WorldWideScience

Sample records for neural engineering community

  1. 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.

  2. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. EDITORIAL: Commercial opportunities for neural engineers

    Science.gov (United States)

    Cavuoto, James

    2008-03-01

    of the device in 2000. Anthony Ignagni, who worked with Mortimer and Onders as project director and chief biomedical engineer, became a co-founder of Synapse and is currently president and CEO. Afferent Corp., Providence, RI, a manufacturer of sensory stimulation systems based on research at Boston University, Afferent's technology is based on work by James Collins, professor of biomedical engineering at Boston University, who showed that low-level stochastic, or random, vibrations improved sense of touch. Collins went on to demonstrate that generating a subthreshold noise in the sensory pathways with a random electrical stimulation improves detectability of weak mechanical stimuli. In 1999 entrepreneur Jason Harry licensed the stochastic resonance technology from Boston University and received help from the Community Technology Fund-the university's technology transfer incubator. NeuroNexus Technologies, Inc., Ann Arbor, MI, a manufacturer of implanted neural probes, based on a program at the University of Michigan. NeuroNexus was spun out of UM's Center for Neural Communications Technology. Daryl Kipke, head of the Neural Engineering Laboratory at the university, started up NeuroNexus with several colleagues and currently serves as president and CEO. Commercialization issues were discussed recently at a preconference workshop at the 2007 meeting of the International Neuromodulation Society in Acapulco, Mexico. In the session, which was chaired by Chris Coburn of the Cleveland Clinic, neurotech entrepreneurs John Bowers from Northstar Neuroscience and Ben Pless, formerly of NeuroPace, shared their experiences bringing neuromodulation therapies to market. Coburn related his observations from the Cleveland Clinic, which has spun off 22 companies over the last five years. He cited several factors that would influence a neurotech startup's market potential, such as identifying the regulatory pathway, any predicate devices that exist, and the revenue potential for

  4. Electrospun Nanofibrous Materials for Neural Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Yee-Shuan Lee

    2011-02-01

    Full Text Available The use of biomaterials processed by the electrospinning technique has gained considerable interest for neural tissue engineering applications. The tissue engineering strategy is to facilitate the regrowth of nerves by combining an appropriate cell type with the electrospun scaffold. Electrospinning can generate fibrous meshes having fiber diameter dimensions at the nanoscale and these fibers can be nonwoven or oriented to facilitate neurite extension via contact guidance. This article reviews studies evaluating the effect of the scaffold’s architectural features such as fiber diameter and orientation on neural cell function and neurite extension. Electrospun meshes made of natural polymers, proteins and compositions having electrical activity in order to enhance neural cell function are also discussed.

  5. Emerging trends in neuro engineering and neural computation

    CERN Document Server

    Lee, Kendall; Garmestani, Hamid; Lim, Chee

    2017-01-01

    This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologie...

  6. Proceedings of the Neural Network Workshop for the Hanford Community

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.

    1994-01-01

    These proceedings were generated from a series of presentations made at the Neural Network Workshop for the Hanford Community. The abstracts and viewgraphs of each presentation are reproduced in these proceedings. This workshop was sponsored by the Computing and Information Sciences Department in the Molecular Science Research Center (MSRC) at the Pacific Northwest Laboratory (PNL). Artificial neural networks constitute a new information processing technology that is destined within the next few years, to provide the world with a vast array of new products. A major reason for this is that artificial neural networks are able to provide solutions to a wide variety of complex problems in a much simpler fashion than is possible using existing techniques. In recognition of these capabilities, many scientists and engineers are exploring the potential application of this new technology to their fields of study. An artificial neural network (ANN) can be a software simulation, an electronic circuit, optical system, or even an electro-chemical system designed to emulate some of the brain`s rudimentary structure as well as some of the learning processes that are believed to take place in the brain. For a very wide range of applications in science, engineering, and information technology, ANNs offer a complementary and potentially superior approach to that provided by conventional computing and conventional artificial intelligence. This is because, unlike conventional computers, which have to be programmed, ANNs essentially learn from experience and can be trained in a straightforward fashion to carry out tasks ranging from the simple to the highly complex.

  7. Brains--Computers--Machines: Neural Engineering in Science Classrooms

    Science.gov (United States)

    Chudler, Eric H.; Bergsman, Kristen Clapper

    2016-01-01

    Neural engineering is an emerging field of high relevance to students, teachers, and the general public. This feature presents online resources that educators and scientists can use to introduce students to neural engineering and to integrate core ideas from the life sciences, physical sciences, social sciences, computer science, and engineering…

  8. Active Engine Mounting Control Algorithm Using Neural Network

    Directory of Open Access Journals (Sweden)

    Fadly Jashi Darsivan

    2009-01-01

    Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

  9. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    the neural network attractive. A neural network is an information processing system modeled on the structure of the dynamic process. It can solve the complex/nonlinear problems quickly once trained by operating on problems using an interconnected number...

  10. EDITORIAL: Why we need a new journal in neural engineering

    Science.gov (United States)

    Durand, Dominique M.

    2004-03-01

    The field of neural engineering crystallizes for many engineers and scientists an area of research at the interface between neuroscience and engineering. For the last 15 years or so, the discipline of neural engineering (neuroengineering) has slowly appeared at conferences as a theme or track. The first conference devoted entirely to this area was the 1st International IEEE EMBS Conference on Neural Engineering which took place in Capri, Italy in 2003. Understanding how the brain works is considered the ultimate frontier and challenge in science. The complexity of the brain is so great that understanding even the most basic functions will require that we fully exploit all the tools currently at our disposal in science and engineering and simultaneously develop new methods of analysis. While neuroscientists and engineers from varied fields such as brain anatomy, neural development and electrophysiology have made great strides in the analysis of this complex organ, there remains a great deal yet to be uncovered. The potential for applications and remedies deriving from scientific discoveries and breakthroughs is extremely high. As a result of the growing availability of micromachining technology, research into neurotechnology has grown relatively rapidly in recent years and appears to be approaching a critical mass. For example, by understanding how neuronal circuits process and store information, we could design computers with capabilities beyond current limits. By understanding how neurons develop and grow, we could develop new technologies for spinal cord repair or central nervous system repair following neurological disorders. Moreover, discoveries related to higher-level cognitive function and consciousness could have a profound influence on how humans make sense of their surroundings and interact with each other. The ability to successfully interface the brain with external electronics would have enormous implications for our society and facilitate a

  11. 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.

  12. Neural engineering from advanced biomaterials to 3D fabrication techniques

    CERN Document Server

    Kaplan, David

    2016-01-01

    This book covers the principles of advanced 3D fabrication techniques, stem cells and biomaterials for neural engineering. Renowned contributors cover topics such as neural tissue regeneration, peripheral and central nervous system repair, brain-machine interfaces and in vitro nervous system modeling. Within these areas, focus remains on exciting and emerging technologies such as highly developed neuroprostheses and the communication channels between the brain and prostheses, enabling technologies that are beneficial for development of therapeutic interventions, advanced fabrication techniques such as 3D bioprinting, photolithography, microfluidics, and subtractive fabrication, and the engineering of implantable neural grafts. There is a strong focus on stem cells and 3D bioprinting technologies throughout the book, including working with embryonic, fetal, neonatal, and adult stem cells and a variety of sophisticated 3D bioprinting methods for neural engineering applications. There is also a strong focus on b...

  13. Control of 12-Cylinder Camless Engine with Neural Networks

    OpenAIRE

    Ashhab Moh’d Sami

    2017-01-01

    The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s). The inputs to the net are the intake valve lift (IVL) and intake valve closing timing (IVC) whereas the output of the net is the cylinder air charge (CAC). The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and appl...

  14. Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

    Science.gov (United States)

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

    Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. Control of 12-Cylinder Camless Engine with Neural Networks

    Directory of Open Access Journals (Sweden)

    Ashhab Moh’d Sami

    2017-01-01

    Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.

  16. Application of neural networks in coastal engineering - An overview

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.

    Artificial Neural Network (ANN) is being applied to solve a wide variety of coastal/ocean engineering problems. In practical terms ANNs are non-linear modeling tools and they can be used to model complex relationship between the input and output...

  17. Ship Benchmark Shaft and Engine Gain FDI Using Neural Network

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Izadi-Zamanabadi, Roozbeh

    2002-01-01

    threshold value. In the paper a method for determining this threshold based on the neural network model is proposed, which can be used for a design strategy to handle residual sensitivity to input variations. The proposed method is used for successful FDI of a diesel engine gain fault in a ship propulsion...

  18. Successful neural network projects at the Idaho National Engineering Laboratory

    International Nuclear Information System (INIS)

    Cordes, G.A.

    1991-01-01

    This paper presents recent and current projects at the Idaho National Engineering Laboratory (INEL) that research and apply neural network technology. The projects are summarized in the paper and their direct application to space reactor power and propulsion systems activities is discussed. 9 refs., 10 figs., 3 tabs

  19. Microfluidic engineered high cell density three-dimensional neural cultures

    Science.gov (United States)

    Cullen, D. Kacy; Vukasinovic, Jelena; Glezer, Ari; La Placa, Michelle C.

    2007-06-01

    Three-dimensional (3D) neural cultures with cells distributed throughout a thick, bioactive protein scaffold may better represent neurobiological phenomena than planar correlates lacking matrix support. Neural cells in vivo interact within a complex, multicellular environment with tightly coupled 3D cell-cell/cell-matrix interactions; however, thick 3D neural cultures at cell densities approaching that of brain rapidly decay, presumably due to diffusion limited interstitial mass transport. To address this issue, we have developed a novel perfusion platform that utilizes forced intercellular convection to enhance mass transport. First, we demonstrated that in thick (>500 µm) 3D neural cultures supported by passive diffusion, cell densities =104 cells mm-3), continuous medium perfusion at 2.0-11.0 µL min-1 improved viability compared to non-perfused cultures (p death and matrix degradation. In perfused cultures, survival was dependent on proximity to the perfusion source at 2.00-6.25 µL min-1 (p 90% viability in both neuronal cultures and neuronal-astrocytic co-cultures. This work demonstrates the utility of forced interstitial convection in improving the survival of high cell density 3D engineered neural constructs and may aid in the development of novel tissue-engineered systems reconstituting 3D cell-cell/cell-matrix interactions.

  20. Artificial neural networks in the nuclear engineering (Part 2)

    International Nuclear Information System (INIS)

    Baptista Filho, Benedito Dias

    2002-01-01

    The field of Artificial Neural Networks (ANN), one of the branches of Artificial Intelligence has been waking up a lot of interest in the Nuclear Engineering (NE). ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in high complexity problems of control. The first part of this work began a discussion with feed-forward neural networks in back-propagation. In this part of the work, the Multi-synaptic neural networks is applied to control problems. Also, the self-organized maps is presented in a typical pattern classification problem: transients classification. The main purpose of the work is to show that ANN can be successfully used in NE if a carefully choice of its type is done: the application sets this choice. (author)

  1. Performance engineering in the community atmosphere model

    International Nuclear Information System (INIS)

    Worley, P; Mirin, A; Drake, J; Sawyer, W

    2006-01-01

    The Community Atmosphere Model (CAM) is the atmospheric component of the Community Climate System Model (CCSM) and is the primary consumer of computer resources in typical CCSM simulations. Performance engineering has been an important aspect of CAM development throughout its existence. This paper briefly summarizes these efforts and their impacts over the past five years

  2. Engineering chemical interactions in microbial communities.

    Science.gov (United States)

    Kenny, Douglas J; Balskus, Emily P

    2018-03-05

    Microbes living within host-associated microbial communities (microbiotas) rely on chemical communication to interact with surrounding organisms. These interactions serve many purposes, from supplying the multicellular host with nutrients to antagonizing invading pathogens, and breakdown of chemical signaling has potentially negative consequences for both the host and microbiota. Efforts to engineer microbes to take part in chemical interactions represent a promising strategy for modulating chemical signaling within these complex communities. In this review, we discuss prominent examples of chemical interactions found within host-associated microbial communities, with an emphasis on the plant-root microbiota and the intestinal microbiota of animals. We then highlight how an understanding of such interactions has guided efforts to engineer microbes to participate in chemical signaling in these habitats. We discuss engineering efforts in the context of chemical interactions that enable host colonization, promote host health, and exclude pathogens. Finally, we describe prominent challenges facing this field and propose new directions for future engineering efforts.

  3. Engaging Community College Students Using an Engineering Learning Community

    Science.gov (United States)

    Maccariella, James, Jr.

    The study investigated whether community college engineering student success was tied to a learning community. Three separate data collection sources were utilized: surveys, interviews, and existing student records. Mann-Whitney tests were used to assess survey data, independent t-tests were used to examine pre-test data, and independent t-tests, analyses of covariance (ANCOVA), chi-square tests, and logistic regression were used to examine post-test data. The study found students that participated in the Engineering TLC program experienced a significant improvement in grade point values for one of the three post-test courses studied. In addition, the analysis revealed the odds of fall-to-spring retention were 5.02 times higher for students that participated in the Engineering TLC program, and the odds of graduating or transferring were 4.9 times higher for students that participated in the Engineering TLC program. However, when confounding variables were considered in the study (engineering major, age, Pell Grant participation, gender, ethnicity, and full-time/part-time status), the analyses revealed no significant relationship between participation in the Engineering TLC program and course success, fall-to-spring retention, and graduation/transfer. Thus, the confounding variables provided alternative explanations for results. The Engineering TLC program was also found to be effective in providing mentoring opportunities, engagement and motivation opportunities, improved self confidence, and a sense of community. It is believed the Engineering TLC program can serve as a model for other community college engineering programs, by striving to build a supportive environment, and provide guidance and encouragement throughout an engineering student's program of study.

  4. 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)

  5. Reconstruction of an engine combustion process with a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Jacob, P J; Gu, F; Ball, A D [School of Engineering, University of Manchester, Manchester (United Kingdom)

    1998-12-31

    The cylinder pressure waveform in an internal combustion engine is one of the most important parameters in describing the engine combustion process. It is used for a range of diagnostic tasks such as identification of ignition faults or mechanical wear in the cylinders. However, it is very difficult to measure this parameter directly. Never-the-less, the cylinder pressure may be inferred from other more readily obtainable parameters. In this presentation it is shown how a Radial Basis Function network, which may be regarded as a form of neural network, may be used to model the cylinder pressure as a function of the instantaneous crankshaft velocity, recorded with a simple magnetic sensor. The application of the model is demonstrated on a four cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the model once trained are validated against measured data. (orig.) 4 refs.

  6. Reconstruction of an engine combustion process with a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Jacob, P.J.; Gu, F.; Ball, A.D. [School of Engineering, University of Manchester, Manchester (United Kingdom)

    1997-12-31

    The cylinder pressure waveform in an internal combustion engine is one of the most important parameters in describing the engine combustion process. It is used for a range of diagnostic tasks such as identification of ignition faults or mechanical wear in the cylinders. However, it is very difficult to measure this parameter directly. Never-the-less, the cylinder pressure may be inferred from other more readily obtainable parameters. In this presentation it is shown how a Radial Basis Function network, which may be regarded as a form of neural network, may be used to model the cylinder pressure as a function of the instantaneous crankshaft velocity, recorded with a simple magnetic sensor. The application of the model is demonstrated on a four cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the model once trained are validated against measured data. (orig.) 4 refs.

  7. Humanitarian engineering placements in our own communities

    Science.gov (United States)

    VanderSteen, J. D. J.; Hall, K. R.; Baillie, C. A.

    2010-05-01

    There is an increasing interest in the humanitarian engineering curriculum, and a service-learning placement could be an important component of such a curriculum. International placements offer some important pedagogical advantages, but also have some practical and ethical limitations. Local community-based placements have the potential to be transformative for both the student and the community, although this potential is not always seen. In order to investigate the role of local placements, qualitative research interviews were conducted. Thirty-two semi-structured research interviews were conducted and analysed, resulting in a distinct outcome space. It is concluded that local humanitarian engineering placements greatly complement international placements and are strongly recommended if international placements are conducted. More importantly it is seen that we are better suited to address the marginalised in our own community, although it is often easier to see the needs of an outside populace.

  8. Community | College of Engineering & Applied Science

    Science.gov (United States)

    Electrical Engineering Instructional Laboratories Student Resources Industrial & Manufacturing Engineering Industrial & Manufacturing Engineering Academic Programs Industrial & Manufacturing Engineering Major Industrial & Manufacturing Engineering Minor Industrial & Manufacturing Engineering

  9. EDITORIAL: Special issue on applied neurodynamics: from neural dynamics to neural engineering Special issue on applied neurodynamics: from neural dynamics to neural engineering

    Science.gov (United States)

    Chiel, Hillel J.; Thomas, Peter J.

    2011-12-01

    in part on his own work in this area. This is a very small glimpse of a much larger literature; these mathematical themes recur throughout this issue. Practitioners of neural engineering who want to explore the language and role of dynamics further can find accessible introductions to the key ideas in works such as Strogatz (1994) and Izhikevich (2006). In this special issue of Journal of Neural Engineering, we provide a sample of the vigor and excitement of the recent developments in the applications of nonlinear dynamical systems theory to the understanding and control of the nervous system. Four of the papers demonstrate the power of dynamical systems theory to analyze and understand neural systems, both in isolation and within a neuromechanical context (Coggan et al 2011, Nadim et al 2011, Spardy et al 2011a, 2011b). One paper focuses on the importance of noise and delay in dynamical systems for control (Milton 2011). Two papers focus on the dynamics of ion channels—in one paper, new approaches for estimating their parameters are described (Meng et al 2011), and in a second, the time courses of sodium ion channels are used to understand conduction block due to high-frequency stimulation (Ackermann et al 2011). Two papers focus on the use of optimal control theory to develop approaches for understanding (deWolf and Eliasmith 2011) and controlling (Nabi and Moehlis 2011) the nervous system. Finally, two papers begin to explore longer time scale neural dynamics through a combination of modeling and experiments, examining how animals learn to reduce the time required to forage for food at multiple sites (de Jong et al 2011), and how the dynamics of the respiratory system change with development (Fietkiewicz et al 2011). The first four papers of this special issue illustrate the use of dynamical systems theory to analyze and understand neural circuitry and neuromechanical systems. The first of these papers uses the phase response curve (PRC) of an oscillator, which

  10. Neural tissue engineering options for peripheral nerve regeneration.

    Science.gov (United States)

    Gu, Xiaosong; Ding, Fei; Williams, David F

    2014-08-01

    Tissue engineered nerve grafts (TENGs) have emerged as a potential alternative to autologous nerve grafts, the gold standard for peripheral nerve repair. Typically, TENGs are composed of a biomaterial-based template that incorporates biochemical cues. A number of TENGs have been used experimentally to bridge long peripheral nerve gaps in various animal models, where the desired outcome is nerve tissue regeneration and functional recovery. So far, the translation of TENGs to the clinic for use in humans has met with a certain degree of success. In order to optimize the TENG design and further approach the matching of TENGs with autologous nerve grafts, many new cues, beyond the traditional ones, will have to be integrated into TENGs. Furthermore, there is a strong requirement for monitoring the real-time dynamic information related to the construction of TENGs. The aim of this opinion paper is to specifically and critically describe the latest advances in the field of neural tissue engineering for peripheral nerve regeneration. Here we delineate new attempts in the design of template (or scaffold) materials, especially in the context of biocompatibility, the choice and handling of support cells, and growth factor release systems. We further discuss the significance of RNAi for peripheral nerve regeneration, anticipate the potential application of RNAi reagents for TENGs, and speculate on the possible contributions of additional elements, including angiogenesis, electrical stimulation, molecular inflammatory mediators, bioactive peptides, antioxidant reagents, and cultured biological constructs, to TENGs. Finally, we consider that a diverse array of physicochemical and biological cues must be orchestrated within a TENG to create a self-consistent coordinated system with a close proximity to the regenerative microenvironment of the peripheral nervous system. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Modelling a variable valve timing spark ignition engine using different neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Beham, M. [BMW AG, Munich (Germany); Yu, D.L. [John Moores University, Liverpool (United Kingdom). Control Systems Research Group

    2004-10-01

    In this paper different neural networks (NN) are compared for modelling a variable valve timing spark-ignition (VVT SI) engine. The overall system is divided for each output into five neural multi-input single output (MISO) subsystems. Three kinds of NN, multilayer Perceptron (MLP), pseudo-linear radial basis function (PLRBF), and local linear model tree (LOLIMOT) networks, are used to model each subsystem. Real data were collected when the engine was under different operating conditions and these data are used in training and validation of the developed neural models. The obtained models are finally tested in a real-time online model configuration on the test bench. The neural models run independently of the engine in parallel mode. The model outputs are compared with process output and compared among different models. These models performed well and can be used in the model-based engine control and optimization, and for hardware in the loop systems. (author)

  12. 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.

  13. Methodology of Neural Design: Applications in Microwave Engineering

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-06-01

    Full Text Available In the paper, an original methodology for the automatic creation of neural models of microwave structures is proposed and verified. Following the methodology, neural models of the prescribed accuracy are built within the minimum CPU time. Validity of the proposed methodology is verified by developing neural models of selected microwave structures. Functionality of neural models is verified in a design - a neural model is joined with a genetic algorithm to find a global minimum of a formulated objective function. The objective function is minimized using different versions of genetic algorithms, and their mutual combinations. The verified methodology of the automated creation of accurate neural models of microwave structures, and their association with global optimization routines are the most important original features of the paper.

  14. CLASSIFICATION OF NEURAL NETWORK FOR TECHNICAL CONDITION OF TURBOFAN ENGINES BASED ON HYBRID ALGORITHM

    Directory of Open Access Journals (Sweden)

    Valentin Potapov

    2016-12-01

    Full Text Available Purpose: This work presents a method of diagnosing the technical condition of turbofan engines using hybrid neural network algorithm based on software developed for the analysis of data obtained in the aircraft life. Methods: allows the engine diagnostics with deep recognition to the structural assembly in the presence of single structural damage components of the engine running and the multifaceted damage. Results: of the optimization of neural network structure to solve the problems of evaluating technical state of the bypass turbofan engine, when used with genetic algorithms.

  15. Fuel economy and torque tracking in camless engines through optimization of neural networks

    International Nuclear Information System (INIS)

    Ashhab, Moh'd Sami S.

    2008-01-01

    The feed forward controller of a camless internal combustion engine is modeled by inverting a multi-input multi-output feed forward artificial neural network (ANN) model of the engine. The engine outputs, pumping loss and cylinder air charge, are related to the inputs, intake valve lift and closing timing, by the artificial neural network model, which is trained with historical input-output data. The controller selects the intake valve lift and closing timing that will mimimize the pumping loss and achieve engine torque tracking. Lower pumping loss means better fuel economy, whereas engine torque tracking gurantees the driver's torque demand. The inversion of the ANN is performed with the complex method constrained optimization. How the camless engine inverse controller can be augmented with adaptive techniques to maintain accuracy even when the engine parts degrade is discussed. The simulation results demonstrate the effectiveness of the developed camless engine controller

  16. Educating the Engineer for Sustainable Community Development

    Science.gov (United States)

    Munoz, D. R.

    2008-12-01

    More than ever before, we are confronting the challenges of limited resources (water, food, energy and mineral), while also facing complex challenges with the environment and related social unrest. Resource access problems are exacerbated by multi-scale geopolitical instability. We seek a balance that will allow profit but also leave a world fit for our children to inherit. Many are working with small groups to make positive change through finding solutions that address these challenges. In fact, some say that in sum, it is the largest human movement that has ever existed. In this talk I will share our experiences to alleviate vulnerabilities for populations of humans in need while working with students, corporate entities and non governmental organizations. Our main focus is to educate a new cadre of engineers that have an enhanced awareness of and better communication skills for a different cultural environment than the one in which they were raised and are hungry to seek new opportunities to serve humanity at a basic level. The results of a few of the more than forty humanitarian engineering projects completed since 2003 will be superimposed on a theoretical framework for sustainable community development. This will be useful information to those seeking a social corporate position of responsibility and a world that more closely approaches a sustainable equilibrium.

