth weight, sex, multiple birth status, vocabulary score and mother's level of education. Birth weight was analysed both as a categorical variable (low birth weight, stuttering during childhood (age 3, 5 and 7 and MCS, BCS70 and NCDS, respectively) or at age 16, when developmental stuttering is likely to be persistent. None of the multivariate analyses revealed an association between birth weight and parent-reported stuttering. Sex was a significant predictor of stuttering in all the analyses, with males 1.6-3.6 times more likely than females to stutter. Our results suggest that birth weight is not a clinically useful predictor of childhood or persistent stuttering. Copyright © 2013 Elsevier Inc. All rights reserved.

  18. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  19. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  20. A red-light running prevention system based on artificial neural network and vehicle trajectory data.

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  1. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems. PMID:25435870

  2. Child Maltreatment Among Singletons and Multiple Births in Japan: A Population-Based Study.

    Science.gov (United States)

    Yokoyama, Yoshie; Oda, Terumi; Nagai, Noriyo; Sugimoto, Masako; Mizukami, Kenji

    2015-12-01

    The occurrence of multiple births has been recognized as a risk factor for child maltreatment. However, few population-based studies have examined the relationship between multiple births and child maltreatment. This study aimed to evaluate the degree of risk of child maltreatment among singletons and multiple births in Japan and to identify factors associated with increased risk. Using population-based data, we analyzed the database of records on child maltreatment and medical checkups for infants aged 1.5 years filed at Nishinomiya City Public Health Center between April 2007 and March 2011. To protect personal information, the data were transferred to anonymized electronic files for analysis. After adjusting by logistic regression for each associated factor and gestation number, multiples themselves were not associated with the risk of child maltreatment. However, compared with singletons, multiples had a significantly higher rate of risk factors for child maltreatment, including low birth weight and neural abnormality. Moreover, compared with mothers of singleton, mothers of twins had a significantly higher rate of poor health, which is a risk factor of child maltreatment. Multiples were not associated with the risk of child maltreatment. However, compared with singletons, multiples and their mothers had a significantly higher rate of risk factors of child maltreatment.

  3. Fetal Alcohol Spectrum Disorder (FASD) Associated Neural Defects: Complex Mechanisms and Potential Therapeutic Targets.

    Science.gov (United States)

    Muralidharan, Pooja; Sarmah, Swapnalee; Zhou, Feng C; Marrs, James A

    2013-06-19

    Fetal alcohol spectrum disorder (FASD), caused by prenatal alcohol exposure, can result in craniofacial dysmorphism, cognitive impairment, sensory and motor disabilities among other defects. FASD incidences are as high as 2% to 5 % children born in the US, and prevalence is higher in low socioeconomic populations. Despite various mechanisms being proposed to explain the etiology of FASD, the molecular targets of ethanol toxicity during development are unknown. Proposed mechanisms include cell death, cell signaling defects and gene expression changes. More recently, the involvement of several other molecular pathways was explored, including non-coding RNA, epigenetic changes and specific vitamin deficiencies. These various pathways may interact, producing a wide spectrum of consequences. Detailed understanding of these various pathways and their interactions will facilitate the therapeutic target identification, leading to new clinical intervention, which may reduce the incidence and severity of these highly prevalent preventable birth defects. This review discusses manifestations of alcohol exposure on the developing central nervous system, including the neural crest cells and sensory neural placodes, focusing on molecular neurodevelopmental pathways as possible therapeutic targets for prevention or protection.

  4. Fetal Alcohol Spectrum Disorder (FASD Associated Neural Defects: Complex Mechanisms and Potential Therapeutic Targets

    Directory of Open Access Journals (Sweden)

    James A. Marrs

    2013-06-01

    Full Text Available Fetal alcohol spectrum disorder (FASD, caused by prenatal alcohol exposure, can result in craniofacial dysmorphism, cognitive impairment, sensory and motor disabilities among other defects. FASD incidences are as high as 2% to 5 % children born in the US, and prevalence is higher in low socioeconomic populations. Despite various mechanisms being proposed to explain the etiology of FASD, the molecular targets of ethanol toxicity during development are unknown. Proposed mechanisms include cell death, cell signaling defects and gene expression changes. More recently, the involvement of several other molecular pathways was explored, including non-coding RNA, epigenetic changes and specific vitamin deficiencies. These various pathways may interact, producing a wide spectrum of consequences. Detailed understanding of these various pathways and their interactions will facilitate the therapeutic target identification, leading to new clinical intervention, which may reduce the incidence and severity of these highly prevalent preventable birth defects. This review discusses manifestations of alcohol exposure on the developing central nervous system, including the neural crest cells and sensory neural placodes, focusing on molecular neurodevelopmental pathways as possible therapeutic targets for prevention or protection.

  5. Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

    Science.gov (United States)

    Si, Wenjie; Dong, Xunde; Yang, Feifei

    2018-03-01

    This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

  6. Reducing one million child deaths from birth asphyxia – a survey of health systems gaps and priorities

    Directory of Open Access Journals (Sweden)

    Manandhar Ananta

    2007-05-01

    Full Text Available Abstract Background Millions of child deaths and stillbirths are attributable to birth asphyxia, yet limited information is available to guide policy and practice, particularly at the community level. We surveyed selected policymakers, programme implementers and researchers to compile insights on policies, programmes, and research to reduce asphyxia-related deaths. Method A questionnaire was developed and pretested based on an extensive literature review, then sent by email (or airmail or fax, when necessary to 453 policymakers, programme implementers, and researchers active in child health, particularly at the community level. The survey was available in French and English and employed 5-point scales for respondents to rate effectiveness and feasibility of interventions and indicators. Open-ended questions permitted respondents to furnish additional details based on their experience. Significance testing was carried out using chi-square, F-test and Fisher's exact probability tests as appropriate. Results 173 individuals from 32 countries responded (44%. National newborn survival policies were reported to exist in 20 of 27 (74% developing countries represented, but respondents' answers were occasionally contradictory and revealed uncertainty about policy content, which may hinder policy implementation. Respondents emphasized confusing terminology and a lack of valid measurement indicators at community level as barriers to obtaining accurate data for decision making. Regarding interventions, birth preparedness and essential newborn care were considered both effective and feasible, while resuscitation at community level was considered less feasible. Respondents emphasized health systems strengthening for both supply and demand factors as programme priorities, particularly ensuring wide availability of skilled birth attendants, promotion of birth preparedness, and promotion of essential newborn care. Research priorities included operationalising

  7. Signal Processing and Neural Network Simulator

    Science.gov (United States)

    Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

    1995-04-01

    The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

  8. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    Science.gov (United States)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  9. A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

    Science.gov (United States)

    Kuntanapreeda, S; Fullmer, R R

    1996-01-01

    A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

  10. Birth/birth-death processes and their computable transition probabilities with biological applications.

    Science.gov (United States)

    Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W; Minin, Vladimir N; Suchard, Marc A

    2018-03-01

    Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populations simultaneously. A lack of efficient methods for evaluating finite-time transition probabilities of bivariate processes, however, has restricted statistical inference in these models. Researchers rely on computationally expensive methods such as matrix exponentiation or Monte Carlo approximation, restricting likelihood-based inference to small systems, or indirect methods such as approximate Bayesian computation. In this paper, we introduce the birth/birth-death process, a tractable bivariate extension of the birth-death process, where rates are allowed to be nonlinear. We develop an efficient algorithm to calculate its transition probabilities using a continued fraction representation of their Laplace transforms. Next, we identify several exemplary models arising in molecular epidemiology, macro-parasite evolution, and infectious disease modeling that fall within this class, and demonstrate advantages of our proposed method over existing approaches to inference in these models. Notably, the ubiquitous stochastic susceptible-infectious-removed (SIR) model falls within this class, and we emphasize that computable transition probabilities newly enable direct inference of parameters in the SIR model. We also propose a very fast method for approximating the transition probabilities under the SIR model via a novel branching process simplification, and compare it to the continued fraction representation method with application to the 17th century plague in Eyam. Although the two methods produce similar maximum a posteriori estimates, the branching process approximation fails to capture the correlation structure in the joint posterior distribution.

  11. Low birth weight,very low birth weight rates and gestational age-specific birth weight distribution of korean newborn infants.

    Science.gov (United States)

    Shin, Son-Moon; Chang, Young-Pyo; Lee, Eun-Sil; Lee, Young-Ah; Son, Dong-Woo; Kim, Min-Hee; Choi, Young-Ryoon

    2005-04-01

    To obtain the low birth weight (LBW) rate, the very low birth weight (VLBW) rate, and gestational age (GA)-specific birth weight distribution based on a large population in Korea, we collected and analyzed the birth data of 108,486 live births with GA greater than 23 weeks for 1 yr from 1 January to 31 December 2001, from 75 hospitals and clinics located in Korea. These data included birth weight, GA, gender of the infants, delivery type, maternal age, and the presence of multiple pregnancy. The mean birth weight and GA of a crude population are 3,188 +/-518 g and 38.7+/-2.1 weeks, respectively. The LBW and the VLBW rates are 7.2% and 1.4%, respectively. The preterm birth rate (less than 37 completed weeks of gestation) is 8.4% and the very preterm birth rate (less than 32 completed weeks of gestation) is 0.7%. The mean birth weights for female infants, multiple births, and births delivered by cesarean section were lower than those for male, singletons, and births delivered vaginally. The risk of delivering LBW or VLBW infant was higher for the teenagers and the older women (aged 35 yr and more). We have also obtained the percentile distribution of GA-specific birth weight in infants over 23 weeks of gestation.

  12. Rotation-invariant neural pattern recognition system with application to coin recognition.

    Science.gov (United States)

    Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

    1992-01-01

    In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

  13. Sex differences of gray matter morphology in cortico-limbic-striatal neural system in major depressive disorder.

    Science.gov (United States)

    Kong, Lingtao; Chen, Kaiyuan; Womer, Fay; Jiang, Wenyan; Luo, Xingguang; Driesen, Naomi; Liu, Jie; Blumberg, Hilary; Tang, Yanqing; Xu, Ke; Wang, Fei

    2013-06-01

    Sex differences are observed in both epidemiological and clinical aspects of major depressive disorder (MDD). The cortico-limbic-striatal neural system, including the prefrontal cortex, amygdala, hippocampus, and striatum, have shown sexually dimorphic morphological features and have been implicated in the dysfunctional regulation of mood and emotion in MDD. In this study, we utilized a whole-brain, voxel-based approach to examine sex differences in the regional distribution of gray matter (GM) morphological abnormalities in medication-naïve participants with MDD. Participants included 29 medication-naïve individuals with MDD (16 females and 13 males) and 33 healthy controls (HC) (17 females and 16 males). Gray matter morphology of the cortico-limbic-striatal neural system was examined using voxel-based morphometry analyzes of high-resolution structural magnetic resonance imaging scans. The main effect of diagnosis and interaction effect of diagnosis by sex on GM morphology were statistically significant (p sex-related patterns of abnormalities within the cortico-limbic-strial neural system, such as predominant prefrontal-limbic abnormalities in MDD females vs. predominant prefrontal-striatal abnormalities in MDD males, suggest differences in neural circuitry that may mediate sex differences in the clinical presentation of MDD and potential targets for sex-differentiated treatment of the disorder. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. Realization of a neural algorithm by means of front-propagation in a thyristor-based hybrid system

    CERN Document Server

    Niedernostheide, F J; Freyd, O; Bode, M; Gorbatyuk, A V

    2003-01-01

    Propagating fronts are generic structures in a bistable diffusion-driven system and can be used to realize neural algorithms, as e.g., the Kohonen or the neural-gas algorithm. We present an analog-digital hybrid system based on a thyristor-like structure with several gate terminals. This structure represents the continuous part in which a propagating front, separating a region of high current density from a region of low current density, is used to control the learning process of the neural algorithm. With a system containing five neurons and five gates in a quasi one-dimensional arrangement it is demonstrated that an efficient parallel operating learning process can be realized by using the winner-take-all principle and the front propagation, i.e. exploiting the intrinsic dynamics of the semiconductor device. Finally, numerical and analytical investigations of the dependency of the front velocity and its width on the load current have been performed since these are essential parameters for improving the netw...

  15. Realization of a neural algorithm by means of front-propagation in a thyristor-based hybrid system

    International Nuclear Information System (INIS)

    Niedernostheide, F.-J.; Schulze, H.-J.; Freyd, O.; Bode, M.; Gorbatyuk, A.V.