  17. Preparing engineers for the challenges of community engagement

    Science.gov (United States)

    Harsh, Matthew; Bernstein, Michael J.; Wetmore, Jameson; Cozzens, Susan; Woodson, Thomas; Castillo, Rafael

    2017-11-01

    Despite calls to address global challenges through community engagement, engineers are not formally prepared to engage with communities. Little research has been done on means to address this 'engagement gap' in engineering education. We examine the efficacy of an intensive, two-day Community Engagement Workshop for engineers, designed to help engineers better look beyond technology, listen to and learn from people, and empower communities. We assessed the efficacy of the workshop in a non-experimental pre-post design using a questionnaire and a concept map. Questionnaire results indicate participants came away better able to ask questions more broadly inclusive of non-technological dimensions of engineering projects. Concept map results indicate participants have a greater understanding of ways social factors shape complex material systems after completing the programme. Based on the workshop's strengths and weaknesses, we discuss the potential of expanding and supplementing the programme to help engineers account for social aspects central to engineered systems.

  18. Artificial neural network applications in the calibration of spark-ignition engines: An overview

    Directory of Open Access Journals (Sweden)

    Richard Fiifi Turkson

    2016-09-01

    Full Text Available Emission legislation has become progressively tighter, making the development of new internal combustion engines very challenging. New engine technologies for complying with these regulations introduce an exponential dependency between the number of test combinations required for obtaining optimum results and the time and cost outlays. This makes the calibration task very expensive and virtually impossible to carry out. The potential use of trained neural networks in combination with Design of Experiments (DoE methods for engine calibration has been a subject of research activities in recent times. This is because artificial neural networks, compared with other data-driven modeling techniques, perform better in satisfying a majority of the modeling requirements for engine calibration including the curse of dimensionality; the use of DoE for obtaining few measurements as practicable, with the aim of reducing engine calibration costs; the required flexibility that allows model parameters to be optimized to avoid overfitting; and the facilitation of automated online optimization during the engine calibration process that eliminates the need for user intervention. The purpose of this review is to give an overview of the various applications of neural networks in the calibration of spark-ignition engines. The identified and discussed applications include system identification for rapid prototyping, virtual sensing, use of neural networks as look-up table surrogates, emerging control strategies and On-Board Diagnostic (OBD applications. The demerits of neural networks, future possibilities and alternatives were also discussed.

  19. Engineering Human Neural Tissue by 3D Bioprinting.

    Science.gov (United States)

    Gu, Qi; Tomaskovic-Crook, Eva; Wallace, Gordon G; Crook, Jeremy M

    2018-01-01

    Bioprinting provides an opportunity to produce three-dimensional (3D) tissues for biomedical research and translational drug discovery, toxicology, and tissue replacement. Here we describe a method for fabricating human neural tissue by 3D printing human neural stem cells with a bioink, and subsequent gelation of the bioink for cell encapsulation, support, and differentiation to functional neurons and supporting neuroglia. The bioink uniquely comprises the polysaccharides alginate, water-soluble carboxymethyl-chitosan, and agarose. Importantly, the method could be adapted to fabricate neural and nonneural tissues from other cell types, with the potential to be applied for both research and clinical product development.

  20. Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

    Directory of Open Access Journals (Sweden)

    M. Bazazzadeh

    2011-01-01

    Full Text Available This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc. are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.

  1. 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.

  2. Applying neural networks as software sensors for enzyme engineering.

    Science.gov (United States)

    Linko, S; Zhu, Y H; Linko, P

    1999-04-01

    The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.

  3. A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

    In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

  4. Neural networks and its application in biomedical engineering

    International Nuclear Information System (INIS)

    Husnain, S.K.; Bhatti, M.I.

    2002-01-01

    Artificial network (ANNs) is an information processing system that has certain performance characteristics in common with biological neural networks. A neural network is characterized by connections between the neurons, method of determining the weights on the connections and its activation functions while a biological neuron has three types of components that are of particular interest in understanding an artificial neuron: its dendrites, soma, and axon. The actin of the chemical transmitter modifies the incoming signal. The study of neural networks is an extremely interdisciplinary field. Computer-based diagnosis is an increasingly used method that tries to improve the quality of health care. Systems on Neural Networks have been developed extensively in the last ten years with the hope that medical diagnosis and therefore medical care would improve dramatically. The addition of a symbolic processing layer enhances the ANNs in a number of ways. It is, for instance, possible to supplement a network that is purely diagnostic with a level that recommends or nodes in order to more closely simulate the nervous system. (author)

  5. PERSPECTIVE: Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain

    Science.gov (United States)

    Rousche, Patrick; Schneeweis, David M.; Perreault, Eric J.; Jensen, Winnie

    2008-03-01

    A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who

  6. Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain.

    Science.gov (United States)

    Rousche, Patrick; Schneeweis, David M; Perreault, Eric J; Jensen, Winnie

    2008-03-01

    A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who

  7. Development of a Civil Engineer Corps Community Portal Prototype

    National Research Council Canada - National Science Library

    Rader, Neil

    2002-01-01

    The Civil Engineer Corps (CEC) is a relatively small Navy community consisting of approximately 1300 officers, Billet locations for the CEC range from Bahrain, Saudi Arabia to Keflavik, Iceland, CEC officers have a broad range...

  8. Capstone Engineering Design Projects for Community Colleges

    Science.gov (United States)

    Walz, Kenneth A.; Christian, Jon R.

    2017-01-01

    Capstone engineering design courses have been a feature at research universities and four-year schools for many years. Although such classes are less common at two-year colleges, the experience is equally beneficial for this population of students. With this in mind, Madison College introduced a project-based Engineering Design course in 2007.…

  9. Cascade Optimization for Aircraft Engines With Regression and Neural Network Analysis - Approximators

    Science.gov (United States)

    Patnaik, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

    2000-01-01

    The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.

  10. Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine

    Directory of Open Access Journals (Sweden)

    Özener Orkun

    2013-01-01

    Full Text Available This study details the artificial neural network (ANN modelling of a diesel engine to predict the torque, power, brake-specific fuel consumption and pollutant emissions, including carbon dioxide, carbon monoxide, nitrogen oxides, total hydrocarbons and filter smoke number. To collect data for training and testing the neural network, experiments were performed on a four cylinder, four stroke compression ignition engine. A total of 108 test points were run on a dynamometer. For the first part of this work, a parameter packet was used as the inputs for the neural network, and satisfactory regression was found with the outputs (over ~95%, excluding total hydrocarbons. The second stage of this work addressed developing new networks with additional inputs for predicting the total hydrocarbons, and the regression was raised from 75 % to 90 %. This study shows that the ANN approach can be used for accurately predicting characteristic values of an internal combustion engine and that the neural network performance can be increased using additional related input data.

  11. Women Engineering Transfer Students: The Community College Experience

    Science.gov (United States)

    Patterson, Susan J.

    2011-01-01

    An interpretative philosophical framework was applied to a case study to document the particular experiences and perspectives of ten women engineering transfer students who once attended a community college and are currently enrolled in one of two university professional engineering programs. This study is important because women still do not earn…

  12. Development of biomaterial scaffold for nerve tissue engineering: Biomaterial mediated neural regeneration

    Directory of Open Access Journals (Sweden)

    Sethuraman Swaminathan

    2009-11-01

    Full Text Available Abstract Neural tissue repair and regeneration strategies have received a great deal of attention because it directly affects the quality of the patient's life. There are many scientific challenges to regenerate nerve while using conventional autologous nerve grafts and from the newly developed therapeutic strategies for the reconstruction of damaged nerves. Recent advancements in nerve regeneration have involved the application of tissue engineering principles and this has evolved a new perspective to neural therapy. The success of neural tissue engineering is mainly based on the regulation of cell behavior and tissue progression through the development of a synthetic scaffold that is analogous to the natural extracellular matrix and can support three-dimensional cell cultures. As the natural extracellular matrix provides an ideal environment for topographical, electrical and chemical cues to the adhesion and proliferation of neural cells, there exists a need to develop a synthetic scaffold that would be biocompatible, immunologically inert, conducting, biodegradable, and infection-resistant biomaterial to support neurite outgrowth. This review outlines the rationale for effective neural tissue engineering through the use of suitable biomaterials and scaffolding techniques for fabrication of a construct that would allow the neurons to adhere, proliferate and eventually form nerves.

  13. Development of biomaterial scaffold for nerve tissue engineering: Biomaterial mediated neural regeneration

    Science.gov (United States)

    2009-01-01

    Neural tissue repair and regeneration strategies have received a great deal of attention because it directly affects the quality of the patient's life. There are many scientific challenges to regenerate nerve while using conventional autologous nerve grafts and from the newly developed therapeutic strategies for the reconstruction of damaged nerves. Recent advancements in nerve regeneration have involved the application of tissue engineering principles and this has evolved a new perspective to neural therapy. The success of neural tissue engineering is mainly based on the regulation of cell behavior and tissue progression through the development of a synthetic scaffold that is analogous to the natural extracellular matrix and can support three-dimensional cell cultures. As the natural extracellular matrix provides an ideal environment for topographical, electrical and chemical cues to the adhesion and proliferation of neural cells, there exists a need to develop a synthetic scaffold that would be biocompatible, immunologically inert, conducting, biodegradable, and infection-resistant biomaterial to support neurite outgrowth. This review outlines the rationale for effective neural tissue engineering through the use of suitable biomaterials and scaffolding techniques for fabrication of a construct that would allow the neurons to adhere, proliferate and eventually form nerves. PMID:19939265

  14. Neural stem cell proliferation and differentiation in the conductive PEDOT-HA/Cs/Gel scaffold for neural tissue engineering.

    Science.gov (United States)

    Wang, Shuping; Guan, Shui; Xu, Jianqiang; Li, Wenfang; Ge, Dan; Sun, Changkai; Liu, Tianqing; Ma, Xuehu

    2017-09-26

    Engineering scaffolds with excellent electro-activity is increasingly important in tissue engineering and regenerative medicine. Herein, conductive poly(3,4-ethylenedioxythiophene) doped with hyaluronic acid (PEDOT-HA) nanoparticles were firstly synthesized via chemical oxidant polymerization. A three-dimensional (3D) PEDOT-HA/Cs/Gel scaffold was then developed by introducing PEDOT-HA nanoparticles into a chitosan/gelatin (Cs/Gel) matrix. HA, as a bridge, not only was used as a dopant, but also combined PEDOT into the Cs/Gel via chemical crosslinking. The PEDOT-HA/Cs/Gel scaffold was used as a conductive substrate for neural stem cell (NSC) culture in vitro. The results demonstrated that the PEDOT-HA/Cs/Gel scaffold had excellent biocompatibility for NSC proliferation and differentiation. 3D confocal fluorescence images showed cells attached on the channel surface of Cs/Gel and PEDOT-HA/Cs/Gel scaffolds with a normal neuronal morphology. Compared to the Cs/Gel scaffold, the PEDOT-HA/Cs/Gel scaffold not only promoted NSC proliferation with up-regulated expression of Ki67, but also enhanced NSC differentiation into neurons and astrocytes with up-regulated expression of β tubulin-III and GFAP, respectively. It is expected that this electro-active and bio-active PEDOT-HA/Cs/Gel scaffold will be used as a conductive platform to regulate NSC behavior for neural tissue engineering.

  15. Fault diagnosis for engine air path with neural models and classifier ...

    African Journals Online (AJOL)

    A new FDI scheme is developed for automotive engines in this paper. The method uses an independent radial basis function (RBF) neural ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10-20% changes ...

  16. Сhoosing the best type neural network jet contour diagnostics engines

    Directory of Open Access Journals (Sweden)

    О.С. Якушенко

    2006-01-01

    Full Text Available  In the paper the  choice problems of neurons  type for neural network is considered. The neurons types has to be , optimal from the point of work stability, training speed and quality of gas turbine engine  technical condition class recognition by work process parameters. Results of researches are given.

  17. A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia.

    Science.gov (United States)

    Floares, Alexandru George

    2008-01-01

    Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.

  18. Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Rytter, A.

    In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving...

  19. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  20. Reliability analysis of C-130 turboprop engine components using artificial neural network

    Science.gov (United States)

    Qattan, Nizar A.

    In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine

  1. Where Is "Community"?: Engineering Education and Sustainable Community Development

    Science.gov (United States)

    Schneider, J.; Leydens, J. A.; Lucena, J.

    2008-01-01

    Sustainable development initiatives are proliferating in the US and Europe as engineering educators seek to provide students with knowledge and skills to design technologies that are environmentally sustainable. Many such initiatives involve students from the "North," or "developed" world building projects for villages or…

  2. Artificial neural networks and evolutionary algorithms in engineering design

    OpenAIRE

    T. Velsker; M. Eerme; J. Majak; M. Pohlak; K. Karjust

    2011-01-01

    Purpose: Purpose of this paper is investigation of optimization strategies eligible for solving complex engineering design problems. An aim is to develop numerical algorithms for solving optimal design problems which may contain real and integer variables, a number of local extremes, linear- and non-linear constraints and multiple optimality criteria.Design/methodology/approach: The methodology proposed for solving optimal design problems is based on integrated use of meta-modeling techniques...

  3. Vertical Interaction in Open Software Engineering Communities

    Science.gov (United States)

    2009-03-01

    FIRMS: BUSINESS COLLABORATION THROUGH OPEN SOURCE PROJECTS the Application Lifecycle Framework (ALF) and several sub-project streams in the SOA Tools...to 1This chapter is substantially based on a paper in progress with Jim Herbsleb and Robert Kraut. 86 CHAPTER 4. FIRMS AND INDIVIDUALS: THE IMPACT OF...involved in the community[44]. For exam - ple, the GNOME project, a successful desktop environment for Linux and Unix systems, has a website called

  4. Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

    Directory of Open Access Journals (Sweden)

    Ganapathy Thirunavukkarasu

    2009-01-01

    Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

  5. Prediction of biodiesel ignition delay in a diesel engine using artificial neural networks

    International Nuclear Information System (INIS)

    Piloto-Rodríguez, Ramón; Sánchez-Borroto, Yisel

    2017-01-01

    Ignition delay is one of the most important parameters of the combustion process and have a strong influence in exhaust emissions and engines performance. In the present work, the results of the mathematical modeling of ignition delay through artificial neural networks are shown. The modeling starts from input values that cover thermodynamic variables, engines parameters and biodiesel properties. The model obtained is only useful for biodiesel samples and several neural network algorithms were applied in order to predict the ignition delay. From its correlation coefficient, prediction capability and lowest absolute error, the best model was selected. Among other network’s input parameters, the cetane number was taken into account, also previously predicted by the use of ANN. (author)

  6. Diagnosing of car engine fuel injectors damage using DWT analysis and PNN neural networks

    Directory of Open Access Journals (Sweden)

    Piotr CZECH

    2013-01-01

    Full Text Available In many research centers all over the world nowadays works are being carried out aimed at compiling method for diagnosis machines technical condition. Special meaning have non-invasive methods including methods using vibroacoustic phenomena. In this article is proposed using DWT analysis and energy or entropy, which are a base for diagnostic system of fuel injectors damage in car combustion engine. There were conducted researches aimed at building of diagnostic system using PNN neural networks.

  7. Therapeutically engineered induced neural stem cells are tumour-homing and inhibit progression of glioblastoma

    OpenAIRE

    Bag?, Juli R.; Alfonso-Pecchio, Adolfo; Okolie, Onyi; Dumitru, Raluca; Rinkenbaugh, Amanda; Baldwin, Albert S.; Miller, C. Ryan; Magness, Scott T.; Hingtgen, Shawn D.

    2016-01-01

    Transdifferentiation (TD) is a recent advancement in somatic cell reprogramming. The direct conversion of TD eliminates the pluripotent intermediate state to create cells that are ideal for personalized cell therapy. Here we provide evidence that TD-derived induced neural stem cells (iNSCs) are an efficacious therapeutic strategy for brain cancer. We find that iNSCs genetically engineered with optical reporters and tumouricidal gene products retain the capacity to differentiate and induced ap...

  8. Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine

    Directory of Open Access Journals (Sweden)

    Mohammad Hossein Ahmadi

    2015-02-01

    Full Text Available Different variables affect the performance of the Stirling engine and are considered in optimization and designing activities. Among these factors, torque and power have the greatest effect on the robustness of the Stirling engine, so they need to be determined with low uncertainty and high precision. In this article, the distribution of torque and power are determined using experimental data. Specifically, a novel polynomial approach is proposed to specify torque and power, on the basis of previous experimental work. This research addresses the question of whether GMDH (group method of data handling-type neural networks can be utilized to predict the torque and power based on determined parameters.

  9. Use of neural networks in process engineering. Thermodynamics, diffusion, and process control and simulation applications

    International Nuclear Information System (INIS)

    Otero, F

    1998-01-01

    This article presents the current status of the use of Artificial Neural Networks (ANNs) in process engineering applications where common mathematical methods do not completely represent the behavior shown by experimental observations, results, and plant operating data. Three examples of the use of ANNs in typical process engineering applications such as prediction of activity in solvent-polymer binary systems, prediction of a surfactant self-diffusion coefficient of micellar systems, and process control and simulation are shown. These examples are important for polymerization applications, enhanced-oil recovery, and automatic process control

  10. Imprinting Community College Computer Science Education with Software Engineering Principles

    Science.gov (United States)

    Hundley, Jacqueline Holliday

    Although the two-year curriculum guide includes coverage of all eight software engineering core topics, the computer science courses taught in Alabama community colleges limit student exposure to the programming, or coding, phase of the software development lifecycle and offer little experience in requirements analysis, design, testing, and maintenance. We proposed that some software engineering principles can be incorporated into the introductory-level of the computer science curriculum. Our vision is to give community college students a broader exposure to the software development lifecycle. For those students who plan to transfer to a baccalaureate program subsequent to their community college education, our vision is to prepare them sufficiently to move seamlessly into mainstream computer science and software engineering degrees. For those students who plan to move from the community college to a programming career, our vision is to equip them with the foundational knowledge and skills required by the software industry. To accomplish our goals, we developed curriculum modules for teaching seven of the software engineering knowledge areas within current computer science introductory-level courses. Each module was designed to be self-supported with suggested learning objectives, teaching outline, software tool support, teaching activities, and other material to assist the instructor in using it.

  11. A community sharing hands-on centers in engineer's training

    Directory of Open Access Journals (Sweden)

    jean-pierre jpt Taboy

    2006-02-01

    Full Text Available As teachers in Technical Universities, we must think about the engineer's training. We need good applicants, up to date hardware and software for hand-on. Each university don't have enough money and technical people to cover the new needs. A community sharing remote hand-on centers could be a solution.

  12. Imprinting Community College Computer Science Education with Software Engineering Principles

    Science.gov (United States)

    Hundley, Jacqueline Holliday

    2012-01-01

    Although the two-year curriculum guide includes coverage of all eight software engineering core topics, the computer science courses taught in Alabama community colleges limit student exposure to the programming, or coding, phase of the software development lifecycle and offer little experience in requirements analysis, design, testing, and…

  13. The experimental study of genetic engineering human neural stem cells mediated by lentivirus to express multigene.

    Science.gov (United States)

    Cai, Pei-qiang; Tang, Xun; Lin, Yue-qiu; Martin, Oudega; Sun, Guang-yun; Xu, Lin; Yang, Yun-kang; Zhou, Tian-hua

    2006-02-01

    To explore the feasibility to construct genetic engineering human neural stem cells (hNSCs) mediated by lentivirus to express multigene in order to provide a graft source for further studies of spinal cord injury (SCI). Human neural stem cells from the brain cortex of human abortus were isolated and cultured, then gene was modified by lentivirus to express both green fluorescence protein (GFP) and rat neurotrophin-3 (NT-3); the transgenic expression was detected by the methods of fluorescence microscope, dorsal root ganglion of fetal rats and slot blot. Genetic engineering hNSCs were successfully constructed. All of the genetic engineering hNSCs which expressed bright green fluorescence were observed under the fluorescence microscope. The conditioned medium of transgenic hNSCs could induce neurite flourishing outgrowth from dorsal root ganglion (DRG). The genetic engineering hNSCs expressed high level NT-3 which could be detected by using slot blot. Genetic engineering hNSCs mediated by lentivirus can be constructed to express multigene successfully.

  14. EDITORIAL: Special issue on optical neural engineering: advances in optical stimulation technology Special issue on optical neural engineering: advances in optical stimulation technology

    Science.gov (United States)

    Shoham, Shy; Deisseroth, Karl

    2010-08-01

    Neural engineering, itself an 'emerging interdisciplinary research area' [1] has undergone a sea change over the past few years with the emergence of exciting new optical technologies for monitoring, stimulating, inhibiting and, more generally, modulating neural activity. To a large extent, this change is driven by the realization of the promise and complementary strengths that emerging photo-stimulation tools offer to add to the neural engineer's toolbox, which has been almost exclusively based on electrical stimulation technologies. Notably, photo-stimulation is non-contact, can in some cases be genetically targeted to specific cell populations, can achieve high spatial specificity (cellular or even sub-cellular) in two or three dimensions, and opens up the possibility of large-scale spatial-temporal patterned stimulation. It also offers a seamless solution to the problem of cross-talk generated by simultaneous electrical stimulation and recording. As in other biomedical optics phenomena [2], photo-stimulation includes multiple possible modes of interaction between light and the target neurons, including a variety of photo-physical and photo-bio-chemical effects with various intrinsic components or exogenous 'sensitizers' which can be loaded into the tissue or genetically expressed. Early isolated reports of neural excitation with light date back to the late 19th century [3] and to Arvanitaki and Chalazonitis' work five decades ago [4]; however, the mechanism by which these and other direct photo-stimulation, inhibition and modulation events [5-7] took place is yet unclear, as is their short- and long-term safety profile. Photo-chemical photolysis of covalently 'caged' neurotransmitters [8, 9] has been widely used in cellular neuroscience research for three decades, including for exciting or inhibiting neural activity, and for mapping neural circuits. Technological developments now allow neurotransmitters to be uncaged with exquisite spatial specificity (down to

  15. A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

    Science.gov (United States)

    Guo, T. H.; Musgrave, J.

    1992-11-01

    In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using

  16. Using Long-Short-Term-Memory Recurrent Neural Networks to Predict Aviation Engine Vibrations

    Science.gov (United States)

    ElSaid, AbdElRahman Ahmed

    This thesis examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The different networks are trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a "memory" of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 1, 5, 10, and 20 seconds in the future, with 2.84% 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.

  17. Electrically polarized PLLA nanofibers as neural tissue engineering scaffolds with improved neuritogenesis.

    Science.gov (United States)

    Barroca, Nathalie; Marote, Ana; Vieira, Sandra I; Almeida, Abílio; Fernandes, Maria H V; Vilarinho, Paula M; da Cruz E Silva, Odete A B

    2018-07-01

    Tissue engineering is evolving towards the production of smart platforms exhibiting stimulatory cues to guide tissue regeneration. This work explores the benefits of electrical polarization to produce more efficient neural tissue engineering platforms. Poly (l-lactic) acid (PLLA)-based scaffolds were prepared as solvent cast films and electrospun aligned nanofibers, and electrically polarized by an in-lab built corona poling device. The characterization of the platforms by thermally stimulated depolarization currents reveals a polarization of 60 × 10 -10 C cm -2 that is stable on poled electrospun nanofibers for up to 6 months. Further in vitro studies using neuroblastoma cells reveals that platforms' polarization potentiates Retinoic Acid-induced neuronal differentiation. Additionally, in differentiating embryonic cortical neurons, poled aligned nanofibers further increased neurite outgrowth by 30% (+70 μm) over non-poled aligned nanofibers, and by 50% (+100 μm) over control conditions. Therefore, the synergy of topographical cues and electrical polarization of poled aligned nanofibers places them as promising biocompatible and bioactive platforms for neural tissue regeneration. Given their long lasting induced polarization, these PLLA poled nanofibrous scaffolds can be envisaged as therapeutic devices of long shelf life for neural repair applications. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

    Science.gov (United States)

    Okamoto, Hiroshi

    2016-08-01

    Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  19. Artificial neural networks for dynamic monitoring of simulated-operating parameters of high temperature gas cooled engineering test reactor (HTTR)

    International Nuclear Information System (INIS)

    Seker, Serhat; Tuerkcan, Erdinc; Ayaz, Emine; Barutcu, Burak

    2003-01-01

    This paper addresses to the problem of utilisation of the artificial neural networks (ANNs) for detecting anomalies as well as physical parameters of a nuclear power plant during power operation in real time. Three different types of neural network algorithms were used namely, feed-forward neural network (back-propagation, BP) and two types of recurrent neural networks (RNN). The data used in this paper were gathered from the simulation of the power operation of the Japan's High Temperature Engineering Testing Reactor (HTTR). For the wide range of power operation, 56 signals were generated by the reactor dynamic simulation code for several hours of normal power operation at different power ramps between 30 and 100% nominal power. Paper will compare the outcomes of different neural networks and presents the neural network system and the determination of physical parameters from the simulated operating data

  20. Effects of antagonistic ecosystem engineers on macrofauna communities in a patchy, intertidal mudflat landscape

    NARCIS (Netherlands)

    Eklof, J. S.; Donadi, S.; van der Heide, T.; van der Zee, E. M.; Eriksson, B. K.