    2003-01-01

    Propagating fronts are generic structures in a bistable diffusion-driven system and can be used to realize neural algorithms, as e.g., the Kohonen or the neural-gas algorithm. We present an analog-digital hybrid system based on a thyristor-like structure with several gate terminals. This structure represents the continuous part in which a propagating front, separating a region of high current density from a region of low current density, is used to control the learning process of the neural algorithm. With a system containing five neurons and five gates in a quasi one-dimensional arrangement it is demonstrated that an efficient parallel operating learning process can be realized by using the winner-take-all principle and the front propagation, i.e. exploiting the intrinsic dynamics of the semiconductor device. Finally, numerical and analytical investigations of the dependency of the front velocity and its width on the load current have been performed since these are essential parameters for improving the network performance

  16. Profile and birthing practices of Maranao traditional birth attendants

    Directory of Open Access Journals (Sweden)

    Maghuyop-Butalid R

    2015-10-01

    Full Text Available Roselyn Maghuyop-Butalid, Norhanifa A Mayo, Hania T Polangi College of Nursing, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines Abstract: This study determined the profile and birthing practices in both modern and traditional ways among Maranao traditional birth attendants (TBAs in Lanao del Norte, Philippines. It employed a descriptive research design. The respondents were 50 Maranao TBAs selected through the snowball sampling technique. A questionnaire was developed by the researchers to identify the respondents’ modern birthing practices utilizing the Essential Intrapartum and Newborn Care (EINC Protocol. To determine their profile and traditional birthing practices, items from a previous study and the respondents’ personal claims were adapted. This study shows that Maranao TBAs have less compliance to the EINC Protocol and they often practice the traditional birthing interventions, thus increasing the risk of complications to both mother and newborn. Keywords: intrapartum and newborn care, modern birthing practices, traditional birthing practices 

  17. Neural correlates of sad feelings in healthy girls.

    Science.gov (United States)

    Lévesque, J; Joanette, Y; Mensour, B; Beaudoin, G; Leroux, J-M; Bourgouin, P; Beauregard, M

    2003-01-01

    Emotional development is indisputably one of the cornerstones of personality development during infancy. According to the differential emotions theory (DET), primary emotions are constituted of three distinct components: the neural-evaluative, the expressive, and the experiential. The DET further assumes that these three components are biologically based and functional nearly from birth. Such a view entails that the neural substrate of primary emotions must be similar in children and adults. Guided by this assumption of the DET, the present functional magnetic resonance imaging study was conducted to identify the neural correlates of sad feelings in healthy children. Fourteen healthy girls (aged 8-10) were scanned while they watched sad film excerpts aimed at externally inducing a transient state of sadness (activation task). Emotionally neutral film excerpts were also presented to the subjects (reference task). The subtraction of the brain activity measured during the viewing of the emotionally neutral film excerpts from that noted during the viewing of the sad film excerpts revealed that sad feelings were associated with significant bilateral activations of the midbrain, the medial prefrontal cortex (Brodmann area [BA] 10), and the anterior temporal pole (BA 21). A significant locus of activation was also noted in the right ventrolateral prefrontal cortex (BA 47). These results are compatible with those of previous functional neuroimaging studies of sadness in adults. They suggest that the neural substrate underlying the subjective experience of sadness is comparable in children and adults. Such a similitude provides empirical support to the DET assumption that the neural substrate of primary emotions is biologically based.

  18. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

    Santosh; Vinod, Gopika; Saraf, R.K.

    2003-01-01

    Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

  19. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  20. Home versus hospital birth--process and outcome.

    Science.gov (United States)

    Wax, Joseph R; Pinette, Michael G; Cartin, Angelina

    2010-02-01

    A constant small, but clinically important, number of American women choose to deliver at home. Contradictory professional and public policies reflect the polarization and politicization of the controversy surrounding this birth option. Women opting for home birth seek and often attain their goals of a nonmedicalized experience in comfortable, familiar surroundings wherein they maintain situational control. However, home deliveries in developed Western nations are often associated with excess perinatal and neonatal mortality, particularly among nonanomalous term infants. On the other hand, current home birth practices are, especially when birth attendants are highly trained and fully integrated into comprehensive health care delivery systems, associated with fewer cesareans, operative vaginal deliveries, episiotomies, infections, and third and fourth degree lacerations. Newborn benefits include less meconium staining, assisted ventilation, low birth weight, prematurity, and intensive care admissions. Existing data suggest areas of future research regarding the safety of home birth in the United States. Obstetricians & Gynecologists, Family Physicians. After completion of this educational activity, the participant should be better able to assess perinatal outcomes described in the reported literature associated with home births in developed countries, list potential advantages and disadvantages of planned home births, and identify confounders in current literature that impact our thorough knowledge of home birth outcomes.

  1. Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

    Science.gov (United States)

    Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

    2010-02-01

    Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

  2. Birth Control

    Science.gov (United States)

    Birth control, also known as contraception, is designed to prevent pregnancy. Birth control methods may work in a number of different ... eggs that could be fertilized. Types include birth control pills, patches, shots, vaginal rings, and emergency contraceptive ...

  3. [Home births].

    Science.gov (United States)

    Welffens, K; Kirkpatrick, C; Daelemans, C; Derisbourg, S

    In Belgium, very few women give birth outside the delivery room. In the United Kingdom and in the Netherlands, they are more numerous. Several studies evaluated obstetric and neonatal outcomes of home births compared with hospital births. We selected seven recent and large studies (with cohorts of more than 5.000 women) using PubMed, Science Direct and Cochrane Database of Systematic Reviews. Several questions were examined. Is there any difference in maternal and neonatal outcomes depending on the intended place of birth? Does parity affect outcomes ? What are the characteristics of women who choose to deliver at home ? We conclude that giving birth at home improves obstetric outcomes but is riskier for the baby, especially for the first one. The women delivering at home are mainly white Europeans, between 25 and 35 years old, in a relationship, multiparous and wealthier. In order to avoid this increased risk for the baby while preserving the obstetric advantages, alongside birth centers offer an intermediate solution. They combine the reassuring home-like atmosphere with the safety of the hospital. In Belgium, the first alongside birth center " Le Cocon " (a low technicity unit distinct from the delivery room) offers now this type of alternative place of birth for women in Hôpital Erasme in Brussels.

  4. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.

  5. Reconstruction of neutron spectra using neural networks starting from the Bonner spheres spectrometric system

    International Nuclear Information System (INIS)

    Ortiz R, J.M.; Martinez B, M.R.; Arteaga A, T.; Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.

    2005-01-01

    The artificial neural networks (RN) have been used successfully to solve a wide variety of problems. However to determine an appropriate set of values of the structural parameters and of learning of these, it continues being even a difficult task. Contrary to previous works, here a set of neural networks is designed to reconstruct neutron spectra starting from the counting rates coming from the detectors of the Bonner spheres system, using a systematic and experimental strategy for the robust design of multilayer neural networks of the feed forward type of inverse propagation. The robust design is formulated as a design problem of Taguchi parameters. It was selected a set of 53 neutron spectra, compiled by the International Atomic Energy Agency, the counting rates were calculated that would take place in a Bonner spheres system, the set was arranged according to the wave form of those spectra. With these data and applying the Taguchi methodology to determine the best parameters of the network topology, it was trained and it proved the same one with the spectra. (Author)

  6. Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-01-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  7. Season of birth and primary central nervous system tumors: a systematic review of the literature with critical appraisal of underlying mechanisms.

    Science.gov (United States)

    Georgakis, Marios K; Ntinopoulou, Erato; Chatzopoulou, Despoina; Petridou, Eleni Th

    2017-09-01

    Season of birth has been considered a proxy of seasonally varying exposures around perinatal period, potentially implicated in the etiology of several health outcomes, including malignancies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we have systematically reviewed published literature on the association of birth seasonality with risk of central nervous system tumors in children and adults. Seventeen eligible studies using various methodologies were identified, encompassing 20,523 cases. Eight of 10 studies in children versus four of eight in adults showed some statistically significant associations between birth seasonality and central nervous system tumor or tumor subtype occurrence, pointing to a clustering of births mostly in fall and winter months, albeit no consistent pattern was identified by histologic subtype. A plethora of perinatal factors might underlie or confound the associations, such as variations in birth weight, maternal diet during pregnancy, perinatal vitamin D levels, pesticides, infectious agents, immune system maturity, and epigenetic modifications. Inherent methodological weaknesses of to-date published individual investigations, including mainly underpowered size to explore the hypothesis by histological subtype, call for more elegant concerted actions using primary data of large datasets taking also into account the interplay between the potential underlying etiologic factors. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

    Science.gov (United States)

    Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

    2011-01-01

    To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

  9. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    Science.gov (United States)

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  10. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

  11. Birth Defects Among Fetuses and Infants of US Women With Evidence of Possible Zika Virus Infection During Pregnancy.

    Science.gov (United States)

    Honein, Margaret A; Dawson, April L; Petersen, Emily E; Jones, Abbey M; Lee, Ellen H; Yazdy, Mahsa M; Ahmad, Nina; Macdonald, Jennifer; Evert, Nicole; Bingham, Andrea; Ellington, Sascha R; Shapiro-Mendoza, Carrie K; Oduyebo, Titilope; Fine, Anne D; Brown, Catherine M; Sommer, Jamie N; Gupta, Jyoti; Cavicchia, Philip; Slavinski, Sally; White, Jennifer L; Owen, S Michele; Petersen, Lyle R; Boyle, Coleen; Meaney-Delman, Dana; Jamieson, Denise J

    2017-01-03

    Understanding the risk of birth defects associated with Zika virus infection during pregnancy may help guide communication, prevention, and planning efforts. In the absence of Zika virus, microcephaly occurs in approximately 7 per 10 000 live births. To estimate the preliminary proportion of fetuses or infants with birth defects after maternal Zika virus infection by trimester of infection and maternal symptoms. Completed pregnancies with maternal, fetal, or infant laboratory evidence of possible recent Zika virus infection and outcomes reported in the continental United States and Hawaii from January 15 to September 22, 2016, in the US Zika Pregnancy Registry, a collaboration between the CDC and state and local health departments. Laboratory evidence of possible recent Zika virus infection in a maternal, placental, fetal, or infant sample. Birth defects potentially Zika associated: brain abnormalities with or without microcephaly, neural tube defects and other early brain malformations, eye abnormalities, and other central nervous system consequences. Among 442 completed pregnancies in women (median age, 28 years; range, 15-50 years) with laboratory evidence of possible recent Zika virus infection, birth defects potentially related to Zika virus were identified in 26 (6%; 95% CI, 4%-8%) fetuses or infants. There were 21 infants with birth defects among 395 live births and 5 fetuses with birth defects among 47 pregnancy losses. Birth defects were reported for 16 of 271 (6%; 95% CI, 4%-9%) pregnant asymptomatic women and 10 of 167 (6%; 95% CI, 3%-11%) symptomatic pregnant women. Of the 26 affected fetuses or infants, 4 had microcephaly and no reported neuroimaging, 14 had microcephaly and brain abnormalities, and 4 had brain abnormalities without microcephaly; reported brain abnormalities included intracranial calcifications, corpus callosum abnormalities, abnormal cortical formation, cerebral atrophy, ventriculomegaly, hydrocephaly, and cerebellar abnormalities

  12. Neural Network Models of Simple Mechanical Systems Illustrating the Feasibility of Accelerated Life Testing

    Science.gov (United States)

    Fusaro, Robert L.; Jones, Steven P.; Jansen, Ralph

    1996-01-01

    A complete evaluation of the tribological characteristics of a given material/mechanical system is a time-consuming operation since the friction and wear process is extremely systems sensitive. As a result, experimental designs (i.e., Latin Square, Taguchi) have been implemented in an attempt to not only reduce the total number of experimental combinations needed to fully characterize a material/mechanical system, but also to acquire life data for a system without having to perform an actual life test. Unfortunately, these experimental designs still require a great deal of experimental testing and the output does not always produce meaningful information. In order to further reduce the amount of experimental testing required, this study employs a computer neural network model to investigate different material/mechanical systems. The work focuses on the modeling of the wear behavior, while showing the feasibility of using neural networks to predict life data. The model is capable of defining which input variables will influence the tribological behavior of the particular material/mechanical system being studied based on the specifications of the overall system.

  13. Artificial neural network decision support systems for new product development project selection

    NARCIS (Netherlands)

    Thieme, R.J.; Song, Michael; Calantone, R.J.

    2000-01-01

    The authors extend and develop an artificial neural network decision support system and demonstrate how it can guide managers when they make complex new product development decisions. The authors use data from 612 projects to compare this new method with traditional methods for predicting various

  14. Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

    Science.gov (United States)

    Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

    2017-09-01

    This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

  15. An artificial neural network system to identify alleles in reference electropherograms.

    Science.gov (United States)

    Taylor, Duncan; Harrison, Ash; Powers, David

    2017-09-01

    Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells them about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. This process of interpreting the electropherograms can be time consuming and is prone to subjective differences between analysts. Recently it was demonstrated that artificial neural networks could be used to classify information within an electropherogram as allelic (i.e. representative of a DNA fragment present in the DNA extract) or as one of several different categories of artefactual fluorescence that arise as a result of generating an electropherogram. We extend that work here to demonstrate a series of algorithms and artificial neural networks that can be used to identify peaks on an electropherogram and classify them. We demonstrate the functioning of the system on several profiles and compare the results to a leading commercial DNA profile reading system. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. A developmental perspective on the neural bases of human empathy.

    Science.gov (United States)

    Tousignant, Béatrice; Eugène, Fanny; Jackson, Philip L

    2017-08-01

    While empathy has been widely studied in philosophical and psychological literatures, recent advances in social neuroscience have shed light on the neural correlates of this complex interpersonal phenomenon. In this review, we provide an overview of brain imaging studies that have investigated the neural substrates of human empathy. Based on existing models of the functional architecture of empathy, we review evidence of the neural underpinnings of each main component, as well as their development from infancy. Although early precursors of affective sharing and self-other distinction appear to be present from birth, recent findings also suggest that even higher-order components of empathy such as perspective-taking and emotion regulation demonstrate signs of development during infancy. This merging of developmental and social neuroscience literature thus supports the view that ontogenic development of empathy is rooted in early infancy, well before the emergence of verbal abilities. With age, the refinement of top-down mechanisms may foster more appropriate empathic responses, thus promoting greater altruistic motivation and prosocial behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  18. Crossing Borders in Birthing Practices: A Hmong Village in Northern Thailand (1987-2013

    Directory of Open Access Journals (Sweden)

    Kathleen A. Culhane-Pera

    2014-12-01

    Full Text Available Background: Over the past several decades in Northern Thailand, there has been a contest of authoritative knowledge between the Hmong traditional birth system and the Thai biomedical maternity system. In this paper, we explore the contest in one Hmong village by describing the traditional and biomedical practices; families’ birth location choices; and elements of authoritative knowledge. Methods: We built on a village survey and conducted an ethnographic qualitative case study of 16 families who made different pregnancy care choices. Results: The contest is being won by the Thai biomedical system, as most families deliver at the hospital. These families choose hospital births when they evaluate problems or potential problems; they have more confidence in the superior Thai biomedical system with its technology and medicines than in the inadequate Hmong traditional system. But the contest is ongoing, as some families prefer to birth at home. These families choose home births when they want a supportive home environment; they embrace traditional Hmong birth knowledge and practices as superior and reject hospital birth practices as unnecessary, harmful, abusive, and inadequate. Despite their choice for any given pregnancy, the case study families feel the pull of the other choice: hospital birth families lament loss of the home environment and express their dislike of hospital practices; and home birth families feel the anxiety of potentially needing quick obstetrical assistance that is far away. Conclusion: While most families choose to participate in the Thai biomedical system, they also use Hmong pregnancy and postpartum practices, and some families choose home births. In this village, the contest for the supremacy of authoritative birth knowledge is ongoing.

  19. An electronic system for simulation of neural networks with a micro-second real time constraint

    International Nuclear Information System (INIS)

    Chorti, Arsenia; Granado, Bertrand; Denby, Bruce; Garda, Patrick

    2001-01-01

    Neural networks implemented in hardware can perform pattern recognition very quickly, and as such have been used to advantage in the triggering systems of certain high energy physics experiments. Typically, time constants of the order of a few microseconds are required. In this paper, we present a new system. MAHARADJA, for evaluating MLP and RBF neural network paradigms in real time. The system is tested on a possible ATLAS muon triggering application suggested by the Tel Aviv ATLAS group, consisting of a 4-8-8-4 MLP which must be evaluated in 10 microseconds. The inputs to the net are dx/dz, x(z=0), dy/dz, and y(z=0), whereas the outputs give pt, tan(phi), sin(theta), and q, the charge. With a 10 MHz clock, MAHARADJA calculates the result in 6.8 microseconds; at 20 MHz, which is readily attainable, this would be reduced to only 3.4 microseconds. The system can also handle RBF networks with 3 different distance metrics (Euclidean, Manhattan and Mahalanobis), and can simulate any MLP of 10 hidden layers or less. The electronic implementation is with FPGA's, which can be optimized for a specific neural network because the number of processing elements can be modified

  20. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  1. Deep convolutional neural network based antenna selection in multiple-input multiple-output system

    Science.gov (United States)

    Cai, Jiaxin; Li, Yan; Hu, Ying

    2018-03-01

    Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

  2. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Institut Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D J; Pao, Yohhan [Case Western Reserve Univ., Cleveland, OH (United States)

    1991-10-01

    The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

  3. Birth outcomes of planned home births in Missouri: a population-based study.

    Science.gov (United States)

    Chang, Jen Jen; Macones, George A

    2011-08-01

    We evaluated the birth outcomes of planned home births. We conducted a retrospective cohort study using Missouri vital records from 1989 to 2005 to compare the risk of newborn seizure and intrapartum fetal death in planned home births attended by physicians/certified nurse midwives (CNMs) or non-CNMs with hospitals/birthing center births. The study sample included singleton pregnancies between 36 and 44 weeks of gestation without major congenital anomalies or breech presentation ( N = 859,873). The adjusted odds ratio (aOR) of newborn seizures in planned home births attended by non-CNMs was 5.11 (95% confidence interval [CI]: 2.52, 10.37) compared with deliveries by physicians/CNMs in hospitals/birthing centers. For intrapartum fetal death, aORs were 11.24 (95% CI: 1.43, 88.29), and 20.33 (95% CI: 4.98, 83.07) in planned home births attended by non-CNMs and by physicians/CNMs, respectively, compared with births in hospitals/birthing centers. Planned home births are associated with increased likelihood of adverse birth outcomes. © Thieme Medical Publishers.

  4. Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks.

    Science.gov (United States)

    Chansanroj, Krisanin; Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele

    2011-10-09

    Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties. Copyright © 2011 Elsevier B.V. All rights reserved.

  5. Similar photoperiod-related birth seasonalities among professional baseball players and lesbian women with an opposite seasonality among gay men: Maternal melatonin may affect fetal sexual dimorphism.

    Science.gov (United States)

    Marzullo, Giovanni

    2014-05-30

    Based on pre-mid-20th-century data, the same photoperiod-related birth seasonality previously observed in schizophrenia was also recently found in neural-tube defects and in extreme left-handedness among professional baseball players. This led to a hypothesis implicating maternal melatonin and other mediators of sunlight actions capable of affecting 4th-embryonic-week developments including neural-tube closure and left-right differentiation of the brain. Here, new studies of baseball players suggest that the same sunlight actions could also affect testosterone-dependent male-female differentiation in the 4-month-old fetus. Independently of hand-preferences, baseball players (n=6829), and particularly the stronger hitters among them, showed a unique birth seasonality with an excess around early-November and an equally significant deficit 6 months later around early-May. In two smaller studies, north-American and other northern-hemisphere born lesbians showed the same strong-hitter birth seasonality while gay men showed the opposite seasonality. The sexual dimorphism-critical 4th-fetal-month testosterone surge coincides with the summer-solstice in early-November births and the winter-solstice in early-May births. These coincidences are discussed and a "melatonin mechanism" is proposed based on evidence that in seasonal breeders maternal melatonin imparts "photoperiodic history" to the newborn by direct inhibition of fetal testicular testosterone synthesis. The present effects could represent a vestige of this same phenomenon in man. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  6. Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2017-07-01

    In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

  7. Modulation of nuclear factor-κB signaling and reduction of neural tube defects by quercetin-3-glucoside in embryos of diabetic mice.

    Science.gov (United States)

    Tan, Chengyu; Meng, Fantong; Reece, E Albert; Zhao, Zhiyong

    2018-05-04

    Diabetes mellitus in early pregnancy increases the risk of birth defects in infants. Maternal hyperglycemia stimulates the expression of nitric oxide (NO) synthase 2 (NOS2), which can be regulated by transcription factors of the nuclear factor-κB (NF-κB) family. Increases in reactive nitrogen species (RNS) generate intracellular stress conditions, including nitrosative, oxidative, and endoplasmic reticulum (ER) stresses, and trigger programmed cell death (or apoptosis) in the neural folds, resulting in neural tube defects (NTDs) in the embryo. Inhibiting NOS2 can reduce NTDs; however, the underlying mechanisms require further delineation. Targeting NOS2 and associated nitrosative stress using naturally occurring phytochemicals is a potential approach to preventing birth defects in diabetic pregnancies. This study aims to investigate the effect of quercetin-3-glucoside (Q3G), a polyphenol flavonoid found in fruit, in reducing maternal diabetes-induced NTDs in an animal model, and to delineate the molecular mechanisms underlying Q3G action in regulating NOS2 expression. Female mice (C57BL/6) were induced to develop diabetes using streptozotocin before pregnancy. Diabetic pregnant mice were administered Q3G (100 mg/kg) daily via gavage feeding, introduction of drug to the stomach directly via a feeding needle, during neurulation from embryonic (E) day 6.5 to E9.5. After treatment, E10.5 embryos were collected and examined for the presence of NTDs and apoptosis in the neural tube. Expression of Nos2 and superoxide dismutase 1 (Sod1; an antioxidative enzyme) was quantified using Western blot assay. Nitrosative, oxidative, and endoplasmic reticulum (ER) stress conditions were assessed using specific biomarkers. Expression and posttranslational modification of factors in the NF-κB system were investigated. Treatment with Q3G (suspended in water) significantly decreased NTD rate (24.7%) and apoptosis in the embryos of diabetic mice, compared with those in the water

  8. Committee Opinion No. 669 Summary: Planned Home Birth.

    Science.gov (United States)

    2016-08-01

    In the United States, approximately 35,000 births (0.9%) per year occur in the home. Approximately one fourth of these births are unplanned or unattended. Although the American College of Obstetricians and Gynecologists believes that hospitals and accredited birth centers are the safest settings for birth, each woman has the right to make a medically informed decision about delivery. Importantly, women should be informed that several factors are critical to reducing perinatal mortality rates and achieving favorable home birth outcomes. These factors include the appropriate selection of candidates for home birth; the availability of a certified nurse-midwife, certified midwife or midwife whose education and licensure meet International Confederation of Midwives' Global Standards for Midwifery Education, or physician practicing obstetrics within an integrated and regulated health system; ready access to consultation; and access to safe and timely transport to nearby hospitals. The Committee on Obstetric Practice considers fetal malpresentation, multiple gestation, or prior cesarean delivery to be an absolute contraindication to planned home birth.

  9. Committee Opinion No 697 Summary: Planned Home Birth.

    Science.gov (United States)

    2017-04-01

    In the United States, approximately 35,000 births (0.9%) per year occur in the home. Approximately one fourth of these births are unplanned or unattended. Although the American College of Obstetricians and Gynecologists believes that hospitals and accredited birth centers are the safest settings for birth, each woman has the right to make a medically informed decision about delivery. Importantly, women should be informed that several factors are critical to reducing perinatal mortality rates and achieving favorable home birth outcomes. These factors include the appropriate selection of candidates for home birth; the availability of a certified nurse-midwife, certified midwife or midwife whose education and licensure meet International Confederation of Midwives' Global Standards for Midwifery Education, or physician practicing obstetrics within an integrated and regulated health system; ready access to consultation; and access to safe and timely transport to nearby hospitals. The Committee on Obstetric Practice considers fetal malpresentation, multiple gestation, or prior cesarean delivery to be an absolute contraindication to planned home birth.

  10. Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises.

    Science.gov (United States)

    Borrajo, M Lourdes; Baruque, Bruno; Corchado, Emilio; Bajo, Javier; Corchado, Juan M

    2011-08-01

    During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

  11. Cell Junction Pathology of Neural Stem Cells Is Associated With Ventricular Zone Disruption, Hydrocephalus, and Abnormal Neurogenesis

    NARCIS (Netherlands)

    Montserrat Guerra, Maria; Henzi, Roberto; Ortloff, Alexander; Lichtin, Nicole; Vio, Karin; Jimenez, Antonio J.; Dolores Dominguez-Pinos, Maria; Gonzalez, Cesar; Clara Jara, Maria; Hinostroza, Fernando; Rodriguez, Sara; Jara, Maryoris; Ortega, Eduardo; Guerra, Francisco; Sival, Deborah A.; den Dunnen, Wilfred F. A.; Perez-Figares, Jose M.; McAllister, James P.; Johanson, Conrad E.; Rodriguez, Esteban M.

    Fetal-onset hydrocephalus affects 1 to 3 per 1,000 live births. It is not only a disorder of cerebrospinal fluid dynamics but also a brain disorder that corrective surgery does not ameliorate. We hypothesized that cell junction abnormalities of neural stem cells (NSCs) lead to the inseparable

  12. Going public: do risk and choice explain differences in caesarean birth rates between public and private places of birth in Australia?