    Ecosystem engineers are organisms that strongly modify abiotic conditions and in the process alter associated communities. Different types of benthic ecosystem engineers have been suggested to facilitate different communities in otherwise similar marine environments, partly because they alter

  1. A community-based, interdisciplinary rehabilitation engineering course.

    Science.gov (United States)

    Lundy, Mary; Aceros, Juan

    2016-08-01

    A novel, community-based course was created through collaboration between the School of Engineering and the Physical Therapy program at the University of North Florida. This course offers a hands-on, interdisciplinary training experience for undergraduate engineering students through team-based design projects where engineering students are partnered with physical therapy students. Students learn the process of design, fabrication and testing of low-tech and high-tech rehabilitation technology for children with disabilities, and are exposed to a clinical experience under the guidance of licensed therapists. This course was taught in two consecutive years and pre-test/post-test data evaluating the impact of this interprofessional education experience on the students is presented using the Public Service Motivation Scale, Civic Actions Scale, Civic Attitudes Scale, and the Interprofessional Socialization and Valuing Scale.

  2. ENGINEERING INSIDE PROCESS OF URBAN RENEWAL AND COMMUNITY MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Barrantes, K.

    2015-06-01

    Full Text Available This paper aims to show the community management process and interdisciplinary work involve in the Social Action project named “University social work: Calle de la Amargura towards a physical, recreational and cultural renewal” which belongs to the Civil Engineering School of Universidad de Costa Rica (UCR This initiative began in 2005 as a response to the security issue in a place known as “Calle de la Amargura”, in Costa Rica, this street has been stigmatized as an unsafe and damaged spot. Even though, this place has a negative concept, it has a huge urban potential as a meeting point for youth; this, due to closeness to Universidad de Costa Rica. Nevertheless, situations as drugs dealing and violence have created a negative perception within people all around the country. This project of urban renewal since the beginning has sought to enhance the perception of “Calle de la Amargura” from three axes: the development of educational and leisure activities, the foundation of community working networks and the improvement of physical conditions. Interdisciplinary groups were created in different areas such as engineer, arts, social sciences, health and education. Today, this plan is a recognize project, which involves a hard work on public space appropriation. Indeed, this paper seeks to expose the high content of social action and community management process of urban renewal leading by Engineering Faculty

  3. Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests

    Science.gov (United States)

    Douglas, Freddie; Bourgeois, Edit Kaminsky

    2005-01-01

    The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).

  4. The critical chemical and mechanical regulation of folic acid on neural engineering.

    Science.gov (United States)

    Kim, Gloria B; Chen, Yongjie; Kang, Weibo; Guo, Jinshan; Payne, Russell; Li, Hui; Wei, Qiong; Baker, Julianne; Dong, Cheng; Zhang, Sulin; Wong, Pak Kin; Rizk, Elias B; Yan, Jiazhi; Yang, Jian

    2018-04-03

    The mandate of folic acid supplementation in grained products has reduced the occurrence of neural tube defects by one third in the U.S since its introduction by the Food and Drug Administration in 1998. However, the advantages and possible mechanisms of action of using folic acid for peripheral nerve engineering and neurological diseases still remain largely elusive. Herein, folic acid is described as an inexpensive and multifunctional niche component that modulates behaviors in different cells in the nervous system. The multiple benefits of modulation include: 1) generating chemotactic responses on glial cells, 2) inducing neurotrophin release, and 3) stimulating neuronal differentiation of a PC-12 cell system. For the first time, folic acid is also shown to enhance cellular force generation and global methylation in the PC-12 cells, thereby enabling both biomechanical and biochemical pathways to regulate neuron differentiation. These findings are evaluated in vivo for clinical translation. Our results suggest that folic acid-nerve guidance conduits may offer significant benefits as a low-cost, off-the-shelf product for reaching the functional recovery seen with autografts in large sciatic nerve defects. Consequently, folic acid holds great potential as a critical and convenient therapeutic intervention for neural engineering, regenerative medicine, medical prosthetics, and drug delivery. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Performance Evaluation of 14 Neural Network Architectures Used for Predicting Heat Transfer Characteristics of Engine Oils

    Science.gov (United States)

    Al-Ajmi, R. M.; Abou-Ziyan, H. Z.; Mahmoud, M. A.

    2012-01-01

    This paper reports the results of a comprehensive study that aimed at identifying best neural network architecture and parameters to predict subcooled boiling characteristics of engine oils. A total of 57 different neural networks (NNs) that were derived from 14 different NN architectures were evaluated for four different prediction cases. The NNs were trained on experimental datasets performed on five engine oils of different chemical compositions. The performance of each NN was evaluated using a rigorous statistical analysis as well as careful examination of smoothness of predicted boiling curves. One NN, out of the 57 evaluated, correctly predicted the boiling curves for all cases considered either for individual oils or for all oils taken together. It was found that the pattern selection and weight update techniques strongly affect the performance of the NNs. It was also revealed that the use of descriptive statistical analysis such as R2, mean error, standard deviation, and T and slope tests, is a necessary but not sufficient condition for evaluating NN performance. The performance criteria should also include inspection of the smoothness of the predicted curves either visually or by plotting the slopes of these curves.

  6. 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.

  7. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN

    Directory of Open Access Journals (Sweden)

    K. Prasada Rao

    2017-09-01

    Full Text Available Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility. This study investigates the performance and emission characteristics of single cylinder four stroke indirect diesel injection (IDI engine fueled with Rice Bran Methyl Ester (RBME with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN modeling. The study used IDI engine experimental data to evaluate nine engine performance and emission parameters including Exhaust Gas Temperature (E.G.T, Brake Specific Fuel Consumption (BSFC, Brake Thermal Efficiency (B.The and various emissions like Hydrocarbons (HC, Carbon monoxide (CO, Carbon dioxide (CO2, Oxygen (O2, Nitrogen oxides (NOX and smoke. For the ANN modeling standard back propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception (MLP network was used for non-linear mapping between the input and output parameters. It was found that ANN was able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.995, 0.980, 0.999, 0.985, 0.999, 0.999, 0.980, 0.999, and 0.999 for E.G.T, BSFC, B.The, HC, O2, CO2, CO, NOX, smoke respectively.

  8. Women in Science and Engineering Building Community Online

    Science.gov (United States)

    Kleinman, Sharon S.

    This article explores the constructs of online community and online social support and discusses a naturalistic case study of a public, unmoderated, online discussion group dedicated to issues of interest to women in science and engineering. The benefits of affiliation with OURNET (a pseudonym) were explored through participant observation over a 4-year period, telephone interviews with 21 subscribers, and content analysis of e-mail messages posted to the discussion group during a 125-day period. The case study findings indicated that through affiliation with the online discussion group, women in traditionally male-dominated fields expanded their professional networks, increased their knowledge, constituted and validated positive social identities, bolstered their self-confidence, obtained social support and information from people with a wide range of experiences and areas of expertise, and, most significantly, found community.

  9. Russian Academy of Engineering: a strong power for integration of engineering community

    Directory of Open Access Journals (Sweden)

    GUSEV Boris Vladimirovich

    2015-04-01

    Full Text Available Russian Academy of Engineering is legal successor of the Engineering Academy of USSR, founded by 20 ministries and departments of USSR and RSFSR on May 13, 1990. The Engineering Academy of USSR since the very beginning of its functioning, has launched its task-oriented activity on strengthening of links between science and industry, on solving the problems of using the results of basic (fundamental research and their accelerated adaptation into the industry. In the post-Soviet period, on the basis of the Academy, the Ministry of Justice of the Russian Federation, on December 24, 1991, registered the All-Russian Public Organization Russian Academy of Engineering (RAE. At the present time, RAE includes over 1350 full and corresponding members, prominent Russian scientists, engineers and industry organizers, over 200 member societies which include major Russian science & technology organizations, and over 40 regional engineering-technical structures, departments of RAE. RAE carries out large-scale work on the development of science & technology areas in science, creating new machinery and technologies, organization of efficient functioning of the Russian Engineering community. During the 25-year period of work, about 4,5 thousand new technologies were developed, over 6,5 thousand monographs were published. Over 4 thousand patents were obtained. 209 members of RAE became laureates of State Prize of USSR and RF, 376 members of RAE became laureates of Government Prize of USSR and RF. Annual value of science & research, project and other works in the area of engineering amounts from 0,5 to 1 billion roubles. This information and reference edition of the Encyclopedia of the Russian Academy of Engineering is dedicated to the 25th anniversary of the Russian Academy of Engineering. The Encyclopedia includes creative biographies of more than 1750 full and corresponding members of RAE, prominent scientists, distinguished engineers and organizers of industry

  10. Back propagation artificial neural network for community Alzheimer's disease screening in China.

    Science.gov (United States)

    Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

    2013-01-25

    Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868-0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community.

  11. Back propagation artificial neural network for community Alzheimer's disease screening in China★

    Science.gov (United States)

    Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

    2013-01-01

    Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868–0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community. PMID:25206598

  12. Performance Parameters Analysis of an XD3P Peugeot Engine Using Artificial Neural Networks (ANN) Concept in MATLAB

    Science.gov (United States)

    Rangaswamy, T.; Vidhyashankar, S.; Madhusudan, M.; Bharath Shekar, H. R.

    2015-04-01

    The current trends of engineering follow the basic rule of innovation in mechanical engineering aspects. For the engineers to be efficient, problem solving aspects need to be viewed in a multidimensional perspective. One such methodology implemented is the fusion of technologies from other disciplines in order to solve the problems. This paper mainly deals with the application of Neural Networks in order to analyze the performance parameters of an XD3P Peugeot engine (used in Ministry of Defence). The basic propaganda of the work is divided into two main working stages. In the former stage, experimentation of an IC engine is carried out in order to obtain the primary data. In the latter stage the primary database formed is used to design and implement a predictive neural network in order to analyze the output parameters variation with respect to each other. A mathematical governing equation for the neural network is obtained. The obtained polynomial equation describes the characteristic behavior of the built neural network system. Finally, a comparative study of the results is carried out.

  13. Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

    Science.gov (United States)

    Bennett, C.; Dunne, J. F.; Trimby, S.; Richardson, D.

    2017-02-01

    A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 l, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified during RAGD training, acceptably accurate cylinder pressures, and especially accurate location-of-peak-pressure, can be reconstructed robustly under generalisation conditions, making it the most practical NARX configuration and recurrent training methodology for use on production engines.

  14. Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Tosun Erdi

    2017-01-01

    Full Text Available This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

  15. A Novel Modeling Method for Aircraft Engine Using Nonlinear Autoregressive Exogenous (NARX) Models Based on Wavelet Neural Networks

    Science.gov (United States)

    Yu, Bing; Shu, Wenjun; Cao, Can

    2018-05-01

    A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine's dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.

  16. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

    Directory of Open Access Journals (Sweden)

    Bota Mihail

    2011-08-01

    Full Text Available Abstract Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871 that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED based on experimental variables and their interdependencies. The software has three parts: (a the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger

  17. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

    Science.gov (United States)

    2011-01-01

    Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized

  18. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case.

    Science.gov (United States)

    Russ, Thomas A; Ramakrishnan, Cartic; Hovy, Eduard H; Bota, Mihail; Burns, Gully A P C

    2011-08-22

    We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized bioinformatics system: the Brain

  19. Memetic Engineering as a Basis for Learning in Robotic Communities

    Science.gov (United States)

    Truszkowski, Walter F.; Rouff, Christopher; Akhavannik, Mohammad H.

    2014-01-01

    This paper represents a new contribution to the growing literature on memes. While most memetic thought has been focused on its implications on humans, this paper speculates on the role that memetics can have on robotic communities. Though speculative, the concepts are based on proven advanced multi agent technology work done at NASA - Goddard Space Flight Center and Lockheed Martin. The paper is composed of the following sections : 1) An introductory section which gently leads the reader into the realm of memes. 2) A section on memetic engineering which addresses some of the central issues with robotic learning via memes. 3) A section on related work which very concisely identifies three other areas of memetic applications, i.e., news, psychology, and the study of human behaviors. 4) A section which discusses the proposed approach for realizing memetic behaviors in robots and robotic communities. 5) A section which presents an exploration scenario for a community of robots working on Mars. 6) A final section which discusses future research which will be required to realize a comprehensive science of robotic memetics.

  20. Configurable Analog-Digital Conversion Using the Neural EngineeringFramework

    Directory of Open Access Journals (Sweden)

    Christian G Mayr

    2014-07-01

    Full Text Available Efficient Analog-Digital Converters (ADC are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e. to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF. It carries out a large fraction of the overall ADC process in the digital domain, i.e. it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic.

  1. Modelling the continuous cooling transformation diagram of engineering steels using neural networks. Part I. Phase regions

    Energy Technology Data Exchange (ETDEWEB)

    Wolk, P.J. van der; Wang, J. [Delft Univ. of Technology (Netherlands); Sietsma, J.; Zwaag, S. van der [Delft Univ. of Technology, Lab. for Materials Science (Netherlands)

    2002-12-01

    A neural network model for the calculation of the phase regions of the continuous cooling transformation (CCT) diagram of engineering steels has been developed. The model is based on experimental CCT diagrams of 459 low-alloy steels, and calculates the CCT diagram as a function of composition and austenitisation temperature. In considering the composition, 9 alloying elements are taken into account. The model reproduces the original diagrams rather accurately, with deviations that are not larger than the average experimental inaccuracy of the experimental diagrams. Therefore, it can be considered an adequate alternative to the experimental determination of the CCT diagram of a certain steel within the composition range used. The effects of alloying elements can be quantified, either individually or in combination, with the model. Nonlinear composition dependencies are observed. (orig.)

  2. Outsourcing neural active control to passive composite mechanics: a tissue engineered cyborg ray

    Science.gov (United States)

    Gazzola, Mattia; Park, Sung Jin; Park, Kyung Soo; Park, Shirley; di Santo, Valentina; Deisseroth, Karl; Lauder, George V.; Mahadevan, L.; Parker, Kevin Kit

    2016-11-01

    Translating the blueprint that stingrays and skates provide, we create a cyborg swimming ray capable of orchestrating adaptive maneuvering and phototactic navigation. The impossibility of replicating the neural system of batoids fish is bypassed by outsourcing algorithmic functionalities to the body composite mechanics, hence casting the active control problem into a design, passive one. We present a first step in engineering multilevel "brain-body-flow" systems that couple sensory information to motor coordination and movement, leading to behavior. This work paves the way for the development of autonomous and adaptive artificial creatures able to process multiple sensory inputs and produce complex behaviors in distributed systems and may represent a path toward soft-robotic "embodied cognition".

  3. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  4. STEADY STATE PERFORMANCES ANALYSIS OF MODERN MARINE TWO-STROKE LOW SPEED DIESEL ENGINE USING MLP NEURAL NETWORK MODEL

    Directory of Open Access Journals (Sweden)

    Ozren Bukovac

    2016-01-01

    Full Text Available Compared to the other marine engines for ship propulsion, turbocharged two-stroke low speed diesel engines have advantages due to their high efficiency and reliability. Modern low speed ”intelligent” marine diesel engines have a flexibility in its operation due to the variable fuel injection strategy and management of the exhaust valve drive. This paper carried out verified zerodimensional numerical simulations which have been used for MLP (Multilayer Perceptron neural network predictions of marine two-stroke low speed diesel engine steady state performances. The developed MLP neural network was used for marine engine optimized operation control. The paper presents an example of achieving lowest specific fuel consumption and for minimization of the cylinder process highest temperature for reducing NOx emission. Also, the developed neural network was used to achieve optimal exhaust gases heat flow for utilization. The obtained data maps give insight into the optimal working areas of simulated marine diesel engine, depending on the selected start of the fuel injection (SOI and the time of the exhaust valve opening (EVO.

  5. Tinkering and Technical Self-Efficacy of Engineering Students at the Community College

    Science.gov (United States)

    Baker, Dale R.; Wood, Lorelei; Corkins, James; Krause, Stephen

    2015-01-01

    Self-efficacy in engineering is important because individuals with low self-efficacy have lower levels of achievement and persistence in engineering majors. To examine self-efficacy among community college engineering students, an instrument to specifically measure two important aspects of engineering, tinkering and technical self-efficacy, was…

  6. Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network

    International Nuclear Information System (INIS)

    Özgören, Yaşar Önder; Çetinkaya, Selim; Sarıdemir, Suat; Çiçek, Adem; Kara, Fuat

    2013-01-01

    Highlights: ► Max torque and power values were obtained at 3.5 bar Pch, 1273 K Hst and 1.4:1 r. ► According to ANOVA, the most influential parameter on power was Hst with 48.75%. ► According to ANOVA, the most influential parameter on torque was Hst with 41.78%. ► ANN (R 2 = 99.8% for T, P) was superior to regression method (R 2 = 92% for T, 81% for P). ► LM was the best learning algorithm in predicting both power and torque. - Abstract: In this study, an artificial neural network (ANN) model was developed to predict the torque and power of a beta-type Stirling engine using helium as the working fluid. The best results were obtained by 5-11-7-1 and 5-13-7-1 network architectures, with double hidden layers for the torque and power respectively. For these network architectures, the Levenberg–Marquardt (LM) learning algorithm was used. Engine performance values predicted with the developed ANN model were compared with the actual performance values measured experimentally, and substantially coinciding results were observed. After ANN training, correlation coefficients (R 2 ) of both engine performance values for testing and training data were very close to 1. Similarly, root-mean-square error (RMSE) and mean error percentage (MEP) values for the testing and training data were less than 0.02% and 3.5% respectively. These results showed that the ANN is an acceptable model for prediction of the torque and power of the beta-type Stirling engine

  7. Engineering a plant community to deliver multiple ecosystem services.

    Science.gov (United States)

    Storkey, Jonathan; Döring, Thomas; Baddeley, John; Collins, Rosemary; Roderick, Stephen; Jones, Hannah; Watson, Christine

    2015-06-01

    The sustainable delivery of multiple ecosystem services requires the management of functionally diverse biological communities. In an agricultural context, an emphasis on food production has often led to a loss of biodiversity to the detriment of other ecosystem services such as the maintenance of soil health and pest regulation. In scenarios where multiple species can be grown together, it may be possible to better balance environmental and agronomic services through the targeted selection of companion species. We used the case study of legume-based cover crops to engineer a plant community that delivered the optimal balance of six ecosystem services: early productivity, regrowth following mowing, weed suppression, support of invertebrates, soil fertility building (measured as yield of following crop), and conservation of nutrients in the soil. An experimental species pool of 12 cultivated legume species was screened for a range of functional traits and ecosystem services at five sites across a geographical gradient in the United Kingdom. All possible species combinations were then analyzed, using a process-based model of plant competition, to identify the community that delivered the best balance of services at each site. In our system, low to intermediate levels of species richness (one to four species) that exploited functional contrasts in growth habit and phenology were identified as being optimal. The optimal solution was determined largely by the number of species and functional diversity represented by the starting species pool, emphasizing the importance of the initial selection of species for the screening experiments. The approach of using relationships between functional traits and ecosystem services to design multifunctional biological communities has the potential to inform the design of agricultural systems that better balance agronomic and environmental services and meet the current objective of European agricultural policy to maintain viable food

  8. Reformulated Neural Network (ReNN): a New Alternative for Data-driven Modelling in Hydrology and Water Resources Engineering

    Science.gov (United States)

    Razavi, S.; Tolson, B.; Burn, D.; Seglenieks, F.

    2012-04-01

    Reformulated Neural Network (ReNN) has been recently developed as an efficient and more effective alternative to feedforward multi-layer perceptron (MLP) neural networks [Razavi, S., and Tolson, B. A. (2011). "A new formulation for feedforward neural networks." IEEE Transactions on Neural Networks, 22(10), 1588-1598, DOI: 1510.1109/TNN.2011.2163169]. This presentation initially aims to introduce the ReNN to the water resources community and then demonstrates ReNN applications to water resources related problems. ReNN is essentially equivalent to a single-hidden-layer MLP neural network but defined on a new set of network variables which is more effective than the traditional set of network weights and biases. The main features of the new network variables are that they are geometrically interpretable and each variable has a distinct role in forming the network response. ReNN is more efficiently trained as it has a less complex error response surface. In addition to the ReNN training efficiency, the interpretability of the ReNN variables enables the users to monitor and understand the internal behaviour of the network while training. Regularization in the ReNN response can be also directly measured and controlled. This feature improves the generalization ability of the network. The appeal of the ReNN is demonstrated with two ReNN applications to water resources engineering problems. In the first application, the ReNN is used to model the rainfall-runoff relationships in multiple watersheds in the Great Lakes basin located in northeastern North America. Modelling inflows to the Great Lakes are of great importance to the management of the Great Lakes system. Due to the lack of some detailed physical data about existing control structures in many subwatersheds of this huge basin, the data-driven approach to modelling such as the ReNN are required to replace predictions from a physically-based rainfall runoff model. Unlike traditional MLPs, the ReNN does not necessarily

  9. Broadening engineering education: bringing the community in : commentary on "social responsibility in French engineering education: a historical and sociological analysis".

    Science.gov (United States)

    Conlon, Eddie

    2013-12-01

    Two issues of particular interest in the Irish context are (1) the motivation for broadening engineering education to include the humanities, and an emphasis on social responsibility and (2) the process by which broadening can take place. Greater community engagement, arising from a socially-driven model of engineering education, is necessary if engineering practice is to move beyond its present captivity by corporate interests.

  10. A facile route to the synthesis of anilinic electroactive colloidal hydrogels for neural tissue engineering applications.

    Science.gov (United States)

    Zarrintaj, Payam; Urbanska, Aleksandra M; Gholizadeh, Saman Seyed; Goodarzi, Vahabodin; Saeb, Mohammad Reza; Mozafari, Masoud

    2018-04-15

    An innovative drug-loaded colloidal hydrogel was synthesized for applications in neural interfaces in tissue engineering by reacting carboxyl capped aniline dimer and gelatin molecules. Dexamethasone was loaded into the gelatin-aniline dimer solution as a model drug to form an in situ drug-loaded colloidal hydrogel. The conductivity of the hydrogel samples fluctuated around 10 -5  S/cm which appeared suitable for cellular activities. Cyclic voltammetry was used for electroactivity determination, in which 2 redox states were observed, suggesting that the short chain length and steric hindrance prevented the gel from achieving a fully oxidized state. Rheological data depicted the modulus decreasing with aniline dimer increment due to limited hydrogen bonds accessibility. Though the swelling ratio of pristine gelatin (600%) decreased by the introduction and increasing the concentration of aniline dimer because of its hydrophobic nature, it took the value of 300% at worst, which still seems promising for drug delivery uses. Degradation rate of hydrogel was similarly decreased by adding aniline dimer. Drug release was evaluated in passive and stimulated patterns demonstrating tendency of aniline dimer to form a vesicle that controls the drug release behavior. The optimal cell viability, proper cell attachment and neurite extension was achieved in the case of hydrogel containing 10 wt% aniline dimer. Based on tissue/organ behavior, it was promisingly possible to adjust the characteristics of the hydrogels for an optimal drug release. The outcome of this simple and effective approach can potentially offer additional tunable characteristics for recording and stimulating purposes in neural interfaces. Copyright © 2018 Elsevier Inc. All rights reserved.