    Science.gov (United States)

    Miller, Yvette D; Prosser, Samantha J; Thompson, Rachel

    2012-10-01

    differences in the likelihood of caesarean births are complex and are linked to differences in the perceived choices for mode of birth between women birthing in the private and public systems. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. A neural network based artificial vision system for licence plate recognition.

    Science.gov (United States)

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

  14. Profile and birthing practices of Maranao traditional birth attendants.

    Science.gov (United States)

    Maghuyop-Butalid, Roselyn; Mayo, Norhanifa A; Polangi, Hania T

    2015-01-01

    This study determined the profile and birthing practices in both modern and traditional ways among Maranao traditional birth attendants (TBAs) in Lanao del Norte, Philippines. It employed a descriptive research design. The respondents were 50 Maranao TBAs selected through the snowball sampling technique. A questionnaire was developed by the researchers to identify the respondents' modern birthing practices utilizing the Essential Intrapartum and Newborn Care (EINC) Protocol. To determine their profile and traditional birthing practices, items from a previous study and the respondents' personal claims were adapted. This study shows that Maranao TBAs have less compliance to the EINC Protocol and they often practice the traditional birthing interventions, thus increasing the risk of complications to both mother and newborn.

  15. Facilitating home birth.

    Science.gov (United States)

    Finigan, Valerie; Chadderton, Diane

    2015-06-01

    The birth of a baby is a family experience. However, in the United Kingdom birth often occurs outside the family environment, in hospital. Both home and hospital births have risks and benefits, but research shows that, for most women, it is as safe to give birth at home as it is in hospital. Women report home-birth to be satisfying with lowered risks of intervention and less likelihood of being separated from their family. It is also more cost effective for the National Health Service. Yet, whilst midwives are working hard to promote home birth as an option, it remains controversial. The aim of this paper is to raise awareness of the safety of home birth and the needs of women and midwives when a home birth is chosen. It provides an overview of care required and the role of the midwife in the ensuring care is woman-centred and personalised.

  16. Low Birth Weight, Very Low Birth Weight Rates and Gestational Age-Specific Birth Weight Distribution of Korean Newborn Infants

    OpenAIRE

    Shin, Son-Moon; Chang, Young-Pyo; Lee, Eun-Sil; Lee, Young-Ah; Son, Dong-Woo; Kim, Min-Hee; Choi, Young-Ryoon

    2005-01-01

    To obtain the low birth weight (LBW) rate, the very low birth weight (VLBW) rate, and gestational age (GA)-specific birth weight distribution based on a large population in Korea, we collected and analyzed the birth data of 108,486 live births with GA greater than 23 weeks for 1 yr from 1 January to 31 December 2001, from 75 hospitals and clinics located in Korea. These data included birth weight, GA, gender of the infants, delivery type, maternal age, and the presence of multiple pregnancy. ...

  17. Artificial Neural Network for Location Estimation in Wireless Communication Systems

    Directory of Open Access Journals (Sweden)

    Chien-Sheng Chen

    2012-03-01

    Full Text Available In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS. To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA measurements and the angle of arrival (AOA information to locate MS when three base stations (BSs are available. Artificial neural networks (ANN are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line, based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  18. Artificial neural network for location estimation in wireless communication systems.

    Science.gov (United States)

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  19. Kinetic Behavior of Aggregation-Exchange Growth Process with Catalyzed-Birth

    International Nuclear Information System (INIS)

    Han Anjia; Chen Yu; Lin Zhenquan; Ke Jianhong

    2007-01-01

    We propose an aggregation model of a two-species system to mimic the growth of cities' population and assets, in which irreversible coagulation reactions and exchange reactions occur between any two aggregates of the same species, and the monomer-birth reactions of one species occur by the catalysis of the other species. In the case with population-catalyzed birth of assets, the rate kernel of an asset aggregate B k of size k grows to become an aggregate B k+1 through a monomer-birth catalyzed by a population aggregate A j of size j is J(k,j) = Jkj λ . And in mutually catalyzed birth model, the birth rate kernels of population and assets are H(k,j) = Hkj η and J(k,j) = Jkj λ , respectively. The kinetics of the system is investigated based on the mean-field theory. In the model of population-catalyzed birth of assets, the long-time asymptotic behavior of the assets aggregate size distribution obeys the conventional or modified scaling form. In mutually catalyzed birth system, the asymptotic behaviors of population and assets obey the conventional scaling form in the case of η = λ = 0, and they obey the modified scaling form in the case of η = 0,λ = 1. In the case of η = λ = 1, the total mass of population aggregates and that of asset aggregates both grow much faster than those in population-catalyzed birth of assets model, and they approaches to infinite values in finite time.

  20. Neural plasticity of development and learning.

    Science.gov (United States)

    Galván, Adriana

    2010-06-01

    Development and learning are powerful agents of change across the lifespan that induce robust structural and functional plasticity in neural systems. An unresolved question in developmental cognitive neuroscience is whether development and learning share the same neural mechanisms associated with experience-related neural plasticity. In this article, I outline the conceptual and practical challenges of this question, review insights gleaned from adult studies, and describe recent strides toward examining this topic across development using neuroimaging methods. I suggest that development and learning are not two completely separate constructs and instead, that they exist on a continuum. While progressive and regressive changes are central to both, the behavioral consequences associated with these changes are closely tied to the existing neural architecture of maturity of the system. Eventually, a deeper, more mechanistic understanding of neural plasticity will shed light on behavioral changes across development and, more broadly, about the underlying neural basis of cognition. (c) 2010 Wiley-Liss, Inc.

  1. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

    Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

  2. Chemical analysis of multicomponent aqueous solutions using a system of nonselective sensor and artificial neural networks

    International Nuclear Information System (INIS)

    Vlasov, Yu.G.; Legin, A.V.; Rudnitskaya, A.M.; Amiko, A.D.; Natale, K.D.

    1997-01-01

    With the aim of creating a multisensor system for determining heavy-metal cations (Cu 2+ , Pb 2+ , Cd 2+ , and Zn 2+ ) and inorganic anions (Cl - , F - , and SO 4 2- ), measurements in mixed solutions were carried out with the use of an array of sensors based on chalcogenide glass electrodes, and the possibility of using various methods of mathematical processing of the resulting intricate signals was studied. Three methods of data processing were used: multilinear regression, partial least squares, and artificial neural networks. It was found that the multisensor system proposed were suitable for determining all of the analytes with an accuracy of 1-10%. Because the responses of sensors in solutions of complex composition deviated from linearity, the lowest determination errors were obtained with the use of an artificial neural network. As to the method of data securing (nonselective response of a sensor array) and processing (artificial neural network), the multisensor system developed may be considered a prototype of a device of the electronic tongue type

  3. A multiple system governed by a quasi-birth-and-death process

    International Nuclear Information System (INIS)

    Perez-Ocon, Rafael; Montoro-Cazorla, Delia

    2004-01-01

    The system we consider comprises n units, of which one has to operate for the system to work. The other units are in repair, in cold standby, or waiting for repair. Only the working unit can fail. The operational and repair times follow phase-type distributions. Upon failure, it is replaced by a standby unit and goes to the repair facility. There is only one repairman. When one unit operates the system is up and when all the units are in repair or waiting for repair, the system is down. This system is governed by a finite quasi-birth-and-death process. The stationary probability vector and useful performance measures in reliability, such as the availability and the rate of occurrence of failures are explicitly calculated. This model extends other previously considered in the literature. The case with an infinite number of units in cold standby is also studied. Computational implementation of the results is performed via a numerical example, and the different systems considered are compared from the reliability measures determined

  4. Neural systems of second language reading are shaped by native language.

    Science.gov (United States)

    Tan, Li Hai; Spinks, John A; Feng, Ching-Mei; Siok, Wai Ting; Perfetti, Charles A; Xiong, Jinhu; Fox, Peter T; Gao, Jia-Hong

    2003-03-01

    Reading in a second language (L2) is a complex task that entails an interaction between L2 and the native language (L1). To study the underlying mechanisms, we used functional magnetic resonance imaging (fMRI) to visualize Chinese-English bilinguals' brain activity in phonological processing of logographic Chinese and alphabetic English, two written languages with a sharp contrast in phonology and orthography. In Experiment 1, we found that phonological processing of Chinese characters recruits a neural system involving left middle frontal and posterior parietal gyri, cortical regions that are known to contribute to spatial information representation, spatial working memory, and coordination of cognitive resources as a central executive system. We assume that the peak activation of this system is relevant to the unique feature of Chinese that a logographic character has a square configuration that maps onto a monosyllabic unit of speech. Equally important, when our bilingual subjects performed a phonological task on English words, this neural system was most active, whereas brain areas mediating English monolinguals' fine-grained phonemic analysis, as demonstrated by Experiment 2, were only weakly activated. This suggests that our bilingual subjects were applying their L1 system to L2 reading and that the lack of letter-to-sound conversion rules in Chinese led Chinese readers to being less capable of processing English by recourse to an analytic reading system on which English monolinguals rely. Our brain imaging findings lend strongest support to the idea that language experience tunes the cortex. Copyright 2003 Wiley-Liss, Inc.

  5. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    A two-layer feed-forward neural network with Hyperbolic tangent sigmoid ... The neural network model when subjected to test, using the validation input data; ... Proportional Integral Derivative (PID) Controller is used to control the neural ...

  6. Neural Networks and Micromechanics

    Science.gov (United States)

    Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

    The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

  7. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems.

    Science.gov (United States)

    Chang, Sun-Il; Park, Sung-Yun; Yoon, Euisik

    2018-01-17

    This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm² and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µV rms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  8. Born too soon: preterm birth matters.

    Science.gov (United States)

    Howson, Christopher P; Kinney, Mary V; McDougall, Lori; Lawn, Joy E

    2013-01-01

    Urgent action is needed to address preterm birth given that the fi rst country-level estimates show that globally 15 million babies are born too soon and rates are increasing in most countries with reliable time trend data. As the fi rst in a supplement entitled “Born Too Soon”, this paper focuses on the global policy context. Preterm birth is critical for progress on Millennium Development Goal 4 (MDG) for child survival by 2015 and beyond, and gives added value to maternal health (MDG 5) investments also linking to non-communicable diseases. For preterm babies who survive, the additional burden of prematurity-related disability may aff ect families and health systems. Prematurity is an explicit priority in many high-income settings; however, more attention is needed especially in low- and middle-income countries where the invisibility of preterm birth as well as its myths and misconceptions have slowed action on prevention and care. Recent global attention to preterm birth hit a tipping point in 2012, with the May 2 publication of Born Too Soon: The Global Action Report on Preterm Birth and with the 2nd annual World Prematurity Day on November 17 which mobilised the actions of partners in many countries to address preterm birth and newborn health. Interventions to strengthen preterm birth prevention and care span the continuum of care for reproductive, maternal, newborn and child health. Both prevention of preterm birth and implementation of care of premature babies require more research, as well as more policy attention and programmatic investment.

  9. Speaker diarization system using HXLPS and deep neural network

    Directory of Open Access Journals (Sweden)

    V. Subba Ramaiah

    2018-03-01

    Full Text Available In general, speaker diarization is defined as the process of segmenting the input speech signal and grouped the homogenous regions with regard to the speaker identity. The main idea behind this system is that it is able to discriminate the speaker signal by assigning the label of the each speaker signal. Due to rapid growth of broadcasting and meeting, the speaker diarization is burdensome to enhance the readability of the speech transcription. In order to solve this issue, Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS and deep neural network (DNN is proposed for the speaker diarization system. The HXLPS extraction method is newly developed by incorporating the Holoentropy with the XLPS. Once we attain the features, the speech and non-speech signals are detected by the Voice Activity Detection (VAD method. Then, i-vector representation of every segmented signal is obtained using Universal Background Model (UBM model. Consequently, DNN is utilized to assign the label for the speaker signal which is then clustered according to the speaker label. The performance is analysed using the evaluation metrics, such as tracking distance, false alarm rate and diarization error rate. The outcome of the proposed method ensures the better diarization performance by achieving the lower DER of 1.36% based on lambda value and DER of 2.23% depends on the frame length. Keywords: Speaker diarization, HXLPS feature extraction, Voice activity detection, Deep neural network, Speaker clustering, Diarization Error Rate (DER

  10. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  11. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  12. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

    Science.gov (United States)

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-06-02

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

  13. Abnormal cortical development after premature birth shown by altered allometric scaling of brain growth.

    Directory of Open Access Journals (Sweden)

    Olga Kapellou

    2006-08-01

    Full Text Available We postulated that during ontogenesis cortical surface area and cerebral volume are related by a scaling law whose exponent gives a quantitative measure of cortical development. We used this approach to investigate the hypothesis that premature termination of the intrauterine environment by preterm birth reduces cortical development in a dose-dependent manner, providing a neural substrate for functional impairment.We analyzed 274 magnetic resonance images that recorded brain growth from 23 to 48 wk of gestation in 113 extremely preterm infants born at 22 to 29 wk of gestation, 63 of whom underwent neurodevelopmental assessment at a median age of 2 y. Cortical surface area was related to cerebral volume by a scaling law with an exponent of 1.29 (95% confidence interval, 1.25-1.33, which was proportional to later neurodevelopmental impairment. Increasing prematurity and male gender were associated with a lower scaling exponent (p < 0.0001 independent of intrauterine or postnatal somatic growth.Human brain growth obeys an allometric scaling relation that is disrupted by preterm birth in a dose-dependent, sexually dimorphic fashion that directly parallels the incidence of neurodevelopmental impairments in preterm infants. This result focuses attention on brain growth and cortical development during the weeks following preterm delivery as a neural substrate for neurodevelopmental impairment after premature delivery.

  14. Systemic Injection of Neural Stem/progenitor Cells in Mice With Chronic EAE

    OpenAIRE

    Donegà, Matteo; Giusto, Elena; Cossetti, Chiara; Schaeffer, Julia; Pluchino, Stefano

    2014-01-01

    Neural stem/precursor cells (NPCs) are a promising stem cell source for transplantation approaches aiming at brain repair or restoration in regenerative neurology. This directive has arisen from the extensive evidence that brain repair is achieved after focal or systemic NPC transplantation in several pre clinical models of neurological diseases.

  15. Risk factors and birth prevalence of birth defects and inborn errors of ...

    African Journals Online (AJOL)

    raoul

    2011-02-23

    Feb 23, 2011 ... methylmalonic aciduria, and maple syrup urine disease (MSUD) had their diagnoses confirmed by enzyme assay. The diagnosis of all ... Personal information like date of birth, sex, area of residence, mother's age at birth, father's age, order of birth, birth weight, gestational age on birth, medical history and ...

  16. Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays

    Directory of Open Access Journals (Sweden)

    Fei Chen

    2013-01-01

    Full Text Available This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabilization of the closed-loop system. A numerical example is illustrated to verify the efficiency of the proposed technique.

  17. Implantable Neural Interfaces for Sharks

    Science.gov (United States)

    2007-05-01

    technology for recording and stimulating from the auditory and olfactory sensory nervous systems of the awake, swimming nurse shark , G. cirratum (Figures...overlay of the central nervous system of the nurse shark on a horizontal MR image. Implantable Neural Interfaces for Sharks ...Neural Interfaces for Characterizing Population Responses to Odorants and Electrical Stimuli in the Nurse Shark , Ginglymostoma cirratum.” AChemS Abs

  18. Prevalentie, klinisch beeld en prognose van neuralebuisdefecten in Nederland [Prevalence, presentation and prognosis of neural tube defects in the Netherlands

    NARCIS (Netherlands)

    Ouden, A.L. den; Hirasing, R.A.; Buitendijk, S.E.; Jong-van de Berg, L.T.W. de; Walle, H.E.K. de; Cornel, M.C.

    1996-01-01

    Objective. To determine the live birth prevalence of neural tube defects (NTD) in the Netherlands and describe the clinical picture. Design. Descriptive. Setting. TNO Prevention and Health, Leiden, the Netherlands. Method. Data collected through active surveillance of NTD on a monthly basis by

  19. Toward a distributed free-floating wireless implantable neural recording system.

    Science.gov (United States)

    Pyungwoo Yeon; Xingyuan Tong; Byunghun Lee; Mirbozorgi, Abdollah; Ash, Bruce; Eckhardt, Helmut; Ghovanloo, Maysam

    2016-08-01

    To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm3 neural recording probes, floating with the brain and operating wirelessly across the cortical surface. For powering FF-WINeR probes, a 3-coil link with an intermediate high-Q resonator provides a minimum S21 of -22.22 dB (in the body medium) and -21.23 dB (in air) at 2.8 cm coil separation, which translates to 0.76%/759 μW and 0.6%/604 μW of power transfer efficiency (PTE) / power delivered to a 9 kΩ load (PDL), in body and air, respectively. A mock-up FF-WINeR is implemented to explore microassembly method of the 1×1 mm2 micromachined silicon die with a bonding wire-wound coil and a tungsten micro-wire electrode. Circuit design methods to fit the active circuitry in only 0.96 mm2 of die area in a 130 nm standard CMOS process, and satisfy the strict power and performance requirements (in simulations) are discussed.

  20. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  1. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

  2. A Pleasing Birth

    NARCIS (Netherlands)

    Vries, De Raymond

    2005-01-01

    Women have long searched for a pleasing birth-a birth with a minimum of fear and pain, in the company of supportive family, friends, and caregivers, a birth that ends with a healthy mother and baby gazing into each other's eyes. For women in the Netherlands, such a birth is defined as one at home

  3. A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

    Science.gov (United States)

    de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

    2018-07-01

    The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. The findings of this

  4. A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

    Directory of Open Access Journals (Sweden)

    Jilin Zhang

    2017-01-01

    Full Text Available With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

  5. Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction

    Energy Technology Data Exchange (ETDEWEB)

    Karri, Vishy; Ho, Tien [School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001 (Australia); Madsen, Ole [Department of Production, Aalborg University, Fibigerstraede 16, DK-9220 Aalborg (Denmark)

    2008-06-15

    Hydrogen is increasingly investigated as an alternative fuel to petroleum products in running internal combustion engines and as powering remote area power systems using generators. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the percentage of explosive limits, flow rates and production pressure. This paper investigates the use of model based virtual sensors (rather than expensive physical sensors) in connection with hydrogen production with a Hogen 20 electrolyzer system. The virtual sensors are used to predict relevant hydrogen safety parameters, such as the percentage of lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions of power supplied (voltage and current), the feed of de-ionized water and Hogen 20 electrolyzer system parameters. The virtual sensors are developed by means of the application of various Artificial Intelligent techniques. To train and appraise the neural network models as virtual sensors, the Hogen 20 electrolyzer is instrumented with necessary sensors to gather experimental data which together with MATLAB neural networks toolbox and tailor made adaptive neuro-fuzzy inference systems (ANFIS) were used as predictive tools to estimate hydrogen safety parameters. It was shown that using the neural networks hydrogen safety parameters were predicted to less than 3% of percentage average root mean square error. The most accurate prediction was achieved by using ANFIS. (author)

  6. Neuroimaging of decoding and language comprehension in young very low birth weight (VLBW adolescents: Indications for compensatory mechanisms.

    Directory of Open Access Journals (Sweden)

    Helene van Ettinger-Veenstra

    Full Text Available In preterm children with very low birth weight (VLBW ≤ 1500 g, reading problems are often observed. Reading comprehension is dependent on word decoding and language comprehension. We investigated neural activation-within brain regions important for reading-related to components of reading comprehension in young VLBW adolescents in direct comparison to normal birth weight (NBW term-born peers, with the use of functional magnetic resonance imaging (fMRI. We hypothesized that the decoding mechanisms will be affected by VLBW, and expect to see increased neural activity for VLBW which may be modulated by task performance and cognitive ability. The study investigated 13 (11 included in fMRI young adolescents (ages 12 to 14 years born preterm with VLBW and in 13 NBW controls (ages 12-14 years for performance on the Block Design and Vocabulary subtests of the Wechsler Intelligence Scale for Children; and for semantic, orthographic, and phonological processing during an fMRI paradigm. The VLBW group showed increased phonological activation in left inferior frontal gyrus, decreased orthographic activation in right supramarginal gyrus, and decreased semantic activation in left inferior frontal gyrus. Block Design was related to altered right-hemispheric activation, and VLBW showed lower WISC Block Design scores. Left angular gyrus showed activation increase specific for VLBW with high accuracy on the semantic test. Young VLBW adolescents showed no accuracy and reaction time performance differences on our fMRI language tasks, but they did exhibit altered neural activation during these tasks. This altered activation for VLBW was observed as increased activation during phonological decoding, and as mainly decreased activation during orthographic and semantic processing. Correlations of neural activation with accuracy on the semantic fMRI task and with decreased WISC Block Design performance were specific for the VLBW group. Together, results suggest

  7. Comparable mechanisms for action and language: Neural systems behind intentions, goals and means

    NARCIS (Netherlands)

    Schie, H.T. van; Toni, I.; Bekkering, H.

    2006-01-01

    In this position paper we explore correspondence between neural systems for language and action starting from recent electrophysiological findings on the roles of posterior and frontal areas in goal-directed grasping actions. The paper compares the perceptual and motor organization for action and

  8. Ablation of cholesterol biosynthesis in neural stem cells increases their VEGF expression and angiogenesis but causes neuron apoptosis.

    Science.gov (United States)

    Saito, Kanako; Dubreuil, Veronique; Arai, Yoko; Wilsch-Bräuninger, Michaela; Schwudke, Dominik; Saher, Gesine; Miyata, Takaki; Breier, Georg; Thiele, Christoph; Shevchenko, Andrej; Nave, Klaus-Armin; Huttner, Wieland B

    2009-05-19

    Although sufficient cholesterol supply is known to be crucial for neurons in the developing mammalian brain, the cholesterol requirement of neural stem and progenitor cells in the embryonic central nervous system has not been addressed. Here we have conditionally ablated the activity of squalene synthase (SQS), a key enzyme for endogenous cholesterol production, in the neural stem and progenitor cells of the ventricular zone (VZ) of the embryonic mouse brain. Mutant embryos exhibited a reduced brain size due to the atrophy of the neuronal layers, and died at birth. Analyses of the E11.5-E15.5 dorsal telencephalon and diencephalon revealed that this atrophy was due to massive apoptosis of newborn neurons, implying that this progeny of the SQS-ablated neural stem and progenitor cells was dependent on endogenous cholesterol biosynthesis for survival. Interestingly, the neural stem and progenitor cells of the VZ, the primary target of SQS inactivation, did not undergo significant apoptosis. Instead, vascular endothelial growth factor (VEGF) expression in these cells was strongly upregulated via a hypoxia-inducible factor-1-independent pathway, and angiogenesis in the VZ was increased. Consistent with an increased supply of lipoproteins to these cells, the level of lipid droplets containing triacylglycerides with unsaturated fatty acyl chains was found to be elevated. Our study establishes a direct link between intracellular cholesterol levels, VEGF expression, and angiogenesis. Moreover, our data reveal a hitherto unknown compensatory process by which the neural stem and progenitor cells of the developing mammalian brain evade the detrimental consequences of impaired endogenous cholesterol biosynthesis.

  9. Stability of a neural predictive controller scheme on a neural model

    DEFF Research Database (Denmark)

    Luther, Jim Benjamin; Sørensen, Paul Haase

    2009-01-01

    In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system.......In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

  10. Randomized trial of BCG vaccination at birth to low-birth-weight children

    DEFF Research Database (Denmark)

    Aaby, Peter; Roth, Adam Anders Edvin; Ravn, Henrik

    2011-01-01

    Observational studies have suggested that BCG may have nonspecific beneficial effects on survival. Low-birth-weight (LBW) children are not given BCG at birth in Guinea-Bissau; we conducted a randomized trial of BCG at birth (early BCG) vs delayed BCG.......Observational studies have suggested that BCG may have nonspecific beneficial effects on survival. Low-birth-weight (LBW) children are not given BCG at birth in Guinea-Bissau; we conducted a randomized trial of BCG at birth (early BCG) vs delayed BCG....

  11. Controlling selective stimulations below a spinal cord hemisection using brain recordings with a neural interface system approach

    Science.gov (United States)

    Panetsos, Fivos; Sanchez-Jimenez, Abel; Torets, Carlos; Largo, Carla; Micera, Silvestro

    2011-08-01

    In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions.

  12. Planned hospital birth versus planned home birth

    DEFF Research Database (Denmark)

    Olsen, O.; Clausen, J.A.

    2012-01-01

    Observational studies of increasingly better quality and in different settings suggest that planned home birth in many places can be as safe as planned hospital birth and with less intervention and fewer complications. This is an update of a Cochrane review first published in 1998....

  13. Maternal bereavement in the antenathal period and Neural tube defect in the offspring

    DEFF Research Database (Denmark)

    Ingstrup, Katja Glejsted; Olsen, Jørn; Bech, Bodil Hammer

    2013-01-01

    Title: Maternal bereavement after death of a close relative and neural tube defect in the offspring Background: Neural tube defects are the second most common and often lethal congenital anomaly in the world leaving surviving children with life-long severe disabilities. A low intake of folic acid...... was seen (OR 1.61, 95% CI: 1.07; 2.41). Discussion: We only studied live born children but about 2/3 of children with spina bifida survive the birth or longer with corrective surgery. We did not adjust for folic acid, but a sub-analysis of approximately 85,000 mothers showed no difference in intake during...... all children born in Denmark from 1978-2008 and their mothers (n=1,734,190). In the time window of one year before pregnancy or during the first trimester of pregnancy 34,407 mothers were exposed to bereavement. Results: A total of 5,031 cases of neural tube defects were identified: 889 with spina...