  11. Estimating Survival Rates in Engineering for Community College Transfer Students Using Grades in Calculus and Physics

    Science.gov (United States)

    Laugerman, Marcia; Shelley, Mack; Rover, Diane; Mickelson, Steve

    2015-01-01

    This study uses a unique synthesized set of data for community college students transferring to engineering by combining several cohorts of longitudinal data along with transcript-level data, from both the Community College and the University, to measure success rates in engineering. The success rates are calculated by developing Kaplan-Meier…

  12. A Qualitative Study of African American Women in Engineering Technology Programs in Community Colleges

    Science.gov (United States)

    Blakley, Jacquelyn

    2016-01-01

    This study examined the experiences of African American women in engineering technology programs in community colleges. There is a lack of representation of African American women in engineering technology programs throughout higher education, especially in community/technical colleges. There is also lack of representation of African American…

  13. Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies

    Science.gov (United States)

    Protalinsky, O. M.; Shcherbatov, I. A.; Stepanov, P. V.

    2017-11-01

    A growing number of severe accidents in RF call for the need to develop a system that could prevent emergency situations. In a number of cases accident rate is stipulated by careless inspections and neglects in developing repair programs. Across the country rates of accidents are growing because of a so-called “human factor”. In this regard, there has become urgent the problem of identification of the actual state of technological facilities in power engineering using data on engineering processes running and applying artificial intelligence methods. The present work comprises four model states of manufacturing equipment of engineering companies: defect, failure, preliminary situation, accident. Defect evaluation is carried out using both data from SCADA and ASEPCR and qualitative information (verbal assessments of experts in subject matter, photo- and video materials of surveys processed using pattern recognition methods in order to satisfy the requirements). Early identification of defects makes possible to predict the failure of manufacturing equipment using mathematical techniques of artificial neural network. In its turn, this helps to calculate predicted characteristics of reliability of engineering facilities using methods of reliability theory. Calculation of the given parameters provides the real-time estimation of remaining service life of manufacturing equipment for the whole operation period. The neural networks model allows evaluating possibility of failure of a piece of equipment consistent with types of actual defects and their previous reasons. The article presents the grounds for a choice of training and testing samples for the developed neural network, evaluates the adequacy of the neural networks model, and shows how the model can be used to forecast equipment failure. There have been carried out simulating experiments using a computer and retrospective samples of actual values for power engineering companies. The efficiency of the developed

  14. Cell surface glycan engineering of neural stem cells augments neurotropism and improves recovery in a murine model of multiple sclerosis

    KAUST Repository

    Merzaban, Jasmeen S.

    2015-09-13

    Neural stem cell (NSC)-based therapies offer potential for neural repair in central nervous system (CNS) inflammatory and degenerative disorders. Typically, these conditions present with multifocal CNS lesions making it impractical to inject NSCs locally, thus mandating optimization of vascular delivery of the cells to involved sites. Here, we analyzed NSCs for expression of molecular effectors of cell migration and found that these cells are natively devoid of E-selectin ligands. Using glycosyltransferase-programmed stereosubstitution (GPS), we glycan engineered the cell surface of NSCs ("GPS-NSCs") with resultant enforced expression of the potent E-selectin ligand HCELL (hematopoietic cell E-/L-selectin ligand) and of an E-selectin-binding glycoform of neural cell adhesion molecule ("NCAM-E"). Following intravenous (i.v.) injection, short-term homing studies demonstrated that, compared with buffer-treated (control) NSCs, GPS-NSCs showed greater neurotropism. Administration of GPS-NSC significantly attenuated the clinical course of experimental autoimmune encephalomyelitis (EAE), with markedly decreased inflammation and improved oligodendroglial and axonal integrity, but without evidence of long-term stem cell engraftment. Notably, this effect of NSC is not a universal property of adult stem cells, as administration of GPS-engineered mouse hematopoietic stem/progenitor cells did not improve EAE clinical course. These findings highlight the utility of cell surface glycan engineering to boost stem cell delivery in neuroinflammatory conditions and indicate that, despite the use of a neural tissue-specific progenitor cell population, neural repair in EAE results from endogenous repair and not from direct, NSC-derived cell replacement.

  15. Community structure analysis of rejection sensitive personality profiles: A common neural response to social evaluative threat?

    Science.gov (United States)

    Kortink, Elise D; Weeda, Wouter D; Crowley, Michael J; Gunther Moor, Bregtje; van der Molen, Melle J W

    2018-06-01

    Monitoring social threat is essential for maintaining healthy social relationships, and recent studies suggest a neural alarm system that governs our response to social rejection. Frontal-midline theta (4-8 Hz) oscillatory power might act as a neural correlate of this system by being sensitive to unexpected social rejection. Here, we examined whether frontal-midline theta is modulated by individual differences in personality constructs sensitive to social disconnection. In addition, we examined the sensitivity of feedback-related brain potentials (i.e., the feedback-related negativity and P3) to social feedback. Sixty-five undergraduate female participants (mean age = 19.69 years) participated in the Social Judgment Paradigm, a fictitious peer-evaluation task in which participants provided expectancies about being liked/disliked by peer strangers. Thereafter, they received feedback signaling social acceptance/rejection. A community structure analysis was employed to delineate personality profiles in our data. Results provided evidence of two subgroups: one group scored high on attachment-related anxiety and fear of negative evaluation, whereas the other group scored high on attachment-related avoidance and low on fear of negative evaluation. In both groups, unexpected rejection feedback yielded a significant increase in theta power. The feedback-related negativity was sensitive to unexpected feedback, regardless of valence, and was largest for unexpected rejection feedback. The feedback-related P3 was significantly enhanced in response to expected social acceptance feedback. Together, these findings confirm the sensitivity of frontal midline theta oscillations to the processing of social threat, and suggest that this alleged neural alarm system behaves similarly in individuals that differ in personality constructs relevant to social evaluation.

  16. Neural tissue engineering scaffold with sustained RAPA release relieves neuropathic pain in rats.

    Science.gov (United States)

    Ding, Tan; Zhu, Chao; Kou, Zhen-Zhen; Yin, Jun-Bin; Zhang, Ting; Lu, Ya-Cheng; Wang, Li-Ying; Luo, Zhuo-Jing; Li, Yun-Qing

    2014-09-01

    To investigate the effect of locally slow-released rapamycin (RAPA) from bionic peripheral nerve stent to reduce the incidence of neuropathic pain or mitigate the degree of pain after nerve injury. We constructed a neural tissue engineering scaffold with sustained release of RAPA to repair 20mm defects in rat sciatic nerves. Four presurgical and postsurgical time windows were selected to monitor the changes in the expression of pain-related dorsal root ganglion (DRG) voltage-gated sodium channels 1.3 (Nav1.3), 1.7 (Nav1.7), and 1.8 (Nav1.8) through immunohistochemistry (IHC) and Western Blot, along with the observation of postsurgical pathological pain in rats by pain-related behavior approaches. Relatively small upregulation of DRG sodium channels was observed in the experimental group (RAPA+poly(lactic-co-glycolic acid) (PLGA)+stent) after surgery, along with low degrees of neuropathic pain and anxiety, which were similar to those in the Autologous nerve graft group. Autoimmune inflammatory response plays a leading role in the occurrence of post-traumatic neuropathic pain, and that RAPA significantly inhibits the abnormal upregulation of sodium channels to reduce pain by alleviating inflammatory response. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

    Science.gov (United States)

    Ferentinos, Konstantinos P

    2005-09-01

    Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

  18. CONCEPTION OF USE VIBROACOUSTIC SIGNALS AND NEURAL NETWORKS FOR DIAGNOSING OF CHOSEN ELEMENTS OF INTERNAL COMBUSTION ENGINES IN CAR VEHICLES

    Directory of Open Access Journals (Sweden)

    Piotr CZECH

    2014-03-01

    Full Text Available Currently used diagnostics systems are not always efficient and do not give straightforward results which allow for the assessment of the technological condition of the engine or for the identification of the possible damages in their early stages of development. Growing requirements concerning durability, reliability, reduction of costs to minimum and decrease of negative influence on the natural environment are the reasons why there is a need to acquire information about the technological condition of each of the elements of a vehicle during its exploitation. One of the possibilities to achieve information about technological condition of a vehicle are vibroacoustic phenomena. Symptoms of defects, achieved as a result of advanced methods of vibroacoustic signals processing can serve as models which can be used during construction of intelligent diagnostic system based on artificial neural networks. The work presents conception of use artificial neural networks in the task of combustion engines diagnosis.

  19. Fabrication of Chitosan/Poly (vinyl alcohol/Carbon Nanotube/Bioactive Glass Nanocomposite Scaffolds for Neural Tissue Engineering

    Directory of Open Access Journals (Sweden)

    S. Nikbakht Katouli

    2016-06-01

    5 and 10 wt% incorporated electrospun chitosan (CS/polyvinyl alcohol (PVA nanofibers for potential neural tissue engineering applications.The morphology, structure, and mechanical properties of the formed electrospun fibrous mats were characterized using scanning electron microscopy (SEM and mechanical testing, respectively. In vitro cell culture of embryonal carcinoma stem cells (P19 were seeded onto the electrospun scaffolds. The results showed that the incorporation of CNTs and BG nanoparticles did not appreciably affect the morphology of the CS/PVA nanofibers. The maximum tensile strength (7.9 MPa was observed in the composite sample with 5 %wt bioactive glass nanoparticles. The results suggest that BG and CNT-incorporated CS/PVA nanofibrous scaffolds with small diameters, high porosity, and promoted mechanical properties can potentially provide many possibilities for applications in the fields of neural tissue engineering and regenerative medicine.

  20. Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities

    Science.gov (United States)

    Yusufaly, Tahir I.; Boedicker, James Q.

    2017-08-01

    Microbial communities frequently communicate via quorum sensing (QS), where cells produce, secrete, and respond to a threshold level of an autoinducer (AI) molecule, thereby modulating gene expression. However, the biology of QS remains incompletely understood in heterogeneous communities, where variant bacterial strains possess distinct QS systems that produce chemically unique AIs. AI molecules bind to ‘cognate’ receptors, but also to ‘non-cognate’ receptors found in other strains, resulting in inter-strain crosstalk. Understanding these interactions is a prerequisite for deciphering the consequences of crosstalk in real ecosystems, where multiple AIs are regularly present in the same environment. As a step towards this goal, we map crosstalk in a heterogeneous community of variant QS strains onto an artificial neural network model. This formulation allows us to systematically analyze how crosstalk regulates the community’s capacity for flexible decision making, as quantified by the Boltzmann entropy of all QS gene expression states of the system. In a mean-field limit of complete cross-inhibition between variant strains, the model is exactly solvable, allowing for an analytical formula for the number of variants that maximize capacity as a function of signal kinetics and activation parameters. An analysis of previous experimental results on the Staphylococcus aureus two-component Agr system indicates that the observed combination of variant numbers, gene expression rates and threshold concentrations lies near this critical regime of parameter space where capacity peaks. The results are suggestive of a potential evolutionary driving force for diversification in certain QS systems.

  1. Advanced biomaterial strategies to transplant preformed micro-tissue engineered neural networks into the brain

    Science.gov (United States)

    Harris, J. P.; Struzyna, L. A.; Murphy, P. L.; Adewole, D. O.; Kuo, E.; Cullen, D. K.

    2016-02-01

    Objective. Connectome disruption is a hallmark of many neurological diseases and trauma with no current strategies to restore lost long-distance axonal pathways in the brain. We are creating transplantable micro-tissue engineered neural networks (micro-TENNs), which are preformed constructs consisting of embedded neurons and long axonal tracts to integrate with the nervous system to physically reconstitute lost axonal pathways. Approach. We advanced micro-tissue engineering techniques to generate micro-TENNs consisting of discrete populations of mature primary cerebral cortical neurons spanned by long axonal fascicles encased in miniature hydrogel micro-columns. Further, we improved the biomaterial encasement scheme by adding a thin layer of low viscosity carboxymethylcellulose (CMC) to enable needle-less insertion and rapid softening for mechanical similarity with brain tissue. Main results. The engineered architecture of cortical micro-TENNs facilitated robust neuronal viability and axonal cytoarchitecture to at least 22 days in vitro. Micro-TENNs displayed discrete neuronal populations spanned by long axonal fasciculation throughout the core, thus mimicking the general systems-level anatomy of gray matter—white matter in the brain. Additionally, micro-columns with thin CMC-coating upon mild dehydration were able to withstand a force of 893 ± 457 mN before buckling, whereas a solid agarose cylinder of similar dimensions was predicted to withstand less than 150 μN of force. This thin CMC coating increased the stiffness by three orders of magnitude, enabling needle-less insertion into brain while significantly reducing the footprint of previous needle-based delivery methods to minimize insertion trauma. Significance. Our novel micro-TENNs are the first strategy designed for minimally invasive implantation to facilitate nervous system repair by simultaneously providing neuronal replacement and physical reconstruction of long-distance axon pathways in the brain

  2. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ghobadian, B.; Rahimi, H.; Nikbakht, A.M.; Najafi, G. [Tarbiat Modares University, P.O. Box 14115-111, Tehran (Iran); Yusaf, T.F. [University of Southern Queensland, Toowoomba 4350 QLD (Australia)

    2009-04-15

    This study deals with artificial neural network (ANN) modeling of a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results revealed that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model was developed based on standard Back-Propagation algorithm for the engine. Multi layer perception network (MLP) was used for non-linear mapping between the input and output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0004 by the model. (author)

  3. Neural Networks: Implementations and Applications

    OpenAIRE

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  4. Research of performance on a spark ignition engine fueled by alcohol–gasoline blends using artificial neural networks

    International Nuclear Information System (INIS)

    Kapusuz, Murat; Ozcan, Hakan; Yamin, Jehad Ahmad

    2015-01-01

    In this paper, we investigate various alcohol–unleaded gasoline mixtures that can be used with no modifications in a spark-ignition engine. The mixtures consisted of 5%, 10% and 15% ethanol, methanol together and separately. Based on the recommendations of the Jordanian Petroleum Company (JoPetrol), total alcohol content should not exceed 15–20% owing to safety and ignition hazards. Optimizations for the use of alcohol were made for the maximum torque, maximum power and minimum specific fuel consumption values. For torque 0.9906, for brake power 0.997, and for brake specific fuel consumption 0.9312 regression values for tests have been obtained from models generated by the neural network. According to the modeling and optimizations, use of fuel mixture containing 11% methanol–1% ethanol for performance, and fuel mixture containing 2% methanol for BSFC were found to have better results. Moreover, the paper demonstrates that ANN (Artificial Neural Network) can be used successfully as an alternative type of modeling technique for internal combustion engines. - Highlights: • ANN model was developed and verified. • Effects of alcohol–gasoline blends on performance of a SI engine are fairly simulated. • Effects of alcohol–gasoline blends on performance of a SI engine are optimized.

  5. Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity

    International Nuclear Information System (INIS)

    Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Moztarzadeh, Fathollah; Sadeghi, Ali

    2016-01-01

    Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell–scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. - Highlights: • This paper focuses on design a high porous nanofibrous scaffold. • Hyaluronic acid and polycaprolactone were used as materials to provide ideal conditions for nerve regeneration. • Proper physicochemical and mechanical signals applied for improving cell attachment

  6. Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity.

    Science.gov (United States)

    Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Moztarzadeh, Fathollah; Sadeghi, Ali

    2016-12-01

    Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell-scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity

    Energy Technology Data Exchange (ETDEWEB)

    Entekhabi, Elahe [Department of Biomedical Engineering, Amirkabir University of Technology, P.O. Box: 15875/4413, Tehran 159163/4311 (Iran, Islamic Republic of); Haghbin Nazarpak, Masoumeh, E-mail: mhaghbinn@gmail.com [New Technologies Research Center (NTRC), Amirkabir University of Technology, Tehran 15875-4413 (Iran, Islamic Republic of); Moztarzadeh, Fathollah; Sadeghi, Ali [Department of Biomedical Engineering, Amirkabir University of Technology, P.O. Box: 15875/4413, Tehran 159163/4311 (Iran, Islamic Republic of)

    2016-12-01

    Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell–scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. - Highlights: • This paper focuses on design a high porous nanofibrous scaffold. • Hyaluronic acid and polycaprolactone were used as materials to provide ideal conditions for nerve regeneration. • Proper physicochemical and mechanical signals applied for improving cell attachment.

  8. Ordination of self-organizing feature map neural networks and its application to the study of plant communities

    Institute of Scientific and Technical Information of China (English)

    Jintun ZHANG; Dongping MENG; Yuexiang XI

    2009-01-01

    A self-organizing feature map (SOFM) neural network is a powerful tool in analyzing and solving complex, non-linear problems. According to its features, a SOFM is entirely compatible with ordination studies of plant communities. In our present work, mathematical principles, and ordination techniques and procedures are introduced. A SOFM ordination was applied to the study of plant communities in the middle of the Taihang mountains. The ordination was carried out by using the NNTool box in MATLAB. The results of 68 quadrats of plant communities were distributed in SOFM space. The ordination axes showed the ecological gradients clearly and provided the relationships between communities with ecological meaning. The results are consistent with the reality of vegetation in the study area. This suggests that SOFM ordination is an effective technique in plant ecology. During ordination procedures, it is easy to carry out clustering of communities and so it is beneficial for combining classification and ordination in vegetation studies.

  9. Engineering the Brain: Ethical Issues and the Introduction of Neural Devices.

    Science.gov (United States)

    Klein, Eran; Brown, Tim; Sample, Matthew; Truitt, Anjali R; Goering, Sara

    2015-01-01

    Neural devices now under development stand to interact with and alter the human brain in ways that may challenge standard notions of identity, normality, authority, responsibility, privacy and justice.

  10. Teaching "Community Engagement" in Engineering Education for International Development: Integration of an Interdisciplinary Social Work Curriculum

    Science.gov (United States)

    Gilbert, Dorie J.; Held, Mary Lehman; Ellzey, Janet L.; Bailey, William T.; Young, Laurie B.

    2015-01-01

    This article reviews the literature on challenges faced by engineering faculty in educating their students on community-engaged, sustainable technical solutions in developing countries. We review a number of approaches to increasing teaching modules on social and community components of international development education, from adding capstone…

  11. Developing an Understanding of Higher Education Science and Engineering Learning Communities

    Science.gov (United States)

    Coll, Richard K.; Eames, Chris

    2008-01-01

    This article sets the scene for this special issue of "Research in Science & Technological Education", dedicated to understanding higher education science and engineering learning communities. We examine what the literature has to say about the nature of, and factors influencing, higher education learning communities. A discussion of…

  12. New bioactive motifs and their use in functionalized self-assembling peptides for NSC differentiation and neural tissue engineering

    Science.gov (United States)

    Gelain, F.; Cigognini, D.; Caprini, A.; Silva, D.; Colleoni, B.; Donegá, M.; Antonini, S.; Cohen, B. E.; Vescovi, A.

    2012-04-01

    Developing functionalized biomaterials for enhancing transplanted cell engraftment in vivo and stimulating the regeneration of injured tissues requires a multi-disciplinary approach customized for the tissue to be regenerated. In particular, nervous tissue engineering may take a great advantage from the discovery of novel functional motifs fostering transplanted stem cell engraftment and nervous fiber regeneration. Using phage display technology we have discovered new peptide sequences that bind to murine neural stem cell (NSC)-derived neural precursor cells (NPCs), and promote their viability and differentiation in vitro when linked to LDLK12 self-assembling peptide (SAPeptide). We characterized the newly functionalized LDLK12 SAPeptides via atomic force microscopy, circular dichroism and rheology, obtaining nanostructured hydrogels that support human and murine NSC proliferation and differentiation in vitro. One functionalized SAPeptide (Ac-FAQ), showing the highest stem cell viability and neural differentiation in vitro, was finally tested in acute contusive spinal cord injury in rats, where it fostered nervous tissue regrowth and improved locomotor recovery. Interestingly, animals treated with the non-functionalized LDLK12 had an axon sprouting/regeneration intermediate between Ac-FAQ-treated animals and controls. These results suggest that hydrogels functionalized with phage-derived peptides may constitute promising biomimetic scaffolds for in vitro NSC differentiation, as well as regenerative therapy of the injured nervous system. Moreover, this multi-disciplinary approach can be used to customize SAPeptides for other specific tissue engineering applications.Developing functionalized biomaterials for enhancing transplanted cell engraftment in vivo and stimulating the regeneration of injured tissues requires a multi-disciplinary approach customized for the tissue to be regenerated. In particular, nervous tissue engineering may take a great advantage from the

  13. Invention through bricolage: epistemic engineering in scientific communities

    Directory of Open Access Journals (Sweden)

    Alexander James Gillett

    2018-03-01

    Full Text Available It is widely recognised that knowledge accumulation is an important aspect of scientific communities. In this essay, drawing on a range of material from theoretical biology and behavioural science, I discuss a particular aspect of the intergenerational nature of human communities – “virtual collaboration” (Tomasello 1999 – and how it can lead to epistemic progress without any explicit intentional creativity (Henrich 2016. My aim in this paper is to make this work relevant to theorists working on the social structures of science so that these processes can be utilised and optimised in scientific communities.

  14. Assessment of community noise for a medium-range airplane with open-rotor engines

    Science.gov (United States)

    Kopiev, V. F.; Shur, M. L.; Travin, A. K.; Belyaev, I. V.; Zamtfort, B. S.; Medvedev, Yu. V.

    2017-11-01

    Community noise of a hypothetical medium-range airplane equipped with open-rotor engines is assessed by numerical modeling of the aeroacoustic characteristics of an isolated open rotor with the simplest blade geometry. Various open-rotor configurations are considered at constant thrust, and the lowest-noise configuration is selected. A two-engine medium-range airplane at known thrust of bypass turbofan engines at different segments of the takeoff-landing trajectory is considered, after the replacement of those engines by the open-rotor engines. It is established that a medium-range airplane with two open-rotor engines meets the requirements of Chapter 4 of the ICAO standard with a significant margin. It is shown that airframe noise makes a significant contribution to the total noise of an airplane with open-rotor engines at landing.

  15. Teaching `community engagement' in engineering education for international development: Integration of an interdisciplinary social work curriculum

    Science.gov (United States)

    Gilbert, Dorie J.; Lehman Held, Mary; Ellzey, Janet L.; Bailey, William T.; Young, Laurie B.

    2015-05-01

    This article reviews the literature on challenges faced by engineering faculty in educating their students on community-engaged, sustainable technical solutions in developing countries. We review a number of approaches to increasing teaching modules on social and community components of international development education, from adding capstone courses and educational track seminars to integrating content from other disciplines, particularly the social sciences. After summarising recent pedagogical strategies to increase content on community-focused development, we present a case study of how one engineering programme incorporates social work students and faculty to infuse strategies for community engagement in designing and implementing student-led global engineering development projects. We outline how this interdisciplinary pedagogical approach teaches students from the two disciplines to work together in addressing power balances, economic and social issues and overall sustainability of international development projects.

  16. Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network

    DEFF Research Database (Denmark)

    Mozaffari, Ahmad; Gorji-Bandpy, Mofid; Samadian, Pendar

    2013-01-01

    Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines...... and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four...... well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable...

  17. A Neural Network Aero Design System for Advanced Turbo-Engines

    Science.gov (United States)

    Sanz, Jose M.

    1999-01-01

    An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a neural network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. The neural network technique works well not only as an interpolating device but also as an extrapolating device to achieve blade designs from a given database. Two validating test cases are discussed.

  18. Benefiting Female Students in Science, Math, and Engineering: The Nuts and Bolts of Establishing a WISE (Women in Science and Engineering) Learning Community

    Science.gov (United States)

    Pace, Diana; Witucki, Laurie; Blumreich, Kathleen

    2008-01-01

    This paper describes the rationale and the step by step process for setting up a WISE (Women in Science and Engineering) learning community at one institution. Background information on challenges for women in science and engineering and the benefits of a learning community for female students in these major areas are described. Authors discuss…

  19. KNOVEL: A NEW SERVICE FOR THE ENGINEERING COMMUNITY

    CERN Multimedia

    2001-01-01

    Electronic books available on trial at CERN Electronic preprints and journals have become tools used on a daily basis, but so far the CERN Library did not provide a significant collection of electronic books. This is now about to change, so searching for scientific information, and in particular engineering-related references, is now easier than ever before. CERN has access to the electronic book collection via Knovel on a trial base until Xmas. Knovel has a database of some of the leading engineering reference handbooks and conference proceedings, published by Reed Elsevier, ASME, ASM, Butterworth, CRC Press, McGraw-Hill and others. The full-text of all e-books can be searched simultaneously. Another Knovel feature allows users to search for data (materials properties) across the whole digital collection. The Web search engine and display interface has been developed to support a wide range of information and file types: text, tables, equations, graphics, figures. This new resource is linked to from the Libr...