  14. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  15. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

  16. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  17. The Effect of Activity Restriction on Infant's Birth Weight and Gestational Age at Birth: PRAMS Data Analysis.

    Science.gov (United States)

    Omar, Abeer

    2018-01-01

    Activity restriction is extensively prescribed for pregnant women with major comorbidities despite the lack of evidence to support its effectiveness in preventing preterm birth or low birth weight. To determine the moderation effect of home activity restriction for more than a week on infant's birth weight and gestational age at birth for high-risk women with obstetrical and medical comorbidities. A secondary analysis of 2004-2008 New York Pregnancy Risk Assessment Monitoring System was conducted with 1426 high-risk women. High-risk group included 41% of women treated with activity restriction and 59% of those not treated with activity restriction. Women with preterm premature rupture of membrane (PPROM) who were treated with activity restriction had a lower infant birth weight ( b = -202.85, p = ≤.001) and gestational age at birth ( b = -.91, p = ≤.001) than those without activity restriction. However, women with preterm labor and hypertensive disorders of pregnancy who were not treated with activity restriction had lower infant gestational age at birth ( b = -96, p = ≤.01) and ( b = -92, p = ≤.001), respectively, compared to those who were treated with activity restriction. Findings suggest a contrary effect of activity restriction on infants born to women with PPROM, which is a major reason for prescribing activity restriction. The current study results may trigger the need to conduct randomized control trials to determine the effect of severity of activity restriction on maternal and infant outcomes.

  18. Beating Birth Defects

    Centers for Disease Control (CDC) Podcasts

    Each year in the U.S., one in 33 babies is affected by a major birth defect. Women can greatly improve their chances of giving birth to a healthy baby by avoiding some of the risk factors for birth defects before and during pregnancy. In this podcast, Dr. Stuart Shapira discusses ways to improve the chances of giving birth to a healthy baby.

  19. Experiences of women who planned birth in a birth centre compared to alternative planned places of birth. Results of the Dutch Birth Centre Study

    NARCIS (Netherlands)

    Hitzert, M.; Hermes, M.A.; Scheerhagen, M.; Boesveld, L.C.; Wiegers, T.A.; Akker-van Marle, M.E.; Dommelen, P. van; Pal-de Bruin, K.M. de; Graaf, J.P. de

    2016-01-01

    Objective to assess the experiences with maternity care of women who planned birth in a birth centre and to compare them to alternative planned places of birth, by using the responsiveness concept of the World Health Organization. Design this study is a cross-sectional study using the ReproQ

  20. Experiences of women who planned birth in a birth centre compared to alternative planned places of birth. Results of the Dutch Birth Centre Study.

    NARCIS (Netherlands)

    Hitzert, M.; Hermus, M.; Scheerhagen, M.; Boesveld, I.C.; Wiegers, T.; Akker-van Marle, M.E. van den; Dommelen, P. van; Pal-de Bruin, K.M. van der; Graal, J. P. de

    2016-01-01

    Objective: to assess the experiences with maternity care of women who planned birth in a birth centre and to compare them to alternative planned places of birth, by using the responsiveness concept of the World Health Organization. Design: this study is a cross-sectional study using the ReproQ

  1. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  2. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  3. Embedding recurrent neural networks into predator-prey models.

    Science.gov (United States)

    Moreau, Yves; Louiès, Stephane; Vandewalle, Joos; Brenig, Leon

    1999-03-01

    We study changes of coordinates that allow the embedding of ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models-also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form (Brenig, L. (1988). Complete factorization and analytic solutions of generalized Lotka-Volterra equations. Physics Letters A, 133(7-8), 378-382), where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoid. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network. We expect that this transformation will permit the application of existing techniques for the analysis of Lotka-Volterra systems to recurrent neural networks. Furthermore, our results show that Lotka-Volterra systems are universal approximators of dynamical systems, just as are continuous-time neural networks.

  4. Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network.

    Science.gov (United States)

    Wang, Yunpeng; Cheng, Long; Hou, Zeng-Guang; Yu, Junzhi; Tan, Min

    2016-02-01

    The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.

  5. An Intelligent Neural Stem Cell Delivery System for Neurodegenerative Diseases Treatment.

    Science.gov (United States)

    Qiao, Shupei; Liu, Yi; Han, Fengtong; Guo, Mian; Hou, Xiaolu; Ye, Kangruo; Deng, Shuai; Shen, Yijun; Zhao, Yufang; Wei, Haiying; Song, Bing; Yao, Lifen; Tian, Weiming

    2018-05-02

    Transplanted stem cells constitute a new therapeutic strategy for the treatment of neurological disorders. Emerging evidence indicates that a negative microenvironment, particularly one characterized by the acute inflammation/immune response caused by physical injuries or transplanted stem cells, severely impacts the survival of transplanted stem cells. In this study, to avoid the influence of the increased inflammation following physical injuries, an intelligent, double-layer, alginate hydrogel system is designed. This system fosters the matrix metalloproeinases (MMP) secreted by transplanted stem cell reactions with MMP peptide grafted on the inner layer and destroys the structure of the inner hydrogel layer during the inflammatory storm. Meanwhile, the optimum concentration of the arginine-glycine-aspartate (RGD) peptide is also immobilized to the inner hydrogels to obtain more stem cells before arriving to the outer hydrogel layer. It is found that blocking Cripto-1, which promotes embryonic stem cell differentiation to dopamine neurons, also accelerates this process in neural stem cells. More interesting is the fact that neural stem cell differentiation can be conducted in astrocyte-differentiation medium without other treatments. In addition, the system can be adjusted according to the different parameters of transplanted stem cells and can expand on the clinical application of stem cells in the treatment of this neurological disorder. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Reforming birth registration law in England and Wales?

    Directory of Open Access Journals (Sweden)

    Julie McCandless

    2017-06-01

    Full Text Available The Law Commission of England and Wales is considering what its 13th Programme of Law Reform should address. During the consultation process, a project on birth registration law has been mooted. This is a very welcome proposal given that civil birth registration in England and Wales is a compulsory procedure that not only finds its roots in the early Victorian era, but also remains very similar, at least in terms of form and the information that is recorded. I first use two recent legal challenges to illustrate why the current system is coming under increasing pressure. I further use these examples to caution against a law reform agenda that is narrowly focused on the precise information recorded, without a preliminary and wider examination of what the role and purpose of birth registration is, and should be, in society. I argue that this needs to be addressed before the state can justify the parameters of the information recorded. I then use an outline of historical reforms relating to the registration of births outside of marriage to highlight the normative two-parent family model that underpins the birth registration system. I argue that legal reform must be cognizant of the tenacity of this normative family model, particularly in relation to reform proposals surrounding donor conception and the annotation of birth certificates. Finally, I draw attention to wider developments in family law that cast birth registration as a social policy tool for the facilitation of parent–child relationships, particularly unmarried fathers.

  7. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    Directory of Open Access Journals (Sweden)

    Y.-M. Chiang

    2011-01-01

    Full Text Available Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  8. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  9. Pattern recognition of state variables by neural networks

    International Nuclear Information System (INIS)

    Faria, Eduardo Fernandes; Pereira, Claubia

    1996-01-01

    An artificial intelligence system based on artificial neural networks can be used to classify predefined events and emergency procedures. These systems are being used in different areas. In the nuclear reactors safety, the goal is the classification of events whose data can be processed and recognized by neural networks. In this works we present a preliminary simple system, using neural networks in the recognition of patterns the recognition of variables which define a situation. (author)

  10. Criticality and avalanches in neural networks

    International Nuclear Information System (INIS)

    Zare, Marzieh; Grigolini, Paolo

    2013-01-01

    Highlights: • Temporal criticality is used as criticality indicator. • The Mittag–Leffler function is proposed as a proper form of temporal complexity. • The distribution of avalanche size becomes scale free in the supercritical state. • The scale-free distribution of avalanche sizes is an epileptic manifestation. -- Abstract: Experimental work, both in vitro and in vivo, reveals the occurrence of neural avalanches with an inverse power law distribution in size and time duration. These properties are interpreted as an evident manifestation of criticality, thereby suggesting that the brain is an operating near criticality complex system: an attractive theoretical perspective that according to Gerhard Werner may help to shed light on the origin of consciousness. However, a recent experimental observation shows no clear evidence for power-law scaling in awake and sleeping brain of mammals, casting doubts on the assumption that the brain works at criticality. This article rests on a model proposed by our group in earlier publications to generate neural avalanches with the time duration and size distribution matching the experimental results on neural networks. We now refine the analysis of the time distance between consecutive firing bursts and observe the deviation of the corresponding distribution from the Poisson statistics, as the system moves from the non-cooperative to the cooperative regime. In other words, we make the assumption that the genuine signature of criticality may emerge from temporal complexity rather than from the size and time duration of avalanches. We argue that the Mittag–Leffler (ML) exponential function is a satisfactory indicator of temporal complexity, namely of the occurrence of non-Poisson and renewal events. The assumption that the onset of criticality corresponds to the birth of renewal non-Poisson events establishes a neat distinction between the ML function and the power law avalanches generating regime. We find that

  11. Artificial Neural Network Application for Power Transfer Capability and Voltage Calculations in Multi-Area Power System

    Directory of Open Access Journals (Sweden)

    Palukuru NAGENDRA

    2010-12-01

    Full Text Available In this study, the use of artificial neural network (ANN based model, multi-layer perceptron (MLP network, to compute the transfer capabilities in a multi-area power system was explored. The input for the ANN is load status and the outputs are the transfer capability among the system areas, voltage magnitudes and voltage angles at concerned buses of the areas under consideration. The repeated power flow (RPF method is used in this paper for calculating the power transfer capability, voltage magnitudes and voltage angles necessary for the generation of input-output patterns for training the proposed MLP neural network. Preliminary investigations on a three area 30-bus system reveal that the proposed model is computationally faster than the conventional method.

  12. Where There Are (Few) Skilled Birth Attendants

    Science.gov (United States)

    Prata, Ndola; Rowen, Tami; Bell, Suzanne; Walsh, Julia; Potts, Malcolm

    2011-01-01

    Recent efforts to reduce maternal mortality in developing countries have focused primarily on two long-term aims: training and deploying skilled birth attendants and upgrading emergency obstetric care facilities. Given the future population-level benefits, strengthening of health systems makes excellent strategic sense but it does not address the immediate safe-delivery needs of the estimated 45 million women who are likely to deliver at home, without a skilled birth attendant. There are currently 28 countries from four major regions in which fewer than half of all births are attended by skilled birth attendants. Sixty-nine percent of maternal deaths in these four regions can be attributed to these 28 countries, despite the fact that these countries only constitute 34% of the total population in these regions. Trends documenting the change in the proportion of births accompanied by a skilled attendant in these 28 countries over the last 15-20 years offer no indication that adequate change is imminent. To rapidly reduce maternal mortality in regions where births in the home without skilled birth attendants are common, governments and community-based organizations could implement a cost-effective, complementary strategy involving health workers who are likely to be present when births in the home take place. Training community-based birth attendants in primary and secondary prevention technologies (e.g. misoprostol, family planning, measurement of blood loss, and postpartum care) will increase the chance that women in the lowest economic quintiles will also benefit from global safe motherhood efforts. PMID:21608417

  13. A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

    Science.gov (United States)

    Lin, Chin-Teng; Wu, Rui-Cheng; Chang, Jyh-Yeong; Liang, Sheng-Fu

    2004-02-01

    In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

  14. Recycling signals in the neural crest

    OpenAIRE

    Taneyhill, Lisa A.; Bronner-Fraser, Marianne E.

    2006-01-01

    Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

  15. Recycling signals in the neural crest.

    Science.gov (United States)

    Taneyhill, Lisa A; Bronner-Fraser, Marianne

    2005-01-01

    Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

  16. A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

    Directory of Open Access Journals (Sweden)

    Yong Tao

    2016-01-01

    Full Text Available A sliding mode control method based on radial basis function (RBF neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

  17. No excess risk of adverse birth outcomes in populations living near special waste landfill sites in Scotland.

    Science.gov (United States)

    Morris, S E; Thomson, A O; Jarup, L; de Hoogh, C; Briggs, D J; Elliott, P

    2003-11-01

    A recent study showed small excess risks of low birth weight, very low birth weight and certain congenital anomalies in populations living near landfill sites in Great Britain. The objective of the current study was to investigate the risk of adverse birth outcomes associated with residence near special waste landfill sites in Scotland. We studied risks of adverse birth outcomes in populations living within 2 km of 61 Scottish special waste landfill sites operational at some time between 1982 and 1997 compared with those living further away. 324,167 live births, 1,849 stillbirths, and 11,138 congenital anomalies (including terminations) were included in the study. Relative risks were computed for all congenital anomalies combined, some specific anomalies and prevalence of stillbirth and low and very low birth weight (special waste landfill sites was 0.96 (99% confidence interval 0.89 to 1.02) adjusted for confounders. Adjusted risks were 0.71 (0.36 to 1.42) for neural tube defects, 1.03 (0.85 to 1.26) for cardiovascular defects, 0.84 (0.58 to 1.22) for hypospadias and epispadias (with no excess of surgical corrections), 0.78 (0.27 to 2.23) for abdominal wall defects (1.32 (0.42-4.17) for hospital admissions), 1.22 (0.28 to 5.38) for surgical correction of gastroschisis and exomphalos and 1.01 (0.96 to 1.07) and 1.01 (0.90 to 1.15) for low and very low birth weight respectively. There was no excess risk of stillbirth. In conclusion, we found no statistically significant excess risks of congenital anomalies or low birth weight in populations living near special waste landfill sites in Scotland.

  18. CDC WONDER: Births

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Births (Natality) online databases in CDC WONDER report birth rates, fertility rates and counts of live births occurring within the United States to U.S....

  19. Vaginal birth after cesarean: neonatal outcomes and United States birth setting.

    Science.gov (United States)

    Tilden, Ellen L; Cheyney, Melissa; Guise, Jeanne-Marie; Emeis, Cathy; Lapidus, Jodi; Biel, Frances M; Wiedrick, Jack; Snowden, Jonathan M

    2017-04-01

    Women who seek vaginal birth after cesarean delivery may find limited in-hospital options. Increasing numbers of women in the United States are delivering by vaginal birth after cesarean delivery out-of-hospital. Little is known about neonatal outcomes among those who deliver by vaginal birth after cesarean delivery in- vs out-of-hospital. The purpose of this study was to compare neonatal outcomes between women who deliver via vaginal birth after cesarean delivery in-hospital vs out-of-hospital (home and freestanding birth center). We conducted a retrospective cohort study using 2007-2010 linked United States birth and death records to compare singleton, term, vertex, nonanomolous, and liveborn neonates who delivered by vaginal birth after cesarean delivery in- or out-of-hospital. Descriptive statistics and multivariate regression analyses were conducted to estimate unadjusted, absolute, and relative birth-setting risk differences. Analyses were stratified by parity and history of vaginal birth. Sensitivity analyses that involved 3 transfer status scenarios were conducted. Of women in the United States with a history of cesarean delivery (n=1,138,813), only a small proportion delivered by vaginal birth after cesarean delivery with the subsequent pregnancy (n=109,970; 9.65%). The proportion of home vaginal birth after cesarean delivery births increased from 1.78-2.45%. A pattern of increased neonatal morbidity was noted in unadjusted analysis (neonatal seizures, Apgar score birthing their second child by vaginal birth after cesarean delivery in out-of-hospital settings had higher odds of neonatal morbidity and death compared with women of higher parity. Women who had not birthed vaginally prior to out-of-hospital vaginal birth after cesarean delivery had higher odds of neonatal morbidity and mortality compared with women who had birthed vaginally prior to out-of-hospital vaginal birth after cesarean delivery. Sensitivity analyses generated distributions of plausible

  20. Fractals and the birth of Gothic: reflections on the biologic basis of creativity.

    Science.gov (United States)

    Goldberger, A L

    1996-05-01

    The birth of Gothic, one of the great triumphs of human spiritual and artistic expression, would appear to be a topic remote from the enterprise of contemporary neurobiology. The intent of this brief essay is to explore the possibility that this singular architectural movement may have important implications for understanding the nonlinear dynamics of the brain. We explore the hypothesis that the Gothic cathedral, with its porous, scale-free structures, may represent an externalization of the fractal properties of our physiology in general, and of our neural architectures and neuro-dynamics, in particular.

  1. Long term trends in prevalence of neural tube defects in Europe

    DEFF Research Database (Denmark)

    Khoshnood, Babak; Loane, Maria; Walle, Hermien de

    2015-01-01

    STUDY QUESTION: What are the long term trends in the total (live births, fetal deaths, and terminations of pregnancy for fetal anomaly) and live birth prevalence of neural tube defects (NTD) in Europe, where many countries have issued recommendations for folic acid supplementation but a policy...... for mandatory folic acid fortification of food does not exist? METHODS: This was a population based, observational study using data on 11 353 cases of NTD not associated with chromosomal anomalies, including 4162 cases of anencephaly and 5776 cases of spina bifida from 28 EUROCAT (European Surveillance......-conceptional folic acid supplementation and existence of voluntary folic acid fortification. FUNDING, COMPETING INTERESTS, DATA SHARING: The study was funded by the European Public Health Commission, EUROCAT Joint Action 2011-2013. HD and ML received support from the European Commission DG Sanco during the conduct...

  2. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  3. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems

    Directory of Open Access Journals (Sweden)

    Sun-Il Chang

    2018-01-01

    Full Text Available This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM module. The core integrated circuit (IC consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm2 and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  4. Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

    Energy Technology Data Exchange (ETDEWEB)

    Olyaee, Saeed; Hamedi, Samaneh, E-mail: s_olyaee@srttu.edu [Nano-photonics and Optoelectronics Research Laboratory (NORLab), Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Lavizan, 16788, Tehran (Iran, Islamic Republic of)

    2011-02-01

    In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

  5. Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

    International Nuclear Information System (INIS)

    Olyaee, Saeed; Hamedi, Samaneh

    2011-01-01

    In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

  6. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....

  7. Planned and unplanned home births and hospital births in Calgary, Alberta, 1984-87.

    Science.gov (United States)

    Abernathy, T J; Lentjes, D M

    1989-01-01

    Information collected on all home births in Calgary (Canada) between the years 1984 and 1987, was examined and analyzed according to whether the home birth environment had been planned or unplanned. The two groups were compared to each other and to all hospital births according to demographic characteristics of mothers, indicators of prenatal care, and birth outcome. Mothers who had planned their home birth were more likely to be primiparous, attend prenatal classes, obtain regular prenatal care from a physician, and have babies with a higher birth weight than either the unplanned or hospital group. Of particular concern, however, were the subset of unplanned home births who were primiparous. These mothers attended prenatal classes less frequently than any other group, reported the lowest number of physician visits, were youngest, and least likely to be married. In addition their babies averaged the shortest gestational age and the lowest birth weight. Findings in general show that planned and unplanned home births must be considered as heterogeneous groups in any comparison of risk factors and of birth outcome between home and hospital births. Further, within the unplanned group, multiparous women differ from primiparous women. Given the limitations inherent in this and similar studies, the apparent better outcome in the planned home birth group, as measured by birth weight, must be viewed with caution.

  8. Development of automated system based on neural network algorithm for detecting defects on molds installed on casting machines

    Science.gov (United States)

    Bazhin, V. Yu; Danilov, I. V.; Petrov, P. A.

    2018-05-01

    During the casting of light alloys and ligatures based on aluminum and magnesium, problems of the qualitative distribution of the metal and its crystallization in the mold arise. To monitor the defects of molds on the casting conveyor, a camera with a resolution of 780 x 580 pixels and a shooting rate of 75 frames per second was selected. Images of molds from casting machines were used as input data for neural network algorithm. On the preparation of a digital database and its analytical evaluation stage, the architecture of the convolutional neural network was chosen for the algorithm. The information flow from the local controller is transferred to the OPC server and then to the SCADA system of foundry. After the training, accuracy of neural network defect recognition was about 95.1% on a validation split. After the training, weight coefficients of the neural network were used on testing split and algorithm had identical accuracy with validation images. The proposed technical solutions make it possible to increase the efficiency of the automated process control system in the foundry by expanding the digital database.

  9. Impacto de la fortificación de alimentos con ácido fólico en los defectos del tubo neural en Costa Rica Impact of the fortification of food with folic acid on neural tube defects in Costa Rica

    Directory of Open Access Journals (Sweden)

    María de la Paz Barboza Argüello

    2011-07-01

    Full Text Available OBJETIVO: Evaluar el impacto de la fortificación de alimentos con ácido fólico en las tendencias de las prevalencias de los defectos del tubo neural (DTN y la tasa de mortalidad infantil (TMI por este trastorno en Costa Rica. MÉTODOS: Se analizaron los datos de vigilancia del Centro de Registro de Enfermedades Congénitas y el Centro Centroamericano de Población. Se consideraron defectos del tubo neural la anencefalia, la espina bífida y el encefalocele. Se examinaron las tendencias durante 1987-2009, así como las diferencias de tasas (intervalo de confianza [IC] 95% de prevalencia y mortalidad antes de la fortificación de alimentos con ácido fólico y hasta 12 años después de su implementación. Se determinó el aporte de la fortificación al descenso en la TMI general. RESULTADOS: En 1987-1997, previo al período de fortificación de alimentos con ácido fólico, la prevalencia de DTN fue de 12/10 000 nacidos (IC95%: 11,1-12,8, mientras que en 2009 fue de 5,1/10 000 nacidos (3,3-6,5. La TMI por DTN en 1997 fue de 0,64/1 000 nacimientos (46-0,82 y en 2009 de 0,19/1 000 (0,09-0,3. La TMI por DTN y su prevalencia disminuyeron en forma significativa, 71% y 58% respectivamente (P OBJECTIVE: Evaluate the impact of the fortification of food with folic acid on prevalence trends for neural tube defects (NTD and the infant mortality rate (IMR associated with this disorder in Costa Rica. METHODS: The surveillance data from the Congenital Disease Registry Center and the Central American Population Center were analyzed. The neural tube defects considered were anencephaly, spina bifida, and encephalocele. The trends from 1987-2009, as well as the differences in prevalence and mortality rates prior to and up to 12 years after food fortification with folic acid, were examined (95% confidence interval [CI]. The contribution of fortification to the decrease in the overall IMR was determined. RESULTS: During 1987-1997, prior to the period of food

  10. Extremely Preterm Birth

    Science.gov (United States)

    ... Events Advocacy For Patients About ACOG Extremely Preterm Birth Home For Patients Search FAQs Extremely Preterm Birth ... Spanish FAQ173, June 2016 PDF Format Extremely Preterm Birth Pregnancy When is a baby considered “preterm” or “ ...

  11. Improvement in the determination of elemental concentrations in PIXE analyses using artificial neural system

    International Nuclear Information System (INIS)

    Correa, R.; Dinator, M.I.; Morales, J.R.; Miranda, P.A.; Cancino, S.A.; Vila, I.; Requena, I.

    2008-01-01

    An Artificial Neural System, ANS, has been designed to operate in the analysis of spectra obtained from a PIXE (Proton Induced X-ray Emissions) application. The special designed ANS was used in the calculation of the concentrations of the major elements in the samples. Neural systems using several feed-forward ANN of similar topology working in parallel were trained with error back propagation algorithm using sets of spectra of known elemental concentrations. Following the training phase of the neural networks, other PIXE spectra were analyzed with this methodology providing unknown elemental concentrations. ANS results were compared with results obtained by traditional computer codes like AXIL and GUPIX, obtaining correlations factors close to one. The rather short time required to process each spectrum, of the order of microseconds, allows fast analysis of a large number of samples. Here we present applications of ANS in the PIXE analyses of samples of organic nature like liver, gills and muscle from fishes. ANS results were compared with elemental concentrations obtained in a previous application where a single ANN was used for each analyzed element. PIXE analyses were performed at the Nuclear Physics Laboratory of the University of Chile, using 2.2 MeV proton beams provided by a Van de Graaff accelerator. (author)

  12. Thermodynamic analysis of an open cycle solid desiccant cooling system using Artificial Neural Network

    International Nuclear Information System (INIS)

    Koronaki, I.P.; Rogdakis, E.; Kakatsiou, T.

    2012-01-01

    Highlights: ► A neural network model based on experimental data was developed. ► Description of the experimental setup. ► Prediction of the state conditions of air at the process and regeneration stream. ► Sensitivity Analysis performed on these predicted results. ► Predicted output values in line with correlation model based on data from industry. - Abstract: This paper examines the performance of an installed open cycle air-conditioning system with a silica gel desiccant wheel which uses a conventional heat pump and heat exchangers for the improvement of the outlet air of the system. A neural network model based on the training of a black box model with experimental data was developed as a method based on experimental results predicting the state conditions of air at the process and regeneration stream. The model development was followed by a Sensitivity Analysis performed on these predicted results. The key parameters were the thermodynamic condition of process and regeneration air streams, the sensible heat factor of the room, and the mass air flow ratio of the regeneration and process streams. The results of this analysis revealed that all investigated parameters influenced the performance of the desiccant unit. Predicted output values of the proposed Neural Network Model for Desiccant Systems are in line with results from other correlation models based on the interpolation of experimental data obtained from industrial air conditioning installations.