  20. Layers and Multilayers of Self-Assembled Polymers: Tunable Engineered Extracellular Matrix Coatings for Neural Cell Growth.

    Science.gov (United States)

    Landry, Michael J; Rollet, Frédéric-Guillaume; Kennedy, Timothy E; Barrett, Christopher J

    2018-03-12

    Growing primary cells and tissue in long-term cultures, such as primary neural cell culture, presents many challenges. A critical component of any environment that supports neural cell growth in vivo is an appropriate 2-D surface or 3-D scaffold, typically in the form of a thin polymer layer that coats an underlying plastic or glass substrate and aims to mimic critical aspects of the extracellular matrix. A fundamental challenge to mimicking a hydrophilic, soft natural cell environment is that materials with these properties are typically fragile and are difficult to adhere to and stabilize on an underlying plastic or glass cell culture substrate. In this review, we highlight the current state of the art and overview recent developments of new artificial extracellular matrix (ECM) surfaces for in vitro neural cell culture. Notably, these materials aim to strike a balance between being hydrophilic and soft while also being thick, stable, robust, and bound well to the underlying surface to provide an effective surface to support long-term cell growth. We focus on improved surface and scaffold coating systems that can mimic the natural physicochemical properties that enhance neuronal survival and growth, applied as soft hydrophilic polymer coatings for both in vitro cell culture and for implantable neural probes and 3-D matrixes that aim to enhance stability and longevity to promote neural biocompatibility in vivo. With respect to future developments, we outline four emerging principles that serve to guide the development of polymer assemblies that function well as artificial ECMs: (a) design inspired by biological systems and (b) the employment of principles of aqueous soft bonding and self-assembly to achieve (c) a high-water-content gel-like coating that is stable over time in a biological environment and possesses (d) a low modulus to more closely mimic soft, compliant real biological tissue. We then highlight two emerging classes of thick material coatings that

  1. Wavelet neural networks with applications in financial engineering, chaos, and classification

    CERN Document Server

    Alexandridis, Antonios K

    2014-01-01

    Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the mathematical and statistical framework needed for model selection, variable selection, wavelet network construction, initialization, training, forecasting and prediction, confidence intervals, prediction intervals, and model adequacy testing. The text is ideal for

  2. Engineering Hybrid Learning Communities: The Case of a Regional Parent Community

    Directory of Open Access Journals (Sweden)

    Sven Strickroth

    2014-09-01

    Full Text Available We present an approach (and a corresponding system design for supporting regionally bound hybrid learning communities (i.e., communities which combine traditional face-to-face elements with web based media such as online community platforms, e-mail and SMS newsletters. The goal of the example community used to illustrate the approach was to support and motivate (especially hard-to-reach underprivileged parents in the education of their young children. The article describes the design process used and the challenges faced during the socio-technical system design. An analysis of the community over more than one year indicates that the hybrid approach works better than the two separated “traditional” approaches separately. Synergy effects like advertising effects from the offline trainings for the online platform and vice versa occurred and regular newsletters turned out to have a noticeable effect on the community.

  3. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Najafi, G.; Ghobadian, B.; Tavakoli, T.; Faizollahnejad, M. [Tarbiat Modares University, Jalale-E-Aleahmad Highway, Tehran, P.O. Box: 14115-111 (Iran); Buttsworth, D.R.; Yusaf, T.F. [University of Southern Queensland, Toowoomba, 4350 QLD (Australia)

    2009-05-15

    The purpose of this study is to experimentally analyse the performance and the pollutant emissions of a four-stroke SI engine operating on ethanol-gasoline blends of 0%, 5%, 10%, 15% and 20% with the aid of artificial neural network (ANN). The properties of bioethanol were measured based on American Society for Testing and Materials (ASTM) standards. The experimental results revealed that using ethanol-gasoline blended fuels increased the power and torque output of the engine marginally. For ethanol blends it was found that the brake specific fuel consumption (bsfc) was decreased while the brake thermal efficiency ({eta}{sub b.th.}) and the volumetric efficiency ({eta}{sub v}) were increased. The concentration of CO and HC emissions in the exhaust pipe were measured and found to be decreased when ethanol blends were introduced. This was due to the high oxygen percentage in the ethanol. In contrast, the concentration of CO{sub 2} and NO{sub x} was found to be increased when ethanol is introduced. An ANN model was developed to predict a correlation between brake power, torque, brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and emission components using different gasoline-ethanol blends and speeds as inputs data. About 70% of the total experimental data were used for training purposes, while the 30% were used for testing. A standard Back-Propagation algorithm for the engine was used in this model. A multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. It was observed that the ANN model can predict engine performance and exhaust emissions with correlation coefficient (R) in the range of 0.97-1. Mean relative errors (MRE) values were in the range of 0.46-5.57%, while root mean square errors (RMSE) were found to be very low. This study demonstrates that ANN approach can be used to accurately predict the SI engine performance and emissions. (author)

  4. Proteomic Profiling of Neuroblastoma Cells Adhesion on Hyaluronic Acid-Based Surface for Neural Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Ming-Hui Yang

    2016-01-01

    Full Text Available The microenvironment of neuron cells plays a crucial role in regulating neural development and regeneration. Hyaluronic acid (HA biomaterial has been applied in a wide range of medical and biological fields and plays important roles in neural regeneration. PC12 cells have been reported to be capable of endogenous NGF synthesis and secretion. The purpose of this research was to assess the effect of HA biomaterial combining with PC12 cells conditioned media (PC12 CM in neural regeneration. Using SH-SY5Y cells as an experimental model, we found that supporting with PC12 CM enhanced HA function in SH-SY5Y cell proliferation and adhesion. Through RP-nano-UPLC-ESI-MS/MS analyses, we identified increased expression of HSP60 and RanBP2 in SH-SY5Y cells grown on HA-modified surface with cotreatment of PC12 CM. Moreover, we also identified factors that were secreted from PC12 cells and may promote SH-SY5Y cell proliferation and adhesion. Here, we proposed a biomaterial surface enriched with neurotrophic factors for nerve regeneration application.

  5. Crowdsourcing biomedical research: leveraging communities as innovation engines.

    Science.gov (United States)

    Saez-Rodriguez, Julio; Costello, James C; Friend, Stephen H; Kellen, Michael R; Mangravite, Lara; Meyer, Pablo; Norman, Thea; Stolovitzky, Gustavo

    2016-07-15

    The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.

  6. Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

    Science.gov (United States)

    Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

    2013-03-01

    Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

  7. Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms

    International Nuclear Information System (INIS)

    Atashkari, K.; Nariman-Zadeh, N.; Goelcue, M.; Khalkhali, A.; Jamali, A.

    2007-01-01

    The main reason for the efficiency decrease at part load conditions for four-stroke spark-ignition (SI) engines is the flow restriction at the cross-sectional area of the intake system. Traditionally, valve-timing has been designed to optimize operation at high engine-speed and wide open throttle conditions. Several investigations have demonstrated that improvements at part load conditions in engine performance can be accomplished if the valve-timing is variable. Controlling valve-timing can be used to improve the torque and power curve as well as to reduce fuel consumption and emissions. In this paper, a group method of data handling (GMDH) type neural network and evolutionary algorithms (EAs) are firstly used for modelling the effects of intake valve-timing (V t ) and engine speed (N) of a spark-ignition engine on both developed engine torque (T) and fuel consumption (Fc) using some experimentally obtained training and test data. Using such obtained polynomial neural network models, a multi-objective EA (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto based optimization of the variable valve-timing engine considering two conflicting objectives such as torque (T) and fuel consumption (Fc). The comparison results demonstrate the superiority of the GMDH type models over feedforward neural network models in terms of the statistical measures in the training data, testing data and the number of hidden neurons. Further, it is shown that some interesting and important relationships, as useful optimal design principles, involved in the performance of the variable valve-timing four-stroke spark-ignition engine can be discovered by the Pareto based multi-objective optimization of the polynomial models. Such important optimal principles would not have been obtained without the use of both the GMDH type neural network modelling and the multi-objective Pareto optimization approach

  8. Building a Community Memory in Communities of Practice of E-Learning: A Knowledge Engineering Approach

    Science.gov (United States)

    Sarirete, Akila; Chikh, Azeddine; Noble, Elizabeth

    2011-01-01

    Purpose: The purpose of this paper is to define a community memory for a virtual communities of practice (CoP) based on organizational learning (OL) concept and ontologies. Design/methodology/approach: The paper focuses on applying the OL concept to virtual CoP and proposes a framework for building the CoP memory by identifying several layers of…

  9. Delivery of Brain-Derived Neurotrophic Factor by 3D Biocompatible Polymeric Scaffolds for Neural Tissue Engineering and Neuronal Regeneration

    KAUST Repository

    Limongi, Tania; Rocchi, A.; Cesca, F.; Tan, H.; Miele, E.; Giugni, Andrea; Orlando, M.; Perrone Donnorso, M.; Perozziello, G.; Benfenati, Fabio; Di Fabrizio, Enzo M.

    2018-01-01

    Biopolymers are increasingly employed for neuroscience applications as scaffolds to drive and promote neural regrowth, thanks to their ability to mediate the upload and subsequent release of active molecules and drugs. Synthetic degradable polymers are characterized by different responses ranging from tunable distension or shrinkage to total dissolution, depending on the function they are designed for. In this paper we present a biocompatible microfabricated poly-ε-caprolactone (PCL) scaffold for primary neuron growth and maturation that has been optimized for the in vitro controlled release of brain-derived neurotrophic factor (BDNF). We demonstrate that the designed morphology confers to these devices an enhanced drug delivery capability with respect to monolithic unstructured supports. After incubation with BDNF, micropillared PCL devices progressively release the neurotrophin over 21 days in vitro. Moreover, the bioactivity of released BDNF is confirmed using primary neuronal cultures, where it mediates a consistent activation of BDNF signaling cascades, increased synaptic density, and neuronal survival. These results provide the proof-of-principle on the fabrication process of micropatterned PCL devices, which represent a promising therapeutic option to enhance neuronal regeneration after lesion and for neural tissue engineering and prosthetics.

  10. Delivery of Brain-Derived Neurotrophic Factor by 3D Biocompatible Polymeric Scaffolds for Neural Tissue Engineering and Neuronal Regeneration

    KAUST Repository

    Limongi, Tania

    2018-04-04

    Biopolymers are increasingly employed for neuroscience applications as scaffolds to drive and promote neural regrowth, thanks to their ability to mediate the upload and subsequent release of active molecules and drugs. Synthetic degradable polymers are characterized by different responses ranging from tunable distension or shrinkage to total dissolution, depending on the function they are designed for. In this paper we present a biocompatible microfabricated poly-ε-caprolactone (PCL) scaffold for primary neuron growth and maturation that has been optimized for the in vitro controlled release of brain-derived neurotrophic factor (BDNF). We demonstrate that the designed morphology confers to these devices an enhanced drug delivery capability with respect to monolithic unstructured supports. After incubation with BDNF, micropillared PCL devices progressively release the neurotrophin over 21 days in vitro. Moreover, the bioactivity of released BDNF is confirmed using primary neuronal cultures, where it mediates a consistent activation of BDNF signaling cascades, increased synaptic density, and neuronal survival. These results provide the proof-of-principle on the fabrication process of micropatterned PCL devices, which represent a promising therapeutic option to enhance neuronal regeneration after lesion and for neural tissue engineering and prosthetics.

  11. Neural stem cells encapsulated in a functionalized self-assembling peptide hydrogel for brain tissue engineering.

    Science.gov (United States)

    Cheng, Tzu-Yun; Chen, Ming-Hong; Chang, Wen-Han; Huang, Ming-Yuan; Wang, Tzu-Wei

    2013-03-01

    Brain injury is almost irreparable due to the poor regenerative capability of neural tissue. Nowadays, new therapeutic strategies have been focused on stem cell therapy and supplying an appropriate three dimensional (3D) matrix for the repair of injured brain tissue. In this study, we specifically linked laminin-derived IKVAV motif on the C-terminal to enrich self-assembling peptide RADA(16) as a functional peptide-based scaffold. Our purpose is providing a functional self-assembling peptide 3D hydrogel with encapsulated neural stem cells to enhance the reconstruction of the injured brain. The physiochemical properties reported that RADA(16)-IKVAV can self-assemble into nanofibrous morphology with bilayer β-sheet structure and become gelationed hydrogel with mechanical stiffness similar to brain tissue. The in vitro results showed that the extended IKVAV sequence can serve as a signal or guiding cue to direct the encapsulated neural stem cells (NSCs) adhesion and then towards neuronal differentiation. Animal study was conducted in a rat brain surgery model to demonstrate the damage in cerebral neocortex/neopallium loss. The results showed that the injected peptide solution immediately in situ formed the 3D hydrogel filling up the cavity and bridging the gaps. The histological analyses revealed the RADA(16)-IKVAV self-assembling peptide hydrogel not only enhanced survival of encapsulated NSCs but also reduced the formation of glial astrocytes. The peptide hydrogel with IKVAV extended motifs also showed the support of encapsulated NSCs in neuronal differentiation and the improvement in brain tissue regeneration after 6 weeks post-transplantation. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. Contamination control engineering design guidelines for the aerospace community

    Science.gov (United States)

    Tribble, A. C. (Principal Investigator); Boyadjian, B.; Davis, J.; Haffner, J.; McCullough, E.

    1996-01-01

    Thermal control surfaces, solar arrays, and optical devices may be adversely affected by a small quantity of molecular and/or particulate contamination. What is rarely discussed is how one: (1) quantifies the level of contamination that must be maintained in order for the system to function properly, and (2) enforces contamination control to ensure compliance with requirements. This document is designed to address these specific issues and is intended to serve as a handbook on contamination control for the reader, illustrating process and methodology while providing direction to more detailed references when needed. The effects of molecular contamination on reflecting and transmitting surfaces are examined and quantified in accordance with MIL STD 1246C. The generation, transportation, and deposition of molecular contamination is reviewed and specific examples are worked to illustrate the process a design engineer can use to estimate end of life cleanliness levels required by solar arrays, thermal control surfaces, and optical surfaces. A similar process is used to describe the effect of particulate contamination as related to percent area coverage (PAC) and bi-directional reflectance distribution function (BRDF). Relationships between PAC and surface cleanliness, which include the effects of submicron sized particles, are developed and BRDF is related to specific sensor design parameters such as Point Source Transmittance (PST). The pros and cons of various methods of preventing, monitoring, and cleaning surfaces are examined and discussed.

  13. BrainCrafter: An investigation into human-based neural network engineering

    DEFF Research Database (Denmark)

    Piskur, J.; Greve, P.; Togelius, J.

    2015-01-01

    This paper presents the online application Brain-Crafter, in which users can manually build artificial neural networks (ANNs) to control a robot in a maze environment. Users can either start to construct networks from scratch or elaborate on networks created by other users. In particular, Brain......Crafter was designed to study how good we as humans are at building ANNs for control problems and if collaborating with other users can facilitate this process. The results in this paper show that (1) some users were in fact able to successfully construct ANNs that solve the navigation tasks, (2) collaboration between...

  14. Technology Education; Engineering Technology and Industrial Technology in California Community Colleges: A Curriculum Guide.

    Science.gov (United States)

    Schon, James F.

    In order to identify the distinguishing characteristics of technical education programs in engineering and industrial technology currently offered by post-secondary institutions in California, a body of data was collected by visiting 25 community colleges, 5 state universities, and 8 industrial firms; by a questionnaire sampling of 72 California…

  15. Navigating Community College Transfer in Science, Technical, Engineering, and Mathematics Fields

    Science.gov (United States)

    Packard, Becky Wai-Ling; Gagnon, Janelle L.; Senas, Arleen J.

    2012-01-01

    Given financial barriers facing community college students today, and workforce projections in science, technical, engineering, and math (STEM) fields, the costs of unnecessary delays while navigating transfer pathways are high. In this phenomenological study, we analyzed the delay experiences of 172 students (65% female) navigating community…

  16. Connecting Urban Students with Engineering Design: Community-Focused, Student-Driven Projects

    Science.gov (United States)

    Parker, Carolyn; Kruchten, Catherine; Moshfeghian, Audrey

    2017-01-01

    The STEM Achievement in Baltimore Elementary Schools (SABES) program is a community partnership initiative that includes both in-school and afterschool STEM education for grades 3-5. It was designed to broaden participation and achievement in STEM education by bringing science and engineering to the lives of low-income urban elementary school…

  17. The Software Engineering Community at DLR: How we got where we are

    OpenAIRE

    Haupt, Carina; Schlauch, Tobias

    2017-01-01

    Sustainable software and reproducible results become vital in research. Awareness for the topic as well as the required knowledge cannot be taken for granted. We show, how scientists at DLR are encouraged to form a self-reliant software engineering community and how we supported this by providing information resources and opportunities for collaboration and exchange.

  18. Long Term Benefits for Women in a Science, Technology, Engineering, and Mathematics Living-Learning Community

    Science.gov (United States)

    Maltby, Jennifer L.; Brooks, Christopher; Horton, Marjorie; Morgan, Helen

    2016-01-01

    Science, technology, engineering and math (STEM) degrees provide opportunities for economic mobility. Yet women, underrepresented minority (URM), and first-generation college students remain disproportionately underrepresented in STEM fields. This study examined the effectiveness of a living-learning community (LLC) for URM and first-generation…

  19. Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

    Science.gov (United States)

    Hu, Miao; Graves, Catherine E; Li, Can; Li, Yunning; Ge, Ning; Montgomery, Eric; Davila, Noraica; Jiang, Hao; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei; Strachan, John Paul

    2018-03-01

    Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single-layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Genetically engineered bacteriophage delivers a tumor necrosis factor alpha antagonist coating on neural electrodes

    International Nuclear Information System (INIS)

    Kim, Young Jun; Nam, Chang-Hoon; Jin, Young-Hyun; Stieglitz, Thomas; Salieb-Beugelaar, Georgette B

    2014-01-01

    This paper reports a novel approach for the formation of anti-inflammatory surface coating on a neural electrode. The surface coating is realized using a recombinant f88 filamentous bacteriophage, which displays a short platinum binding motif and a tumor necrosis factor alpha antagonist (TNF-α antagonist) on p3 and p8 proteins, respectively. The recombinant bacteriophages are immobilized on the platinum surface by a simple dip coating process. The selective and stable immobilization of bacteriophages on a platinum electrode is confirmed by quartz crystal microbalance with dissipation monitoring, atomic force microscope and fluorescence microscope. From the in vitro cell viability test, the inflammatory cytokine (TNF-α) induced cell death was prevented by presenting recombinant bacteriophage coating, albeit with no significant cytotoxic effect. It is also observed that the bacteriophage coating does not have critical effects on the electrochemical properties such as impedance and charge storage capacities. Thus, this approach demonstrates a promising anti-apoptotic as well as anti-inflammatory surface coating for neural implant applications. (paper)

  1. Restoring rocky intertidal communities: Lessons from a benthic macroalgal ecosystem engineer

    International Nuclear Information System (INIS)

    Bellgrove, Alecia; McKenzie, Prudence F.; Cameron, Hayley; Pocklington, Jacqueline B.

    2017-01-01

    As coastal population growth increases globally, effective waste management practices are required to protect biodiversity. Water authorities are under increasing pressure to reduce the impact of sewage effluent discharged into the coastal environment and restore disturbed ecosystems. We review the role of benthic macroalgae as ecosystem engineers and focus particularly on the temperate Australasian fucoid Hormosira banksii as a case study for rocky intertidal restoration efforts. Research focussing on the roles of ecosystem engineers is lagging behind restoration research of ecosystem engineers. As such, management decisions are being made without a sound understanding of the ecology of ecosystem engineers. For successful restoration of rocky intertidal shores it is important that we assess the thresholds of engineering traits (discussed herein) and the environmental conditions under which they are important. - Highlights: • Fucoid algae can be important ecosystem engineers in rocky reef ecosystems • Sewage-effluent disposal negatively affects fucoids and associated communities • Restoring fucoid populations can improve biodiversity of degraded systems • Clarifying the roles of fucoids in ecosystem function can improve restoration efforts • Thresholds of engineering traits and associated environmental conditions important

  2. 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...

  3. Presentation of Knovel - technical information portal for the engineering community | 15 February

    CERN Multimedia

    CERN Library

    2013-01-01

    The Library invites you to a presentation of Knovel, given by Gary Kearns, Knovel Managing Director - EMEA.   Friday 15 February 2013 from 11:00 to 12:30 room 30-7-018 (Kjell Johnsen Auditorium) Knovel is a web-based discovery platform meeting the information needs of the engineering community. It combines the functionalities of an ebooks platform and of a search engine querying a plurality of online databases. These functionalities are complemented by analytical tools that permit to extract and manipulate data from ebooks content. The agenda of the presentation is available here.

  4. Cell surface glycan engineering of neural stem cells augments neurotropism and improves recovery in a murine model of multiple sclerosis

    KAUST Repository

    Merzaban, Jasmeen; Imitola, Jaime; Starossom, Sarah C.; Zhu, Bing; Wang, Yue; Lee, Jack; Ali, Amal J.; Olah, Marta; AbuElela, Ayman; Khoury, Samia J.; Sackstein, Robert

    2015-01-01

    Neural stem cell (NSC)-based therapies offer potential for neural repair in central nervous system (CNS) inflammatory and degenerative disorders. Typically, these conditions present with multifocal CNS lesions making it impractical to inject NSCs

  5. Expandable and Rapidly Differentiating Human Induced Neural Stem Cell Lines for Multiple Tissue Engineering Applications

    Directory of Open Access Journals (Sweden)

    Dana M. Cairns

    2016-09-01

    Full Text Available Limited availability of human neurons poses a significant barrier to progress in biological and preclinical studies of the human nervous system. Current stem cell-based approaches of neuron generation are still hindered by prolonged culture requirements, protocol complexity, and variability in neuronal differentiation. Here we establish stable human induced neural stem cell (hiNSC lines through the direct reprogramming of neonatal fibroblasts and adult adipose-derived stem cells. These hiNSCs can be passaged indefinitely and cryopreserved as colonies. Independently of media composition, hiNSCs robustly differentiate into TUJ1-positive neurons within 4 days, making them ideal for innervated co-cultures. In vivo, hiNSCs migrate, engraft, and contribute to both central and peripheral nervous systems. Lastly, we demonstrate utility of hiNSCs in a 3D human brain model. This method provides a valuable interdisciplinary tool that could be used to develop drug screening applications as well as patient-specific disease models related to disorders of innervation and the brain.

  6. Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

    International Nuclear Information System (INIS)

    Li, Chun-Hsien; Yang, Suh-Yuh

    2014-01-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  7. Building community partnerships to implement the new Science and Engineering component of the NGSS

    Science.gov (United States)

    Burke, M. P.; Linn, F.

    2013-12-01

    Partnerships between science professionals in the community and professional educators can help facilitate the adoption of the Next Generation Science Standards (NGSS). Classroom teachers have been trained in content areas but may be less familiar with the new required Science and Engineering component of the NGSS. This presentation will offer a successful model for building classroom and community partnerships and highlight the particulars of a collaborative lesson taught to Rapid City High School students. Local environmental issues provided a framework for learning activities that encompassed several Crosscutting Concepts and Science and Engineering Practices for a lesson focused on Life Science Ecosystems: Interactions, Energy, and Dynamics. Specifically, students studied local water quality impairments, collected and measured stream samples, and analyzed their data. A visiting hydrologist supplied additional water quality data from ongoing studies to extend the students' datasets both temporally and spatially, helping students to identify patterns and draw conclusions based on their findings. Context was provided through discussions of how science professionals collect and analyze data and communicate results to the public, using an example of a recent bacterial contamination of a local stream. Working with Rapid City High School students added additional challenges due to their high truancy and poverty rates. Creating a relevant classroom experience was especially critical for engaging these at-risk youth and demonstrating that science is a viable career path for them. Connecting science in the community with the problem-solving nature of engineering is a critical component of NGSS, and this presentation will elucidate strategies to help prospective partners maneuver through the challenges that we've encountered. We recognize that the successful implementation of the NGSS is a challenge that requires the support of the scientific community. This partnership

  8. Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

    Science.gov (United States)

    Mason, Cindi; Twomey, Janet; Wright, David; Whitman, Lawrence

    2018-01-01

    As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm…

  9. Persistence of community college engineering science students: The impact of selected cognitive and noncognitive characteristics

    Science.gov (United States)

    Chatman, Lawrence M., Jr.

    If the United States is to remain technologically competitive, persistence in engineering programs must improve. This study on student persistence employed a mixed-method design to identify the cognitive and noncognitive factors which contribute to students remaining in an engineering science curriculum or switching from an engineering curriculum at a community college in the northeast United States. Records from 372 students were evaluated to determine the characteristics of two groups: those students that persisted with the engineering curriculum and those that switched from engineering; also, the dropout phenomenon was evaluated. The quantitative portion of the study used a logistic regression analyses on 22 independent variables, while the qualitative portion of the study used group interviews to investigate the noncognitive factors that influenced persisting or switching. The qualitative portion of the study added depth and credibility to the results from the quantitative portion. The study revealed that (1) high grades in first year calculus, physics and chemistry courses, (2) fewer number of semesters enrolled, (3) attendance with full time status, and (4) not participating in an English as a Second Language (ESL) program were significant variables used to predict student persistence. The group interviews confirmed several of these contributing factors. Students that dropped out of college began with (1) the lowest levels of remediation, (2) the lowest grade point averages, and (3) the fewest credits earned.

  10. RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

    Directory of Open Access Journals (Sweden)

    Marco Grimaldi

    Full Text Available RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.

  11. Iowa community college Science, Engineering and Mathematics (SEM) faculty: Demographics and job satisfaction

    Science.gov (United States)

    Rogotzke, Kathy

    Community college faculty members play an increasingly important role in the educational system in the United States. However, over the past decade, concerns have arisen, especially in several high demand fields of science, technology, engineering and mathematics (STEM), that a shortage of qualified faculty in these fields exists. Furthermore, the average age of community college faculty is increasing, which creates added concern of an increased shortage of qualified faculty due to a potentially large number of faculty retiring. To help further understand the current population of community college faculty, as well as their training needs and their satisfaction with their jobs, data needs to be collected from them and examined. Currently, several national surveys are given to faculty at institutions of higher education, most notably the Higher Education Research Institute Faculty Survey, the National Study of Postsecondary Faculty, and the Community College Faculty Survey of Student Engagement. Of these surveys the Community College Faculty Survey of Student Engagement is the only survey focused solely on community college faculty. This creates a problem because community college faculty members differ from faculty at 4-year institutions in several significant ways. First, qualifications for hiring community college faculty are different at 4-year colleges or universities. Whereas universities and colleges typically require their faculty to have a Ph.D., community colleges require their arts and science faculty to have a only master's degree and their career faculty to have experience and the appropriate training and certification in their field with only a bachelor's degree. The work duties and expectations for community college faculty are also different at 4-year colleges or universities. Community college faculty typically teach 14 to 19 credit hours a semester and do little, if any research, whereas faculty at 4-year colleges typically teach 9 to 12 credit

  12. The community FabLab platform: applications and implications in biomedical engineering.

    Science.gov (United States)

    Stephenson, Makeda K; Dow, Douglas E

    2014-01-01

    Skill development in science, technology, engineering and math (STEM) education present one of the most formidable challenges of modern society. The Community FabLab platform presents a viable solution. Each FabLab contains a suite of modern computer numerical control (CNC) equipment, electronics and computing hardware and design, programming, computer aided design (CAD) and computer aided machining (CAM) software. FabLabs are community and educational resources and open to the public. Development of STEM based workforce skills such as digital fabrication and advanced manufacturing can be enhanced using this platform. Particularly notable is the potential of the FabLab platform in STEM education. The active learning environment engages and supports a diversity of learners, while the iterative learning that is supported by the FabLab rapid prototyping platform facilitates depth of understanding, creativity, innovation and mastery. The product and project based learning that occurs in FabLabs develops in the student a personal sense of accomplishment, self-awareness, command of the material and technology. This helps build the interest and confidence necessary to excel in STEM and throughout life. Finally the introduction and use of relevant technologies at every stage of the education process ensures technical familiarity and a broad knowledge base needed for work in STEM based fields. Biomedical engineering education strives to cultivate broad technical adeptness, creativity, interdisciplinary thought, and an ability to form deep conceptual understanding of complex systems. The FabLab platform is well designed to enhance biomedical engineering education.

  13. Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?

    Science.gov (United States)

    Kabeshova, Anastasiia; Launay, Cyrille P; Gromov, Vasilii A; Annweiler, Cédric; Fantino, Bruno; Beauchet, Olivier

    2015-04-01

    Identification of the risk of recurrent falls is complex in older adults. The aim of this study was to examine the efficiency of 3 artificial neural networks (ANNs: multilayer perceptron [MLP], modified MLP, and neuroevolution of augmenting topologies [NEAT]) for the classification of recurrent fallers and nonrecurrent fallers using a set of clinical characteristics corresponding to risk factors of falls measured among community-dwelling older adults. Based on a cross-sectional design, 3289 community-dwelling volunteers aged 65 and older were recruited. Age, gender, body mass index (BMI), number of drugs daily taken, use of psychoactive drugs, diphosphonate, calcium, vitamin D supplements and walking aid, fear of falling, distance vision score, Timed Up and Go (TUG) score, lower-limb proprioception, handgrip strength, depressive symptoms, cognitive disorders, and history of falls were recorded. Participants were separated into 2 groups based on the number of falls that occurred over the past year: 0 or 1 fall and 2 or more falls. In addition, total population was separated into training and testing subgroups for ANN analysis. Among 3289 participants, 18.9% (n = 622) were recurrent fallers. NEAT, using 15 clinical characteristics (ie, use of walking aid, fear of falling, use of calcium, depression, use of vitamin D supplements, female, cognitive disorders, BMI 4, vision score 9 seconds, handgrip strength score ≤29 (N), and age ≥75 years), showed the best efficiency for identification of recurrent fallers, sensitivity (80.42%), specificity (92.54%), positive predictive value (84.38), negative predictive value (90.34), accuracy (88.39), and Cohen κ (0.74), compared with MLP and modified MLP. NEAT, using a set of 15 clinical characteristics, was an efficient ANN for the identification of recurrent fallers in older community-dwellers. Copyright © 2015 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

  14. Applications of neural networks in environmental and energy sciences and engineering. Proceedings of the 1995 workshop on environmental and energy applications of neural networks

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-12-31

    These proceedings contain edited versions of the technical presentations of the Workshop on Environmental and Energy Applications of Neural Networks, held on March 30--31, 1995, in Richland, Washington. The purpose of the workshop was to provide a forum for discussing environmental, energy, and biomedical applications of neural networks. Panels were held to discuss various research and development issues relating to real-world applications in each of the three areas. The applications covered in the workshop were: Environmental applications -- modeling and predicting soil, air and water pollution, environmental sensing, spectroscopy, hazardous waste handling and cleanup; Energy applications -- process monitoring and optimization of power systems, modeling and control of power plants, environmental monitoring for power systems, power load forecasting, fault location and diagnosis of power systems; and Biomedical applications -- medical image and signal analysis, medical diagnosis, analysis of environmental health effects, and modeling biological systems. Selected papers are indexed separately for inclusion in the Energy Science and Technology Database.

  15. Microorganisms in heavy metal bioremediation: strategies for applying microbial-community engineering to remediate soils

    Directory of Open Access Journals (Sweden)

    Jennifer L. Wood

    2016-06-01

    Full Text Available The remediation of heavy-metal-contaminated soils is essential as heavy metals persist and do not degrade in the environment. Remediating heavy-metal-contaminated soils requires metals to be mobilized for extraction whilst, at the same time, employing strategies to avoid mobilized metals leaching into ground-water or aquatic systems. Phytoextraction is a bioremediation strategy that extracts heavy metals from soils by sequestration in plant tissues and is currently the predominant bioremediation strategy investigated for remediating heavy-metal-contaminated soils. Although the efficiency of phytoextraction remains a limiting feature of the technology, there are numerous reports that soil microorganisms can improve rates of heavy metal extraction.This review highlights the unique challenges faced when remediating heavy-metal-contaminated soils as compared to static aquatic systems and suggests new strategies for using microorganisms to improve phytoextraction. We compare how microorganisms are used in soil bioremediation (i.e. phytoextraction and water bioremediation processes, discussing how the engineering of microbial communities, used in water remediation, could be applied to phytoextraction. We briefly outline possible approaches for the engineering of soil communities to improve phytoextraction either by mobilizing metals in the rhizosphere of the plant or by promoting plant growth to increase the root-surface area available for uptake of heavy metals. We highlight the technological advances that make this research direction possible and how these technologies could be employed in future research.

  16. Integrated Community Energy Systems: engineering analysis and design bibliography. [368 citations

    Energy Technology Data Exchange (ETDEWEB)

    Calm, J.M.; Sapienza, G.R.

    1979-05-01

    This bibliography cites 368 documents that may be helpful in the planning, analysis, and design of Integrated Community Energy Systems. It has been prepared for use primarily by engineers and others involved in the development and implementation of ICES concepts. These documents include products of a number of Government research, development, demonstration, and commercialization programs; selected studies and references from the literature of various technical societies and institutions; and other selected material. The key programs which have produced cited reports are the Department of Energy Community Systems Program (DOE/CSP), the Department of Housing and Urban Development Modular Integrated Utility Systems Program (HUD/MIUS), and the Department of Health, Education, and Welfare Integrated Utility Systems Program (HEW/IUS). The cited documents address experience gained both in the U.S. and in other countries. Several general engineering references and bibliographies pertaining to technologies or analytical methods that may be helpful in the analysis and design of ICES are also included. The body of relevant literature is rapidly growing and future updates are therefore planned. Each citation includes identifying information, a source, descriptive information, and an abstract. The citations are indexed both by subjects and authors, and the subject index is extensively cross-referenced to simplify its use.

  17. Ecosystem engineers on plants: indirect facilitation of arthropod communities by leaf-rollers at different scales.

    Science.gov (United States)

    Vieira, Camila; Romero, Gustavo Q

    2013-07-01

    Ecosystem engineering is a process by which organisms change the distribution of resources and create new habitats for other species via non-trophic interactions. Leaf-rolling caterpillars can act as ecosystem engineers because they provide shelter to secondary users. In this study, we report the influence of leaf-rolling caterpillars on speciose tropical arthropod communities along both spatial scales (leaf-level and plant-level effects) and temporal scales (dry and rainy seasons). We predict that rolled leaves can amplify arthropod diversity at both the leaf and plant levels and that this effect is stronger in dry seasons, when arthropods are prone to desiccation. Our results show that the abundance, richness, and biomass of arthropods within several guilds increased up to 22-fold in naturally and artificially created leaf shelters relative to unaltered leaves. These effects were observed at similar magnitudes at both the leaf and plant scales. Variation in the shelter architecture (funnel, cylinders) did not influence arthropod parameters, as diversity, abundance, orbiomass, but rolled leaves had distinct species composition if compared with unaltered leaves. As expected, these arthropod parameters on the plants with rolled leaves were on average approximately twofold higher in the dry season. Empty leaf rolls and whole plants were rapidly recolonized by arthropods over time, implying a fast replacement of individuals; within 15-day intervals the rolls and plants reached a species saturation. This study is the first to examine the extended effects of engineering caterpillars as diversity amplifiers at different temporal and spatial scales. Because shelter-building caterpillars are ubiquitous organisms in tropical and temperate forests, they can be considered key structuring elements for arthropod communities on plants.

  18. Mammalian engineers drive soil microbial communities and ecosystem functions across a disturbance gradient.

    Science.gov (United States)

    Eldridge, David J; Delgado-Baquerizo, Manuel; Woodhouse, Jason N; Neilan, Brett A

    2016-11-01

    The effects of mammalian ecosystem engineers on soil microbial communities and ecosystem functions in terrestrial ecosystems are poorly known. Disturbance from livestock has been widely reported to reduce soil function, but disturbance by animals that forage in the soil may partially offset these negative effects of livestock, directly and/or indirectly by shifting the composition and diversity of soil microbial communities. Understanding the role of disturbance from livestock and ecosystem engineers in driving soil microbes and functions is essential for formulating sustainable ecosystem management and conservation policies. We compared soil bacterial community composition and enzyme concentrations within four microsites: foraging pits of two vertebrates, the indigenous short-beaked echidna (Tachyglossus aculeatus) and the exotic European rabbit (Oryctolagus cuniculus), and surface and subsurface soils along a gradient in grazing-induced disturbance in an arid woodland. Microbial community composition varied little across the disturbance gradient, but there were substantial differences among the four microsites. Echidna pits supported a lower relative abundance of Acidobacteria and Cyanobacteria, but a higher relative abundance of Proteobacteria than rabbit pits and surface microsites. Moreover, these microsite differences varied with disturbance. Rabbit pits had a similar profile to the subsoil or the surface soils under moderate and high, but not low disturbance. Overall, echidna foraging pits had the greatest positive effect on function, assessed as mean enzyme concentrations, but rabbits had the least. The positive effects of echidna foraging on function were indirectly driven via microbial community composition. In particular, increasing activity was positively associated with increasing relative abundance of Proteobacteria, but decreasing Acidobacteria. Our study suggests that soil disturbance by animals may offset, to some degree, the oft-reported negative

  19. Neural ensemble communities: Open-source approaches to hardware for large-scale electrophysiology

    Science.gov (United States)

    Siegle, Joshua H.; Hale, Gregory J.; Newman, Jonathan P.; Voigts, Jakob

    2014-01-01

    One often-overlooked factor when selecting a platform for large-scale electrophysiology is whether or not a particular data acquisition system is “open” or “closed”: that is, whether or not the system’s schematics and source code are available to end users. Open systems have a reputation for being difficult to acquire, poorly documented, and hard to maintain. With the arrival of more powerful and compact integrated circuits, rapid prototyping services, and web-based tools for collaborative development, these stereotypes must be reconsidered. We discuss some of the reasons why multichannel extracellular electrophysiology could benefit from open-source approaches and describe examples of successful community-driven tool development within this field. In order to promote the adoption of open-source hardware and to reduce the need for redundant development efforts, we advocate a move toward standardized interfaces that connect each element of the data processing pipeline. This will give researchers the flexibility to modify their tools when necessary, while allowing them to continue to benefit from the high-quality products and expertise provided by commercial vendors. PMID:25528614

  20. Influence of Precollege Experience on Self-Concept among Community College Students in Science, Mathematics, and Engineering

    Science.gov (United States)

    Starobin, Soko S.; Laanan, Frankie Santos

    Female and minority students have historically been underrepresented in the field of science, mathematics, and engineering at colleges and universities. Although a plethora of research has focused on students enrolled in 4-year colleges or universities, limited research addresses the factors that influence gender differences in community college students in science, mathematics, and engineering. Using a target population of 1,599 aspirants in science, mathematics, and engineering majors in public community colleges, this study investigates the determinants of self-concept by examining a hypothetical structural model. The findings suggest that background characteristics, high school academic performance, and attitude toward science have unique contributions to the development of self-concept among female community college students. The results add to the literature by providing new theoretical constructs and the variables that predict students' self-concept.

  1. Effect of engineered environment on microbial community structure in biofilter and biofilm on reverse osmosis membrane.

    Science.gov (United States)

    Jeong, Sanghyun; Cho, Kyungjin; Jeong, Dawoon; Lee, Seockheon; Leiknes, TorOve; Vigneswaran, Saravanamuthu; Bae, Hyokwan

    2017-11-01

    Four dual media filters (DMFs) were operated in a biofiltration mode with different engineered environments (DMF I and II: coagulation with/without acidification and DMF III and IV: without/with chlorination). Designed biofilm enrichment reactors (BERs) containing the removable reverse osmosis (RO) coupons, were connected at the end of the DMFs in parallel to analyze the biofilm on the RO membrane by DMF effluents. Filtration performances were evaluated in terms of dissolved organic carbon (DOC) and assimilable organic carbon (AOC). Organic foulants on the RO membrane were also quantified and fractionized. The bacterial community structures in liquid (seawater and effluent) and biofilm (DMF and RO) samples were analyzed using 454-pyrosequencing. The DMF IV fed with the chlorinated seawater demonstrated the highest reductions of DOC including LMW-N as well as AOC among the other DMFs. The DMF IV was also effective in reducing organic foulants on the RO membrane surface. The bacterial community structure was grouped according to the sample phase (i.e., liquid and biofilm samples), sampling location (i.e., DMF and RO samples), and chlorination (chlorinated and non-chlorinated samples). In particular, the biofilm community in the DMF IV differed from the other DMF treatments, suggesting that chlorination exerted as stronger selective pressure than pH adjustment or coagulation on the biofilm community. In the DMF IV, several chemoorganotrophic chlorine-resistant biofilm-forming bacteria such as Hyphomonas, Erythrobacter, and Sphingomonas were predominant, and they may enhance organic carbon degradation efficiency. Diverse halophilic or halotolerant organic degraders were also found in other DMFs (i.e., DMF I, II, and III). Various kinds of dominant biofilm-forming bacteria were also investigated in RO membrane samples; the results provided possible candidates that cause biofouling when DMF process is applied as the pretreatment option for the RO process. Copyright

  2. Effect of engineered environment on microbial community structure in biofilter and biofilm on reverse osmosis membrane

    KAUST Repository

    Jeong, Sanghyun

    2017-07-25

    Four dual media filters (DMFs) were operated in a biofiltration mode with different engineered environments (DMF I and II: coagulation with/without acidification and DMF III and IV: without/with chlorination). Designed biofilm enrichment reactors (BERs) containing the removable reverse osmosis (RO) coupons, were connected at the end of the DMFs in parallel to analyze the biofilm on the RO membrane by DMF effluents. Filtration performances were evaluated in terms of dissolved organic carbon (DOC) and assimilable organic carbon (AOC). Organic foulants on the RO membrane were also quantified and fractionized. The bacterial community structures in liquid (seawater and effluent) and biofilm (DMF and RO) samples were analyzed using 454-pyrosequencing. The DMF IV fed with the chlorinated seawater demonstrated the highest reductions of DOC including LMW-N as well as AOC among the other DMFs. The DMF IV was also effective in reducing organic foulants on the RO membrane surface. The bacterial community structure was grouped according to the sample phase (i.e., liquid and biofilm samples), sampling location (i.e., DMF and RO samples), and chlorination (chlorinated and non-chlorinated samples). In particular, the biofilm community in the DMF IV differed from the other DMF treatments, suggesting that chlorination exerted as stronger selective pressure than pH adjustment or coagulation on the biofilm community. In the DMF IV, several chemoorganotrophic chlorine-resistant biofilm-forming bacteria such as Hyphomonas, Erythrobacter, and Sphingomonas were predominant, and they may enhance organic carbon degradation efficiency. Diverse halophilic or halotolerant organic degraders were also found in other DMFs (i.e., DMF I, II, and III). Various kinds of dominant biofilm-forming bacteria were also investigated in RO membrane samples; the results provided possible candidates that cause biofouling when DMF process is applied as the pretreatment option for the RO process.

  3. Electrospun Collagen/Silk Tissue Engineering Scaffolds: Fiber Fabrication, Post-Treatment Optimization, and Application in Neural Differentiation of Stem Cells

    Science.gov (United States)

    Zhu, Bofan

    Biocompatible scaffolds mimicking the locally aligned fibrous structure of native extracellular matrix (ECM) are in high demand in tissue engineering. In this thesis research, unidirectionally aligned fibers were generated via a home-built electrospinning system. Collagen type I, as a major ECM component, was chosen in this study due to its support of cell proliferation and promotion of neuroectodermal commitment in stem cell differentiation. Synthetic dragline silk proteins, as biopolymers with remarkable tensile strength and superior elasticity, were also used as a model material. Good alignment, controllable fiber size and morphology, as well as a desirable deposition density of fibers were achieved via the optimization of solution and electrospinning parameters. The incorporation of silk proteins into collagen was found to significantly enhance mechanical properties and stability of electrospun fibers. Glutaraldehyde (GA) vapor post-treatment was demonstrated as a simple and effective way to tune the properties of collagen/silk fibers without changing their chemical composition. With 6-12 hours GA treatment, electrospun collagen/silk fibers were not only biocompatible, but could also effectively induce the polarization and neural commitment of stem cells, which were optimized on collagen rich fibers due to the unique combination of biochemical and biophysical cues imposed to cells. Taken together, electrospun collagen rich composite fibers are mechanically strong, stable and provide excellent cell adhesion. The unidirectionally aligned fibers can accelerate neural differentiation of stem cells, representing a promising therapy for neural tissue degenerative diseases and nerve injuries.

  4. News from the Library: Knovel, a technical information portal for the engineering community

    CERN Multimedia

    CERN Library

    2013-01-01

    Knovel is a web-based discovery platform meeting the information needs of the engineering community.   Knovel combines the functionalities of an e-book platform and of a search engine querying a plurality of online databases. These functionalities are complemented by analytical tools that permit the extraction and manipulation of data from e-book content. Knovel provides subscribers with access to more than 4,000 leading reference works and databases from more than 100 international publishers and professional societies (AIAA, AIChE, ASME and NACE, among others) through a single interface. Knovelʼs comprehensive collection of content, covering 31 subject areas, is continually updated as new titles become available to reflect the evolving needs of users. Knovelʼs tools - including its interactive tables and graphs - not only help users to find information hidden in complex graphs, equations and tables quickly, but also to analyse and manipulate data as easily as sorting a spread sheet. Us...

  5. Novel High-Viscosity Polyacrylamidated Chitosan for Neural Tissue Engineering: Fabrication of Anisotropic Neurodurable Scaffold via Molecular Disposition of Persulfate-Mediated Polymer Slicing and Complexation

    Directory of Open Access Journals (Sweden)

    Viness Pillay

    2012-10-01

    Full Text Available Macroporous polyacrylamide-grafted-chitosan scaffolds for neural tissue engineering were fabricated with varied synthetic and viscosity profiles. A novel approach and mechanism was utilized for polyacrylamide grafting onto chitosan using potassium persulfate (KPS mediated degradation of both polymers under a thermally controlled environment. Commercially available high molecular mass polyacrylamide was used instead of the acrylamide monomer for graft copolymerization. This grafting strategy yielded an enhanced grafting efficiency (GE = 92%, grafting ratio (GR = 263%, intrinsic viscosity (IV = 5.231 dL/g and viscometric average molecular mass (MW = 1.63 × 106 Da compared with known acrylamide that has a GE = 83%, GR = 178%, IV = 3.901 dL/g and MW = 1.22 × 106 Da. Image processing analysis of SEM images of the newly grafted neurodurable scaffold was undertaken based on the polymer-pore threshold. Attenuated Total Reflectance-FTIR spectral analyses in conjugation with DSC were used for the characterization and comparison of the newly grafted copolymers. Static Lattice Atomistic Simulations were employed to investigate and elucidate the copolymeric assembly and reaction mechanism by exploring the spatial disposition of chitosan and polyacrylamide with respect to the reactional profile of potassium persulfate. Interestingly, potassium persulfate, a peroxide, was found to play a dual role initially degrading the polymers—“polymer slicing”—thereby initiating the formation of free radicals and subsequently leading to synthesis of the high molecular mass polyacrylamide-grafted-chitosan (PAAm-g-CHT—“polymer complexation”. Furthermore, the applicability of the uniquely grafted scaffold for neural tissue engineering was evaluated via PC12 neuronal cell seeding. The novel PAAm-g-CHT exhibited superior neurocompatibility in terms of cell infiltration owing to the anisotropic porous architecture, high molecular mass mediated robustness

  6. Experimental and artificial neural network based prediction of performance and emission characteristics of DI diesel engine using Calophyllum inophyllum methyl ester at different nozzle opening pressure

    Science.gov (United States)

    Vairamuthu, G.; Thangagiri, B.; Sundarapandian, S.