  13. The application of expert systems and neural networks to gas turbine prognostics and diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    DePold, H.R.; Gass, F.D.

    1999-10-01

    Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools. Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations. This paper presents recent developments in technology and strategies in engine condition monitoring including: (1) application of statistical analysis and artificial neural network filters to improve data quality, (2) neural networks for trend change detection, and classification to diagnose performance change, and (3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.

  14. Large-scale multielectrode recording and stimulation of neural activity

    International Nuclear Information System (INIS)

    Sher, A.; Chichilnisky, E.J.; Dabrowski, W.; Grillo, A.A.; Grivich, M.; Gunning, D.; Hottowy, P.; Kachiguine, S.; Litke, A.M.; Mathieson, K.; Petrusca, D.

    2007-01-01

    Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions

  15. Neural basis of attachment-caregiving systems interaction:insights from neuroimaging

    Directory of Open Access Journals (Sweden)

    Delia eLenzi

    2015-08-01

    Full Text Available The attachment and the caregiving system are complementary systems which are active simultaneously in infant and mother interactions. This ensures the infant survival and optimal social, emotional and cognitive development. In this brief review we first define the characteristics of these two behavioral systems and the theory that links them, according to what Bowlby called the attachment-caregiving social bond (Bowlby, 1969. We then follow with those neuroimaging studies that have focused on this particular issue, i.e. those which have studied the activation of the careging system in women (using infant stimuli and have explored how the individual attachment model (through the Adult Attachment Interview modulates its activity. Studies report altered activation in limbic and prefrontal areas and in basal ganglia and hypothalamus/pituitary regions. These altered activations are thought to be the neural substrate of the attachment-caregiving systems interaction.

  16. Preterm Birth and Low Birth Weight Following Icsi- Pregnancies

    OpenAIRE

    Aygül Demirol; Süleyman Güven; Timur Gürgan

    2006-01-01

    OBJECTIVE: To report preterm birth and low birth weight rate of intracytoplasmic sperm injection (ICSI) related pregnancies and to compare our data with literature findings. STUDY DESIGN: Three-hundred and eighty-nine pregnancies following controlled ovarian hyperstimulation and intracytoplasmic sperm injection were retrospectively evaluated. Patients’ characteristics including age, gestational age at delivery and birth weight were noted from special clinic files. Women with early pregnanc...

  17. Relationship between birth order and birth weight of the pig

    OpenAIRE

    Charneca, Rui; Freitas, Amadeu; Nunes, José; Le Dividich, Jean

    2013-01-01

    The objective of this study was to determine whether birth weight of the pig is related to its birth order. The study involved 292 sows from 2 genotypes (Large White x Landrace crossbred (LL), n= 247 and Alentejano (AL), n=45) of mixed parity and their piglets. Most sows farrowed naturally. Each piglet was identified, weighed (± 1g) (mummies excepted) and its birth order (BO) recorded within 2 min of birth. A total of 3418 LL and 375 AL piglets were born of which 43 and 7 were mummified, a...

  18. Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

    Directory of Open Access Journals (Sweden)

    Leandro L. S. Linhares

    2015-01-01

    Full Text Available Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS. In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE cost function is replaced by the Maximum Correntropy Criterion (MCC in the traditional error backpropagation (BP algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

  19. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

  20. Effects of infants' birth order, maternal age, and socio-economic status on birth weight.

    Science.gov (United States)

    Ghaemmaghami, Seyed J; Nikniaz, Leila; Mahdavi, Reza; Nikniaz, Zeinab; Razmifard, Farzad; Afsharnia, Farzaneh

    2013-09-01

    To determine the effects of infants' birth order, maternal age, and socioeconomic status (SES) on birth weight. This cross-sectional study included a sample of 858 mothers recruited over a 6-month period in 2010, in a defined population of 9 urban health centers, and who were admitted for their infants' first vaccination. Maternal clinical data, demographic data, and infants' birth weight were obtained from the interview and maternal hospital files. Multiple regression and analysis of variance were used for data analysis. First and fourth births had lower birth weights compared with second and third births in all maternal ages in controlling parity, birth weight increases with maternal age up to the early 24, and then tends to level off. Male gender, maternal age 20-24 years, second and third births had a significant positive effect on birth weight. Lower family economic status and higher educational attainment were significantly associated with lower birth weight. For women in the 15-19 and 40-44 years age groups, the second birth order was associated with the most undesirable effect on birth weight. Accessibility of health care services, parity, maternal age, and socioeconomic factors are strongly associated with infants' birth weight.

  1. 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.

  2. A neural network approach to burst detection.

    Science.gov (United States)

    Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

    2002-01-01

    This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

  3. Adaptive nonlinear control using input normalized neural networks

    International Nuclear Information System (INIS)

    Leeghim, Henzeh; Seo, In Ho; Bang, Hyo Choong

    2008-01-01

    An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small

  4. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

    The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

  5. Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

    Directory of Open Access Journals (Sweden)

    Shu-Min Lu

    2017-01-01

    Full Text Available An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

  6. From institutionalized birth to home birth

    Directory of Open Access Journals (Sweden)

    Clara Fróes de Oliveira Sanfelice

    2014-06-01

    Full Text Available The study aimed to describe the experiences of a group of nurse-midwives from the city of Campinas, SP, Brasil, regarding the transition process from attending institutionalized births to attending home births, in the period 2011 – 2013. The study is of the experience report type; the reflections, perceptions and challenges experienced in this process were collected using the technique of brainstorming. Content analysis, as proposed by Bardin, was used, which yielded four thematic categories: a the hospital experience; b living with obstetric violence; c returning home and d the challenges of home care. It is concluded that attending home births offers greater satisfaction to the nurses, even in the face of various obstacles, as it is possible to offer a care to the woman and new-born which covers both the concept of comprehensiveness and the current scientific recommendations.

  7. FRAMEWORK OF TAILORMADE DRIVING SUPPORT SYSTEMS AND NEURAL NETWORK DRIVER MODEL

    Directory of Open Access Journals (Sweden)

    Toshiya HIROSE, M.S.

    2004-01-01

    Nowadays, tailormade medical treatment is receiving much attention in the field of medical care. It is also desirable for driving support systems to reflect the driving characteristics of individuals as much as possible, begin monitoring the driver when a driver starts driving and calculates the driver model, and supports them with a model that makes the driver feel quite normal. That is the construction of Tailormade Driving Support Systems (TDSS. This research proposes a concept and a framework of TDSS, and presents a driver model that uses a neural network to build the system. As for the feasibility of this system, the research selects braking as a typical constituent element, and illustrates and reviews the results of experiments and simulations.

  8. Neural Systems Underlying Emotional and Non-emotional Interference Processing: An ALE Meta-Analysis of Functional Neuroimaging Studies

    OpenAIRE

    Xu, Min; Xu, Guiping; Yang, Yang

    2016-01-01

    Understanding how the nature of interference might influence the recruitments of the neural systems is considered as the key to understanding cognitive control. Although, interference processing in the emotional domain has recently attracted great interest, the question of whether there are separable neural patterns for emotional and non-emotional interference processing remains open. Here, we performed an activation likelihood estimation meta-analysis of 78 neuroimaging experiments, and exam...

  9. Update on the Essure System for Permanent Birth Control.

    Science.gov (United States)

    Fantasia, Heidi Collins

    In 2002, the U.S. Food and Drug Administration approved the Essure system for permanent birth control. Implantation with this device offers a minimally invasive option for permanent female contraception that is placed during a brief office visit. Unlike laparoscopic tubal sterilization, the Essure procedure requires no hospitalization or general anesthesia, resulting in minimal recovery time. After a decade of stability in the report of adverse effects, the U.S. Food and Drug Administration noted a sharp increase in patient-reported adverse events, including chronic pelvic pain, irregular bleeding, allergic reactions, and autoimmune-like reactions. In response to this increase in complaints, the U.S. Food and Drug Administration issued updated guidelines for patient education and counseling. This article discusses those updates, as well as implications for nurses who provide health care to women seeking permanent contraception. © 2017 AWHONN, the Association of Women’s Health, Obstetric and Neonatal Nurses.

  10. A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

    Directory of Open Access Journals (Sweden)

    Carlos Robles Algarín

    2018-01-01

    Full Text Available This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV module. A nonlinear autoregressive network with exogenous inputs (NARX was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA, and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.

  11. The exploitation of neural networks in automotive engine management systems

    Energy Technology Data Exchange (ETDEWEB)

    Shayler, P.J.; Goodman, M. [University of Nottingham (United Kingdom); Ma, T. [Ford Motor Company, Dagenham (United Kingdom). Research and Engineering Centre

    2000-07-01

    The use of electronic engine control systems on spark ignition engines has enabled a high degree of performance optimisation to be achieved. The range of functions performed by these systems, and the level of performance demanded, is rising and thus so are development times and costs. Neural networks have attracted attention as having the potential to simplify software development and improve the performance of this software. The scope and nature of possible applications is described. In particular, the pattern recognition and classification abilities of networks are applied to crankshaft speed fluctuation data for engine-fault diagnosis, and multidimensional mapping capabilities are investigated as an alternative to large 'lookup' tables and calibration functions. (author)

  12. Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

    Science.gov (United States)

    Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

    2011-04-01

    This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

  13. [Birthing institutions and births in Norwegian counties in the early 1990s].

    Science.gov (United States)

    Bergsjø, P; Daltveit, A K

    1996-05-20

    Between 1972 and 1993 the number of hospitals and maternity homes providing obstetric help in Norway fell from 158 to 67. Most of the decline is explained by the closing down of maternity homes and obstetrical units in small hospitals, partly due to a reduction in number of births and partly to a deliberate drive towards giving birth in larger units. 16 of the 19 counties of Norway contained four or fewer obstetric institutions in 1993. Nevertheless, most of the 60,000 births took place in institutions with between 500 and 2,999 births annually. Births at home accounted for 0.3%, and births during transport for 0.2% of the total in 1990 and 1993.

  14. Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

    Science.gov (United States)

    Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun

    2017-09-25

    This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

  15. Fuzzy neural network theory and application

    CERN Document Server

    Liu, Puyin

    2004-01-01

    This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

  16. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  17. Folic acid supplementation and preterm birth: results from observational studies.

    Science.gov (United States)

    Mantovani, Elena; Filippini, Francesca; Bortolus, Renata; Franchi, Massimo

    2014-01-01

    Folic acid (FA) supplementation is recommended worldwide in the periconceptional period for the prevention of neural tube defects. Due to its involvement in a number of cellular processes, its role in other pregnancy outcomes such as miscarriage, recurrent miscarriage, low birth weight, preterm birth (PTB), preeclampsia, abruptio placentae, and stillbirth has been investigated. PTB is a leading cause of perinatal mortality and morbidity; therefore its association with FA supplementation is of major interest. The analysis of a small number of randomized clinical trials (RCTs) has not found a beneficial role of FA in reducing the rate of PTBs. The aim of this review was to examine the results from recent observational studies about the effect of FA supplementation on PTB. We carried out a search on Medline and by manual search of the observational studies from 2009 onwards that analyzed the rate of PTB in patients who received supplementation with FA before and/or throughout pregnancy. The results from recent observational studies suggest a slight reduction of PTBs that is not consistent with the results from RCTs. Further research is needed to better understand the role of FA supplementation before and during pregnancy in PTB.

  18. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  19. Socioeconomic determinants of accessibility to birth registration in Lao PDR.

    Science.gov (United States)

    Nomura, Marika; Xangsayarath, Phonepadith; Takahashi, Kenzo; Kamiya, Yusuke; Siengsounthone, Latsamy; Ogino, Hina; Kobayashi, Jun

    2018-01-08

    The global coverage rate of birth registration is only around 65% for the population of children under five although birth registration secures protection and access to health services that are fundamental rights for all babies. This study aimed to perform a basic analysis of the accessibility to birth registration to better understand how to improve the birth registration system in the Lao PDR. For the analysis of birth registration and related socioeconomic factors, 9576 mother-child pairs were chosen from the data set of The Lao Social Indicator Survey 2011-12. After bivariate analysis with statistical tests including the chi-square test were conducted, logistic regression was performed to determine the variables that statistically influence accessibility to birth registration. Ethno-geographic factors and place of delivery were observed to be the factors associated with birth registration in this analysis. Many mothers in the Lao PDR deliver in their local communities. Therefore, capacity development of various human resources, such as Skilled Birth Attendant, to support the local administrative procedure of birth registration in their communities could be one option to overcoming the bottlenecks in the birth registration process in the Lao PDR.

  20. Breech birth

    Science.gov (United States)

    ... page: //medlineplus.gov/ency/patientinstructions/000623.htm Breech birth To use the sharing features on this page, ... safer for your baby to pass through the birth canal. In the last weeks of pregnancy, your ...