    2018-01-01

    The present work investigates the effect of varying Nozzle Opening Pressures (NOP) from 220 bar to 250 bar on performance, emissions and combustion characteristics of Calophyllum inophyllum Methyl Ester (CIME) in a constant speed, Direct Injection (DI) diesel engine using Artificial Neural Network (ANN) approach. An ANN model has been developed to predict a correlation between specific fuel consumption (SFC), brake thermal efficiency (BTE), exhaust gas temperature (EGT), Unburnt hydrocarbon (UBHC), CO, CO2, NOx and smoke density using load, blend (B0 and B100) and NOP as input data. A standard Back-Propagation Algorithm (BPA) for the engine is used in this model. A Multi Layer Perceptron network (MLP) is used for nonlinear mapping between the input and the output parameters. An ANN model can predict the performance of diesel engine and the exhaust emissions with correlation coefficient (R2) in the range of 0.98-1. Mean Relative Errors (MRE) values are in the range of 0.46-5.8%, while the Mean Square Errors (MSE) are found to be very low. It is evident that the ANN models are reliable tools for the prediction of DI diesel engine performance and emissions. The test results show that the optimum NOP is 250 bar with B100.

  7. A Study To Determine the Job Satisfaction of the Engineering/Industrial Technology Faculty at Delgado Community College.

    Science.gov (United States)

    Satterlee, Brian

    A study assessed job satisfaction among Engineering/Industrial Technology faculty at Delgado Community College (New Orleans, Louisiana). A secondary purpose was to confirm Herzberg's Two-Factor Theory of Job Satisfaction (1966) that workers derived satisfaction from the work itself and that causes of dissatisfaction stemmed from conditions…

  8. Modelling the continuous cooling transformation diagram of engineering steels using neural networks. Part II. Microstructure and hardness

    Energy Technology Data Exchange (ETDEWEB)

    Wolk, P.J. van der; Wang, J. [Delft Univ. of Technology (Netherlands); Sietsma, J.; Zwaag, S. van der [Delft Univ. of Technology, Lab. for Materials Science (Netherlands)

    2002-12-01

    The neural network model of Van der Wolk et al. (2002) describes the effect of composition on the phase regions of the continuous cooling transformation (CCT) diagram, yet does not consider the fractions of microstructural components and the hardness data that are often quoted in CCT diagrams. In the present paper, the construction of two more neural network models, one for the fractions of ferrite, pearlite, bainite and martensite in the microstructure, and one for the hardness after cooling, using the data of 338 and 412 diagrams, respectively. The accuracy of each model was found to be similar to the expected experimental error; moreover, the models were found to be mutually consistent, although they have been constructed independently. Furthermore, the trends in these properties for alloying elements can be quantified with the models, and are largely in line with metallurgical expectations. (orig.)

  9. Municipal solid waste management: identification and analysis of engineering indexes representing demand and costs generated in virtuous Italian communities.

    Science.gov (United States)

    Gamberini, R; Del Buono, D; Lolli, F; Rimini, B

    2013-11-01

    The definition and utilisation of engineering indexes in the field of Municipal Solid Waste Management (MSWM) is an issue of interest for technicians and scientists, which is widely discussed in literature. Specifically, the availability of consolidated engineering indexes is useful when new waste collection services are designed, along with when their performance is evaluated after a warm-up period. However, most published works in the field of MSWM complete their study with an analysis of isolated case studies. Conversely, decision makers require tools for information collection and exchange in order to trace the trends of these engineering indexes in large experiments. In this paper, common engineering indexes are presented and their values analysed in virtuous Italian communities, with the aim of contributing to the creation of a useful database whose data could be used during experiments, by indicating examples of MSWM demand profiles and the costs required to manage them. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

    Directory of Open Access Journals (Sweden)

    A. Moosavian

    2013-01-01

    Full Text Available Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC engine based on power spectral density (PSD technique and two classifiers, namely, K-nearest neighbor (KNN and artificial neural network (ANN. Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

  11. An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters using artificial neural networks and genetic algorithms

    Directory of Open Access Journals (Sweden)

    G-A. Tselentis

    2010-12-01

    Full Text Available Complex application domains involve difficult pattern classification problems. This paper introduces a model of MMI attenuation and its dependence on engineering ground motion parameters based on artificial neural networks (ANNs and genetic algorithms (GAs. The ultimate goal of this investigation is to evaluate the target-region applicability of ground-motion attenuation relations developed for a host region based on training an ANN using the seismic patterns of the host region. This ANN learning is based on supervised learning using existing data from past earthquakes. The combination of these two learning procedures (that is, GA and ANN allows us to introduce a new method for pattern recognition in the context of seismological applications. The performance of this new GA-ANN regression method has been evaluated using a Greek seismological database with satisfactory results.

  12. Municipal solid waste management: Identification and analysis of engineering indexes representing demand and costs generated in virtuous Italian communities

    Energy Technology Data Exchange (ETDEWEB)

    Gamberini, R., E-mail: rita.gamberini@unimore.it; Del Buono, D.; Lolli, F.; Rimini, B.

    2013-11-15

    Highlights: • Collection and analysis of real life data in the field of Municipal Solid Waste (MSW) generation and costs for management. • Study of 92 virtuous Italian communities. • Elaboration of trends of engineering indexes useful during design and evaluation of MSWM systems. - Abstract: The definition and utilisation of engineering indexes in the field of Municipal Solid Waste Management (MSWM) is an issue of interest for technicians and scientists, which is widely discussed in literature. Specifically, the availability of consolidated engineering indexes is useful when new waste collection services are designed, along with when their performance is evaluated after a warm-up period. However, most published works in the field of MSWM complete their study with an analysis of isolated case studies. Conversely, decision makers require tools for information collection and exchange in order to trace the trends of these engineering indexes in large experiments. In this paper, common engineering indexes are presented and their values analysed in virtuous Italian communities, with the aim of contributing to the creation of a useful database whose data could be used during experiments, by indicating examples of MSWM demand profiles and the costs required to manage them.

  13. Women In Engineering Learning Community: What We Learned The First Year

    OpenAIRE

    LaBoone, Kimberly; Lazar, Maureen; Watford, Bevlee

    2007-01-01

    The College of Engineering at Virginia Tech reflects national trends with respect to women in engineering. With first year enrollments hovering around 17%, the retention through graduation of these women is critical to increasing the number of women in the engineering profession. When examining year to year retention rates, it is observed that the largest percentage of women drop out of engineering during or immediately following their first year. It is therefore believed that efforts to incr...

  14. Obesity-specific neural cost of maintaining gait performance under complex conditions in community-dwelling older adults.

    Science.gov (United States)

    Osofundiya, Olufunmilola; Benden, Mark E; Dowdy, Diane; Mehta, Ranjana K

    2016-06-01

    Recent evidence of obesity-related changes in the prefrontal cortex during cognitive and seated motor activities has surfaced; however, the impact of obesity on neural activity during ambulation remains unclear. The purpose of this study was to determine obesity-specific neural cost of simple and complex ambulation in older adults. Twenty non-obese and obese individuals, 65years and older, performed three tasks varying in the types of complexity of ambulation (simple walking, walking+cognitive dual-task, and precision walking). Maximum oxygenated hemoglobin, a measure of neural activity, was measured bilaterally using a portable functional near infrared spectroscopy system, and gait speed and performance on the complex tasks were also obtained. Complex ambulatory tasks were associated with ~2-3.5 times greater cerebral oxygenation levels and ~30-40% slower gait speeds when compared to the simple walking task. Additionally, obesity was associated with three times greater oxygenation levels, particularly during the precision gait task, despite obese adults demonstrating similar gait speeds and performances on the complex gait tasks as non-obese adults. Compared to existing studies that focus solely on biomechanical outcomes, the present study is one of the first to examine obesity-related differences in neural activity during ambulation in older adults. In order to maintain gait performance, obesity was associated with higher neural costs, and this was augmented during ambulatory tasks requiring greater precision control. These preliminary findings have clinical implications in identifying individuals who are at greater risk of mobility limitations, particularly when performing complex ambulatory tasks. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. First Microbial Community Assessment of Borehole Fluids from the Deep Underground Science and Engineering Laboratory (DUSEL)

    Science.gov (United States)

    Moser, D. P.; Anderson, C.; Bang, S.; Jones, T. L.; Boutt, D.; Kieft, T.; Sherwood Lollar, B.; Murdoch, L. C.; Pfiffner, S. M.; Bruckner, J.; Fisher, J. C.; Newburn, J.; Wheatley, A.; Onstott, T. C.

    2010-12-01

    Fluid and gas samples were collected from two flowing boreholes at the 4100 (1,250 m) and 4850 ft (1478 m) levels of the former Homestake Gold Mine in Lead, South Dakota. Service- and flood water samples were also collected as comparative benchmarks. With a maximum depth of 8,000 ft, (2,438 m), this mine currently hosts the Sanford Laboratory and is the proposed location for the US Deep Underground Science and Engineering Laboratory (DUSEL). The uncased 4100L hole is a legacy of mining; whereas, the cased 4850 hole was drilled in 2009 in support of large cavity construction. Both were packered or valved to exclude mine air and sampled anaerobically using aseptic technique. Physical measurements, aquatic and dissolved gas chemistry, cell counts, and microbial community assessments (SSU rRNA libraries) were performed on all samples. This study represents the first at Sanford Lab/DUSEL specifically focused on the deep biosphere rather than mine microbiology. Fluids from the two holes differed markedly, with that from 4100L being characterized by NaHCO3 and 4850 by Na2SO4. pH values of 8.2 vs. 7.5, conductivities (μS) of 1790 vs. 7667 and alkalinities (mg/L) of 767 vs. 187 were obtained from 4100L and 4850, respectively. As expected, the deeper 4850L hole had the higher temperature (38 vs. 30 oC). Neither had measureable nitrate, but both had similar dissolved organic C (DOC) concentrations (0.8 vs. 0.9 mg/L). Sulfate was present at 337 vs. 4,470 mg/L in 4100L and 4850L. Major dissolved gases were N2 (91 and 81 vol%), O2 (12 and 16 vol%) and CH4 (0.07 and 3.35 vol%) in 4100L and 4850L. The δ13C of CH4 was -51 and -56.7 permil in 4100L and 4850, respectively. The uncorrected 14C age of DIC was calculated at 25,310 (+/- 220) and 47,700 (+/-3,100) years for the two fluids. Cell counts were 5.9e3 and 2.01e5 in 4100L and 4850. Microbial community structure was diverse in both holes and distinct from that of service water. A large proportion of rRNA library clones were

  16. Social Work and Engineering Collaboration: Forging Innovative Global Community Development Education

    Science.gov (United States)

    Gilbert, Dorie J.

    2014-01-01

    Interdisciplinary programs in schools of social work are growing in scope and number. This article reports on collaboration between a school of social work and a school of engineering, which is forging a new area of interdisciplinary education. The program engages social work students working alongside engineering students in a team approach to…

  17. A Pre-Engineering Program Using Robots to Attract Underrepresented High School and Community College Students

    Science.gov (United States)

    Mosley, Pauline Helen; Liu, Yun; Hargrove, S. Keith; Doswell, Jayfus T.

    2010-01-01

    This paper gives an overview of a new pre-engineering program--Robotics Technician Curriculum--that uses robots to solicit underrepresented students pursuing careers in science, technology, engineering, and mathematics (STEM). The curriculum uses a project-based learning environment, which consists of part lecture and part laboratory. This program…

  18. Shaping Software Engineering Curricula Using Open Source Communities: A Case Study

    Science.gov (United States)

    Bowring, James; Burke, Quinn

    2016-01-01

    This paper documents four years of a novel approach to teaching a two-course sequence in software engineering as part of the ABET-accredited computer science curriculum at the College of Charleston. This approach is team-based and centers on learning software engineering in the context of open source software projects. In the first course, teams…

  19. Restoring rocky intertidal communities: Lessons from a benthic macroalgal ecosystem engineer.

    Science.gov (United States)

    Bellgrove, Alecia; McKenzie, Prudence F; Cameron, Hayley; Pocklington, Jacqueline B

    2017-04-15

    As coastal population growth increases globally, effective waste management practices are required to protect biodiversity. Water authorities are under increasing pressure to reduce the impact of sewage effluent discharged into the coastal environment and restore disturbed ecosystems. We review the role of benthic macroalgae as ecosystem engineers and focus particularly on the temperate Australasian fucoid Hormosira banksii as a case study for rocky intertidal restoration efforts. Research focussing on the roles of ecosystem engineers is lagging behind restoration research of ecosystem engineers. As such, management decisions are being made without a sound understanding of the ecology of ecosystem engineers. For successful restoration of rocky intertidal shores it is important that we assess the thresholds of engineering traits (discussed herein) and the environmental conditions under which they are important. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. The Competence Readiness of the Electrical Engineering Vocational High School Teachers in Manado towards the ASEAN Economic Community Blueprint in 2025

    OpenAIRE

    Fid Jantje Tasiam; Djoko Kustono; Purnomo Purnomo; Hakkun Elmunsyah

    2017-01-01

    This paper presents the competence readiness of the electrical engineering vocational high school teachers in Manado towards ASEAN Economic Community blueprint in 2025. The objective of this study is to get the competencies readiness description of the electrical engineering vocational high school teachers in Manado towards ASEAN Economic Community blueprint in 2025. Method used quantitative and qualitative approach which the statistical analysis in quantitative and the inductive analysis use...

  1. Mitigating Climate Change at the Carbon Water Nexus: A Call to Action for the Environmental Engineering Community.

    Science.gov (United States)

    Clarens, Andres F; Peters, Catherine A

    2016-10-01

    Environmental engineers have played a critical role in improving human and ecosystem health over the past several decades. These contributions have focused on providing clean water and air as well as managing waste streams and remediating polluted sites. As environmental problems have become more global in scale and more deeply entrenched in sociotechnical systems, the discipline of environmental engineering must grow to be ready to respond to the challenges of the coming decades. Here we make the case that environmental engineers should play a leadership role in the development of climate change mitigation technologies at the carbon-water nexus (CWN). Climate change, driven largely by unfettered emissions of fossil carbon into the atmosphere, is a far-reaching and enormously complex environmental risk with the potential to negatively affect food security, human health, infrastructure, and other systems. Solving this problem will require a massive mobilization of existing and innovative new technology. The environmental engineering community is uniquely positioned to do pioneering work at the CWN using a skillset that has been honed, solving related problems. The focus of this special issue, on "The science and innovation of emerging subsurface energy technologies," provides one example domain within which environmental engineers and related disciplines are beginning to make important contributions at the CWN. In this article, we define the CWN and describe how environmental engineers can bring their considerable expertise to bear in this area. Then we review some of the topics that appear in this special issue, for example, mitigating the impacts of hydraulic fracturing and geologic carbon storage, and we provide perspective on emergent research directions, for example, enhanced geothermal energy, energy storage in sedimentary formations, and others.

  2. Effect of engineered environment on microbial community structure in biofilter and biofilm on reverse osmosis membrane

    KAUST Repository

    Jeong, Sanghyun; Cho, Kyungjin; Jeong, Dawoon; Lee, Seockheon; Leiknes, TorOve; Vigneswaran, Saravanamuthu; Bae, Hyokwan

    2017-01-01

    Four dual media filters (DMFs) were operated in a biofiltration mode with different engineered environments (DMF I and II: coagulation with/without acidification and DMF III and IV: without/with chlorination). Designed biofilm enrichment reactors

  3. Comparative Study of Various Delivery Methods for the Supply of Alpha-Ketoglutarate to the Neural Cells for Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Tanushree Vishnoi

    2013-01-01

    Full Text Available Delivery of growth factors or bioactive molecules plays an important role in tissue engineering, as the duration to which these are supplied can modulate the cell fate. Thus, the delivery method plays an important role, and the same is presented in this work wherein the exogenous supply of alpha-ketoglutarate (α-KG gave better results for fast proliferating cells as compared to delivery by microspheres or microspheres incorporated scaffolds which can be used while culturing slow growing cells. All these studies were performed in two dimensional (2D and three dimensional (3D setups in which chitosan-gelatin-polypyrrole has been used as 3-D scaffolds. Chitosan and gelatin microspheres alone as well as incorporated in the cryogels were characterized. MTT assay done using neuro-2a cell line showed approximately 42% and 70% increment in cellular proliferation when gelatin and chitosan microspheres were added in a 3-D setup, respectively, as compared to the control. Biochemical analysis of ammonia showed 6-fold reductions in ammonia level in a 3-D setup compared to the control. We also studied the synthesis of a neurotransmitter-like glutamate and found that its concentration increased up to 0.25 mg/ml when the microspheres were added exogenously in a 3-D system.

  4. Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes?

    Directory of Open Access Journals (Sweden)

    Emily B. Graham

    2016-02-01

    Full Text Available Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

  5. Model-based neural networks to predict emissions in a diesel engine operating with biodiesel blends of castor; Modelo basado en redes neuronales para predecir las emisiones en un motor diésel que opera con mezclas de biodiésel de higuerilla

    Directory of Open Access Journals (Sweden)

    Fabio Narváez

    2012-12-01

    Full Text Available Some identification methods of nonlinear systems using artificialneural networks are explained. Also, a model based on Neural Networks“Supervised Feed Forward” is presented, developed to identifyand predict the behavior of volumetric emissions from combustion of astationary diésel engine based on two input variables: the engine load and the mixture of castor biodiésel. The neural network training and model validation was performed by using the NNModel.

  6. Community

    Science.gov (United States)

    stability Science & Innovation Collaboration Careers Community Environment Science & Innovation Recruitment Events Community Commitment Giving Campaigns, Drives Economic Development Employee Funded neighbor pledge: contribute to quality of life in Northern New Mexico through economic development

  7. Integrated STEM: Focus on Informal Education and Community Collaboration through Engineering

    Science.gov (United States)

    Burrows, Andrea; Lockwood, Meghan; Borowczak, Mike; Janak, Edward; Barber, Brian

    2018-01-01

    This article showcases STEM as an interdisciplinary field in which the disciplines strengthen and support each other (not as separate science, technology, engineering, and mathematics disciplines). The authors focus on an open-ended, complex problem--water quality--as the primary teaching and learning task. The participants, middle school female…

  8. The Community project on engineering aspects of backfilling and sealing of radioactive waste repositories

    International Nuclear Information System (INIS)

    Lake, L.M.; Bennett, A.

    1987-01-01

    This report summarizes the work carried out under CEC contracts about engineering aspects of backfilling and sealing of radioactive waste repositories, for the time period 1983-84. It complements a previous report (ref. EUR 9283) on the same topic, this latter covering the period 1980-82

  9. Effects of Engineered Nanoparticles on Crops, their Symbionts, and Soil Microbial Communities

    NARCIS (Netherlands)

    Moll, Janine

    2016-01-01

    Engineered nanoparticles (NPs) are small particles (< 100 nm) that are widely used in electronics, paints, cosmetics, and composite materials. As a result of the production and use of NP containing materials, NPs are released into the environment. For future risk assessment it is, therefore,

  10. Epifaunal Community Development on Great Lakes Breakwaters: An Engineering with Nature Demonstration Project

    Science.gov (United States)

    2014-08-01

    Formliners, Inc . Pattern 1705, 305 Standard Spec Flute) and the dimpled surface used a custom built form liner using plywood and round headed carriage...J. Underwood. 2011. Evaluation of ecological engineering of “ armoured ” shorelines to improve their value as habitat. J. Exp. Marine Biol. Ecol. 400

  11. Initial Efforts in Characterizing Radiation and Plasma Effects on Space Assets: Bridging the Space Environment, Engineering and User Community

    Science.gov (United States)

    Zheng, Y.; Ganushkina, N. Y.; Guild, T. B.; Jiggens, P.; Jun, I.; Mazur, J. E.; Meier, M. M.; Minow, J. I.; Pitchford, D. A.; O'Brien, T. P., III; Shprits, Y.; Tobiska, W. K.; Xapsos, M.; Rastaetter, L.; Jordanova, V. K.; Kellerman, A. C.; Fok, M. C. H.

    2017-12-01

    The Community Coordinated Modeling Center (CCMC) has been leading the community-wide model validation projects for many years. Such effort has been broadened and extended via the newly-launched International Forum for Space Weather Modeling Capabilities Assessment (https://ccmc.gsfc.nasa.gov/assessment/), Its objective is to track space weather models' progress and performance over time, which is critically needed in space weather operations. The Radiation and Plasma Effects Working Team is working on one of the many focused evaluation topics and deals with five different subtopics: Surface Charging from 10s eV to 40 keV electrons, Internal Charging due to energetic electrons from hundreds keV to several MeVs. Single Event Effects from solar energetic particles (SEPs) and galactic cosmic rays (GCRs) (several MeV to TeVs), Total Dose due to accumulation of doses from electrons (>100 KeV) and protons (> 1 MeV) in a broad energy range, and Radiation Effects from SEPs and GCRs at aviation altitudes. A unique aspect of the Radiation and Plasma Effects focus area is that it bridges the space environments, engineering and user community. This presentation will summarize the working team's progress in metrics discussion/definition and the CCMC web interface/tools to facilitate the validation efforts. As an example, tools in the areas of surface charging/internal charging will be demoed.

  12. Ecosystem engineering effects of Aster tripolium and Salicornia procumbens saltmarsh on macrofaunal community structure

    NARCIS (Netherlands)

    Van der Wal, D.; Herman, P.M.J.

    2012-01-01

    This paper examines how perennial Aster tripolium and annual Salicornia procumbens salt marshes alter the biomass, density, taxon diversity, and community structure of benthic macrofauna, and also examines the role of elevation, sediment grain size, plant cover, and marsh age. Core samples were

  13. Engine-propeller power plant aircraft community noise reduction key methods

    Science.gov (United States)

    Moshkov P., A.; Samokhin V., F.; Yakovlev A., A.

    2018-04-01

    Basic methods of aircraft-type flying vehicle engine-propeller power plant noise reduction were considered including single different-structure-and-arrangement propellers and piston engines. On the basis of a semiempirical model the expressions for blade diameter and number effect evaluation upon propeller noise tone components under thrust constancy condition were proposed. Acoustic tests performed at Moscow Aviation institute airfield on the whole qualitatively proved the obtained ratios. As an example of noise and detectability reduction provision a design-and-experimental estimation of propeller diameter effect upon unmanned aircraft audibility boundaries was performed. Future investigation ways were stated to solve a low-noise power plant design problem for light aircraft and unmanned aerial vehicles.

  14. Mode Choice Modeling Using Artificial Neural Networks

    OpenAIRE

    Edara, Praveen Kumar

    2003-01-01

    Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

  15. Developing learning community model with soft skill integration for the building engineering apprenticeship programme in vocational high school

    Science.gov (United States)

    Sutrisno, Dardiri, Ahmad; Sugandi, R. Machmud

    2017-09-01

    This study aimed to address the procedure, effectiveness, and problems in the implementation of learning model for Building Engineering Apprenticeship Training Programme. This study was carried out through survey method and experiment. The data were collected using questionnaire, test, and assessment sheet. The collected data were examined through description, t-test, and covariance analysis. The results of the study showed that (1) the model's procedure covered preparation course, readiness assessment, assignment distribution, handing over students to apprenticeship instructors, task completion, assisting, field assessment, report writing, and follow-up examination, (2) the Learning Community model could significantly improve students' active learning, but not improve students' hard skills and soft skills, and (3) the problems emerging in the implementation of the model were (1) students' difficulties in finding apprenticeship places and qualified instructors, and asking for relevant tasks, (2) teachers' difficulties in determining relevant tasks and monitoring students, and (3) apprenticeship instructors' difficulties in assigning, monitoring, and assessing students.

  16. Community.

    Science.gov (United States)

    Grauer, Kit, Ed.

    1995-01-01

    Art in context of community is the theme of this newsletter. The theme is introduced in an editorial "Community-Enlarging the Definition" (Kit Grauer). Related articles include: (1) "The Children's Bridge is not Destroyed: Heart in the Middle of the World" (Emil Robert Tanay); (2) "Making Bridges: The Sock Doll…

  17. Maintaining distances with the engineer: patterns of coexistence in plant communities beyond the patch-bare dichotomy.

    Science.gov (United States)

    Pescador, David S; Chacón-Labella, Julia; de la Cruz, Marcelino; Escudero, Adrian

    2014-10-01

    Two-phase plant communities with an engineer conforming conspicuous patches and affecting the performance and patterns of coexisting species are the norm under stressful conditions. To unveil the mechanisms governing coexistence in these communities at multiple spatial scales, we have developed a new point-raster approach of spatial pattern analysis, which was applied to a Mediterranean high mountain grassland to show how Festuca curvifolia patches affect the local distribution of coexisting species. We recorded 22 111 individuals of 17 plant perennial species. Most coexisting species were negatively associated with F. curvifolia clumps. Nevertheless, bivariate nearest-neighbor analyses revealed that the majority of coexisting species were confined at relatively short distances from F. curvifolia borders (between 0-2 cm and up to 8 cm in some cases). Our study suggests the existence of a fine-scale effect of F. curvifolia for most species promoting coexistence through a mechanism we call 'facilitation in the halo'. Most coexisting species are displaced to an interphase area between patches, where two opposite forces reach equilibrium: attenuated severe conditions by proximity to the F. curvifolia canopy (nutrient-rich islands) and competitive exclusion mitigated by avoiding direct contact with F. curvifolia. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.

  18. National Geothermal Data System (NGDS) Geothermal Data: Community Requirements and Information Engineering

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Arlene [United States Department of Energy; Blackwell, David [Southern Methodist University; Chickering, Cathy [Southern Methodist University; Boyd, Toni [Oregon Institute of Technology; Horne, Roland [Stanford University; MacKenzie, Matthew [Uberity Technology Corporation; Moore, Joseph [University of Utah; Nickull, Duane [Uberity Technology Corporation; Richard, Stephen [Arizona Geological survey; Shevenell, Lisa A. [University of Nevada, Reno

    2013-10-01

    To satisfy the critical need for geothermal data to advance geothermal energy as a viable renewable energy contender, the U.S. Department of Energy is investing in the development of the National Geothermal Data System (NGDS). This paper outlines efforts among geothermal data providers nationwide to supply cutting edge geo-informatics. NGDS geothermal data acquisition, delivery, and methodology are discussed. In particular, this paper addresses the various types of data required to effectively assess geothermal energy potential and why simple links to existing data are insufficient. To create a platform for ready access by all geothermal stakeholders, the NGDS includes a work plan that addresses data assets and resources of interest to users, a survey of data providers, data content models, and how data will be exchanged and promoted, as well as lessons learned within the geothermal community.

  19. Neurosecurity: security and privacy for neural devices.

    Science.gov (United States)

    Denning, Tamara; Matsuoka, Yoky; Kohno, Tadayoshi

    2009-07-01

    An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.

  20. The Competence Readiness of the Electrical Engineering Vocational High School Teachers in Manado towards the ASEAN Economic Community Blueprint in 2025

    Directory of Open Access Journals (Sweden)

    Fid Jantje Tasiam

    2017-08-01

    Full Text Available This paper presents the competence readiness of the electrical engineering vocational high school teachers in Manado towards ASEAN Economic Community blueprint in 2025. The objective of this study is to get the competencies readiness description of the electrical engineering vocational high school teachers in Manado towards ASEAN Economic Community blueprint in 2025. Method used quantitative and qualitative approach which the statistical analysis in quantitative and the inductive analysis used in qualitative. There were 46 teachers of the electrical engineering vocational high school in Manado observed. The results have shown that the competencies readiness of the electrical engineering vocational high school teachers in Manado such as: pedagogical, professional, personality, and social were 13.04%, 19.56%, 19.56%, and 19.56% respectively. The results were still far from the focus of the ASEAN economic community blueprint in 2025, so they need to be improved through in-house training, internship programs, school partnerships, distance learning, tiered training and special training, short courses in educational institutions, internal coaching by schools, discussion of educational issues, workshops, research and community service, textbook writing, learning media making, and the creation of technology and art.

  1. Aged-engineered nanoparticles effect on sludge anaerobic digestion performance and associated microbial communities.

    Science.gov (United States)

    Eduok, Samuel; Ferguson, Robert; Jefferson, Bruce; Villa, Raffaella; Coulon, Frédéric

    2017-12-31

    To investigate the potential effect of aged engineered nanoparticles (a-ENPs) on sludge digestion performance, 150L pilot anaerobic digesters (AD) were fed with a blend of primary and waste activated sludge spiked either with a mixture of silver oxide, titanium dioxide and zinc oxide or a mixture of their equivalent bulk metal salts to achieve a target concentration of 250, 2000, and 2800mgkg -1 dry weight, respectively. Volatile fatty acids (VFA) were 1.2 times higher in the spiked digesters and significantly different (p=0.05) from the control conditions. Specifically, isovaleric acid concentration was 2 times lower in the control digester compared to the spiked digesters, whereas hydrogen sulfide was 2 times lower in the ENPs spiked digester indicating inhibitory effect on sulfate reducing microorganisms. Based on the ether-linked isoprenoids concentration, the total abundance of methanogens was 1.4 times lower in the ENPs spiked digester than in the control and metal salt spiked digesters. Pyrosequencing indicated 80% decrease in abundance and diversity of methanogens in ENPs spiked digester compared to the control digester. Methanosarcina acetivorans and Methanosarcina barkeri were identified as nano-tolerant as their relative abundance increased by a factor of 6 and 11, respectively, compared to the other digesters. The results further provide compelling evidence on the resilience of Fusobacteria, Actinobacteria and the Trojan horse-like effect of ENPs which offered a competitive advantage to some organisms while reducing microbial abundance and diversity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  2. Exploring the Academic and Social Experiences of Latino Engineering Community College Transfer Students at a 4-Year Institution: A Qualitative Research Study

    Science.gov (United States)

    Hagler, LaTesha R.

    As the number of historically underrepresented populations transfer from community college to university to pursue baccalaureate degrees in science, technology, engineering, and mathematics (STEM), little research exists about the challenges and successes Latino students experience as they transition from 2-year colleges to 4-year universities. Thus, institutions of higher education have limited insight to inform their policies, practices, and strategic planning in developing effective sources of support, services, and programs for underrepresented students in STEM disciplines. This qualitative research study explored the academic and social experiences of 14 Latino engineering community college transfer students at one university. Specifically, this study examined the lived experiences of minority community college transfer students' transition into and persistence at a 4-year institution. The conceptual framework applied to this study was Schlossberg's Transition Theory, which analyzed the participant's social and academic experiences that led to their successful transition from community college to university. Three themes emerged from the narrative data analysis: (a) Academic Experiences, (b) Social Experiences, and (c) Sources of Support. The findings indicate that engineering community college transfer students experience many challenges in their transition into and persistence at 4-year institutions. Some of the challenges include lack of academic preparedness, environmental challenges, lack of time management skills and faculty serving the role as institutional agents.

  3. 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

  4. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

    This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

  5. An efficient automated parameter tuning framework for spiking neural networks.

    Science.gov (United States)

    Carlson, Kristofor D; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L

    2014-01-01

    As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

  6. Hybrid intelligent engineering systems

    CERN Document Server

    Jain, L C; Adelaide, Australia University of

    1997-01-01

    This book on hybrid intelligent engineering systems is unique, in the sense that it presents the integration of expert systems, neural networks, fuzzy systems, genetic algorithms, and chaos engineering. It shows that these new techniques enhance the capabilities of one another. A number of hybrid systems for solving engineering problems are presented.

  7. Reflections of a community-based participatory researcher from the intersection of disability advocacy, engineering, and the academy.

    Science.gov (United States)

    Raymaker, Dora M

    2017-09-01

    This article uses an evocative autoethnographic approach to explore the experience of being an insider-researcher in a community-based participatory research setting. Taking a holistic perspective and using the form of narrative story-telling, I examine the dynamics between the typically marginalizing (but sometimes empowering) experience of being an autistic woman and the typically privileging (but sometimes oppressive) experience of being an engineering professional, during a time of career upheaval. Themes of motivations and mentors, adversity from social services and the academy, belonging, the slipperiness of intersectional positioning, feedback cycles of opportunity, dichotomies of competence and inadequacy, heightened stakes, and power and resistance are explored through the narrative. While primarily leaving the narrative to speak for itself per the qualitative approach taken, the article concludes with a discussion of how the personal experiences described relate both to the broader work of insider-researchers within disability-related fields, and to misconceptions about self-reflection and capacity for story-telling in individuals on the autism spectrum.

  8. Sessile macro-epibiotic community of solitary ascidians, ecosystem engineers in soft substrates of Potter Cove, Antarctica

    Directory of Open Access Journals (Sweden)

    Clara Rimondino

    2015-01-01

    Full Text Available The muddy bottoms of inner Potter Cove, King George Island (Isla 25 de Mayo, South Shetlands, Antarctica, show a high density and richness of macrobenthic species, particularly ascidians. In other areas, ascidians have been reported to play the role of ecosystem engineers, as they support a significant number of epibionts, increasing benthic diversity. In this study, a total of 21 sessile macro-epibiotic taxa present on the ascidian species Corella antarctica Sluiter, 1905, Cnemidocarpa verrucosa (Lesson, 1830 and Molgula pedunculata Herdman, 1881 were identified, with Bryozoa being the most diverse. There were differences between the three ascidian species in terms of richness, percent cover and diversity of sessile macro-epibionts. The morphological characteristics of the tunic surface, the available area for colonization (and its relation with the age of the basibiont individuals and the pH of the ascidian tunic seem to explain the observed differences. Recent environmental changes in the study area (increase of suspended particulate matter caused by glaciers retreat have been related to observed shifts in the benthic community structure, negatively affecting the abundance and distribution of the studied ascidian species. Considering the diversity of sessile macro-epibionts found on these species, the impact of environmental shifts may be greater than that estimated so far.

  9. Exploring Counseling Services and Their Impact on Female, Underrepresented Minority Community College Students in Science, Technology, Engineering, and Math: A Qualitative Study

    Science.gov (United States)

    Strother, Elizabeth

    The economic future of the United States depends on developing a workforce of professionals in science, technology, engineering and mathematics (Adkins, 2012; Mokter Hossain & Robinson, 2012). In California, the college population is increasingly female and underrepresented minority, a population that has historically chosen to study majors other than STEM. In California, community colleges provide a major inroad for students seeking to further their education in one of the many universities in the state. The recent passage of Senate Bill 1456 and the Student Success and Support Program mandate increased counseling services for all California community college students (California Community College Chancellors Office, 2014). This dissertation is designed to explore the perceptions of female, underrepresented minority college students who are majoring in an area of science, technology, engineering and math, as they relate to community college counseling services. Specifically, it aims to understand what counseling services are most effective, and what community college counselors can do to increase the level of interest in STEM careers in this population. This is a qualitative study. Eight participants were interviewed for the case study, all of whom are current or former community college students who have declared a major in a STEM discipline. The semi-structured interviews were designed to help understand what community college counselors can do to better serve this population, and to encourage more students to pursue STEM majors and careers. Through the interviews, themes emerged to explain what counseling services are the most helpful. Successful STEM students benefited from counselors who showed empathy and support. Counselors who understood the intricacies of educational planning for STEM majors were considered the most efficacious. Counselors who could connect students with enrichment activities, such as internships, were highly valued, as were counseling

  10. Governing Engineering

    DEFF Research Database (Denmark)

    Buch, Anders

    2012-01-01

    Most people agree that our world face daunting problems and, correctly or not, technological solutions are seen as an integral part of an overall solution. But what exactly are the problems and how does the engineering ‘mind set’ frame these problems? This chapter sets out to unravel dominant...... perspectives in challenge per-ception in engineering in the US and Denmark. Challenge perception and response strategies are closely linked through discursive practices. Challenge perceptions within the engineering community and the surrounding society are thus critical for the shaping of engineering education...... and the engineering profession. Through an analysis of influential reports and position papers on engineering and engineering education the chapter sets out to identify how engineering is problematized and eventually governed. Drawing on insights from governmentality studies the chapter strives to elicit the bodies...

  11. Governing Engineering

    DEFF Research Database (Denmark)

    Buch, Anders

    2011-01-01

    Abstract: Most people agree that our world faces daunting problems and, correctly or not, technological solutions are seen as an integral part of an overall solution. But what exactly are the problems and how does the engineering ‘mind set’ frame these problems? This chapter sets out to unravel...... dominant perspectives in challenge perception in engineering in the US and Denmark. Challenge perception and response strategies are closely linked through discursive practices. Challenge perceptions within the engineering community and the surrounding society are thus critical for the shaping...... of engineering education and the engineering profession. Through an analysis of influential reports and position papers on engineering and engineering education the chapter sets out to identify how engineering is problematized and eventually governed. Drawing on insights from governmentality studies the chapter...

  12. Nitric oxide and nitrous oxide turnover in natural and engineered microbial communities: biological pathways, chemical reactions, and novel technologies

    Science.gov (United States)

    Schreiber, Frank; Wunderlin, Pascal; Udert, Kai M.; Wells, George F.

    2012-01-01

    Nitrous oxide (N2O) is an environmentally important atmospheric trace gas because it is an effective greenhouse gas and it leads to ozone depletion through photo-chemical nitric oxide (NO) production in the stratosphere. Mitigating its steady increase in atmospheric concentration requires an understanding of the mechanisms that lead to its formation in natural and engineered microbial communities. N2O is formed biologically from the oxidation of hydroxylamine (NH2OH) or the reduction of nitrite (NO−2) to NO and further to N2O. Our review of the biological pathways for N2O production shows that apparently all organisms and pathways known to be involved in the catabolic branch of microbial N-cycle have the potential to catalyze the reduction of NO−2 to NO and the further reduction of NO to N2O, while N2O formation from NH2OH is only performed by ammonia oxidizing bacteria (AOB). In addition to biological pathways, we review important chemical reactions that can lead to NO and N2O formation due to the reactivity of NO−2, NH2OH, and nitroxyl (HNO). Moreover, biological N2O formation is highly dynamic in response to N-imbalance imposed on a system. Thus, understanding NO formation and capturing the dynamics of NO and N2O build-up are key to understand mechanisms of N2O release. Here, we discuss novel technologies that allow experiments on NO and N2O formation at high temporal resolution, namely NO and N2O microelectrodes and the dynamic analysis of the isotopic signature of N2O with quantum cascade laser absorption spectroscopy (QCLAS). In addition, we introduce other techniques that use the isotopic composition of N2O to distinguish production pathways and findings that were made with emerging molecular techniques in complex environments. Finally, we discuss how a combination of the presented tools might help to address important open questions on pathways and controls of nitrogen flow through complex microbial communities that eventually lead to N2O build

  13. Nitric oxide and nitrous oxide turnover in natural and engineered microbial communities: biological pathways, chemical reactions and novel technologies

    Directory of Open Access Journals (Sweden)

    Frank eSchreiber

    2012-10-01

    Full Text Available Nitrous oxide (N2O is an environmentally important atmospheric trace gas because it is an effective greenhouse gas and it leads to ozone depletion through photo-chemical nitric oxide (NO production in the stratosphere. Mitigating its steady increase in atmospheric concentration requires an understanding of the mechanisms that lead to its formation in natural and engineered microbial communities. N2O is formed biologically from the oxidation of hydroxylamine (NH2OH or the reduction of nitrite (NO2- to NO and further to N2O. Our review of the biological pathways for N2O production shows that apparently all organisms and pathways known to be involved in the catabolic branch of microbial N-cycle have the potential to catalyze the reduction of NO2- to NO and the further reduction of NO to N2O, while N2O formation from NH2OH is only performed by ammonia oxidizing bacteria. In addition to biological pathways, we review important chemical reactions that can lead to NO and N2O formation due to the reactivity of NO2-, NH2OH and nitroxyl (HNO. Moreover, biological N2O formation is highly dynamic in response to N-imbalance imposed on a system. Thus, understanding NO formation and capturing the dynamics of NO and N2O build-up are key to understand mechanisms of N2O release. Here, we discuss novel technologies that allow experiments on NO and N2O formation at high temporal resolution, namely NO and N2O microelectrodes and the dynamic analysis of the isotopic signature of N2O with quantum cascade laser based absorption spectroscopy. In addition, we introduce other techniques that use the isotopic composition of N2O to distinguish production pathways and findings that were made with emerging molecular techniques in complex environments. Finally, we discuss how a combination of the presented tools might help to address important open questions on pathways and controls of nitrogen flow through complex microbial communities that eventually lead to N2O build-up.

  14. Metabolic Engineering X Conference

    Energy Technology Data Exchange (ETDEWEB)

    Flach, Evan [American Institute of Chemical Engineers

    2015-05-07

    The International Metabolic Engineering Society (IMES) and the Society for Biological Engineering (SBE), both technological communities of the American Institute of Chemical Engineers (AIChE), hosted the Metabolic Engineering X Conference (ME-X) on June 15-19, 2014 at the Westin Bayshore in Vancouver, British Columbia. It attracted 395 metabolic engineers from academia, industry and government from around the globe.

  15. Introduction to neural networks

    International Nuclear Information System (INIS)

    Pavlopoulos, P.

    1996-01-01

    This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

  16. 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.

  17. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  18. 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.

  19. Scheduling with artificial neural networks

    OpenAIRE

    Gürgün, Burçkaan

    1993-01-01

    Ankara : Department of Industrial Engineering and The Institute of Engineering and Sciences of Bilkent Univ., 1993. Thesis (Master's) -- Bilkent University, 1993. Includes bibliographical references leaves 59-65. Artificial Neural Networks (ANNs) attempt to emulate the massively parallel and distributed processing of the human brain. They are being examined for a variety of problems that have been very difficult to solve. The objective of this thesis is to review the curren...

  20. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report 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: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  1. Neural networks in signal processing

    International Nuclear Information System (INIS)

    Govil, R.

    2000-01-01

    Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

  2. Optoelectronic Implementation of Neural Networks

    Indian Academy of Sciences (India)

    neural networks, such as learning, adapting and copying by means of parallel ... to provide robust recognition of hand-printed English text. Engine idle and misfiring .... and s represents the bounded activation function of a neuron. It is typically ...

  3. Evaluation of environmental management resources (ISO 14001) at civil engineering construction worksites: a case study of the community of Madrid.

    Science.gov (United States)

    Rodríguez, Gracia; Alegre, Francisco Javier; Martínez, Germán

    2011-07-01

    In recent years, significant advances have been made in business organization and management. The growing demands of clients as well as the globalization of world markets are among the many factors that have led to the establishment of systems of quality control and environmental management as a competitive strategy for businesses. When compared to other professional sectors, the construction sector has been slower to respond to environmental problems and to adopt Environmental Management Systems (EMS). In the world today the ISO 14001 standard is currently the main frame of reference used by construction companies to implement this type of management system. This article presents the results of a general study regarding the evaluation of the application of the ISO 14001 standard at civil engineering construction worksites in the Community of Madrid (Spain), specifically pertaining to requirement 4.4.1, Resources, roles, responsibilities, and authority. According to requirement 4.4.1, company executives should appoint people responsible for implementing the EMS and also specify their responsibilities and functions. The personnel designated for supervising environmental work should also have sufficient authority to establish and maintain the EMS. The results obtained were the following: - EMS supervisors did not generally possess adequate training and solid experience in construction work and in the environment. Furthermore, supervisors were usually forced to combine their environmental work with other tasks, which made their job even more difficult. - Generally speaking, supervisors were not given sufficient authority and autonomy because productivity at the construction site had priority over environmental management. This was due to the fact that the company management did not have a respectful attitude toward the environment, nor was the management actively involved in the establishment of the EMS. - Insufficient resources were allocated to the Environmental

  4. Radial basis function neural network in fault detection of automotive ...

    African Journals Online (AJOL)

    Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

  5. Design and manufacturing challenges of optogenetic neural interfaces: a review

    Science.gov (United States)

    Goncalves, S. B.; Ribeiro, J. F.; Silva, A. F.; Costa, R. M.; Correia, J. H.

    2017-08-01

    Optogenetics is a relatively new technology to achieve cell-type specific neuromodulation with millisecond-scale temporal precision. Optogenetic tools are being developed to address neuroscience challenges, and to improve the knowledge about brain networks, with the ultimate aim of catalyzing new treatments for brain disorders and diseases. To reach this ambitious goal the implementation of mature and reliable engineered tools is required. The success of optogenetics relies on optical tools that can deliver light into the neural tissue. Objective/Approach: Here, the design and manufacturing approaches available to the scientific community are reviewed, and current challenges to accomplish appropriate scalable, multimodal and wireless optical devices are discussed. Significance: Overall, this review aims at presenting a helpful guidance to the engineering and design of optical microsystems for optogenetic applications.

  6. When Disney Meets the Research Park: Metaphors and Models for Engineering an Online Learning Community of Tomorrow

    Science.gov (United States)

    Chenail, Ronald J.

    2004-01-01

    It is suggested that educators look to an environment in which qualitative research can be learned in more flexible and creative ways--an online learning community known as the Research Park Online (RPO). This model, based upon Walt Disney's 1966 plan for his "Experimental Prototype Community of Tomorrow" (EPCOT) and university cooperative…

  7. Pima Community College Planning Grant for Autonomous Intelligent Network of Systems (AINS) Science, Mathematics and Engineering Education Center

    National Research Council Canada - National Science Library

    2006-01-01

    .... The Center was to be funded by the Department of Defense, Office of Naval Research (ONR). The TDRI AINS Center's objectives were to advance ONR's technologies and to improve exposure and participation in science, math, and engineering (SME...

  8. An engineering context for software engineering

    OpenAIRE

    Riehle, Richard D.

    2008-01-01

    New engineering disciplines are emerging in the late Twentieth and early Twenty-first Century. One such emerging discipline is software engineering. The engineering community at large has long harbored a sense of skepticism about the validity of the term software engineering. During most of the fifty-plus years of software practice, that skepticism was probably justified. Professional education of software developers often fell short of the standard expected for conventional engineers; so...

  9. Neural networks. A new analytical tool, applicable also in nuclear technology

    International Nuclear Information System (INIS)

    Stritar, A.

    1992-01-01

    The basic concept of neural networks and back propagation learning algorithm are described. The behaviour of typical neural network is demonstrated on a simple graphical case. A short literature survey about the application of neural networks in nuclear science and engineering is made. The application of the neural network to the probability density calculation is shown. (author) [sl

  10. Metagenomic insights into effects of spent engine oil perturbation on the microbial community composition and function in a tropical agricultural soil.

    Science.gov (United States)

    Salam, Lateef B; Obayori, Sunday O; Nwaokorie, Francisca O; Suleiman, Aisha; Mustapha, Raheemat

    2017-03-01

    Analyzing the microbial community structure and functions become imperative for ecological processes. To understand the impact of spent engine oil (SEO) contamination on microbial community structure of an agricultural soil, soil microcosms designated 1S (agricultural soil) and AB1 (agricultural soil polluted with SEO) were set up. Metagenomic DNA extracted from the soil microcosms and sequenced using Miseq Illumina sequencing were analyzed for their taxonomic and functional properties. Taxonomic profiling of the two microcosms by MG-RAST revealed the dominance of Actinobacteria (23.36%) and Proteobacteria (52.46%) phyla in 1S and AB1 with preponderance of Streptomyces (12.83%) and Gemmatimonas (10.20%) in 1S and Geodermatophilus (26.24%), Burkholderia (15.40%), and Pseudomonas (12.72%) in AB1, respectively. Our results showed that soil microbial diversity significantly decreased in AB1. Further assignment of the metagenomic reads to MG-RAST, Cluster of Orthologous Groups (COG) of proteins, Kyoto Encyclopedia of Genes and Genomes (KEGG), GhostKOALA, and NCBI's CDD hits revealed diverse metabolic potentials of the autochthonous microbial community. It also revealed the adaptation of the community to various environmental stressors such as hydrocarbon hydrophobicity, heavy metal toxicity, oxidative stress, nutrient starvation, and C/N/P imbalance. To the best of our knowledge, this is the first study that investigates the effect of SEO perturbation on soil microbial communities through Illumina sequencing. The results indicated that SEO contamination significantly affects soil microbial community structure and functions leading to massive loss of nonhydrocarbon degrading indigenous microbiota and enrichment of hydrocarbonoclastic organisms such as members of Proteobacteria and Actinobacteria.

  11. Using Self-Determination Theory to build communities of support to aid in the retention of women in engineering

    Science.gov (United States)

    Dell, Elizabeth M.; Verhoeven, Yen; Christman, Jeanne W.; Garrick, Robert D.

    2018-05-01

    Diverse perspectives are required to address the technological problems facing our world. Although women perform as well as their male counterparts in math and science prior to entering college, the numbers of women students entering and completing engineering programmes are far below their representation in the workforce. This paper reports on a qualitative, multiyear study of the experiences of women students in an Engineering Technology programme. The project addressed some of the unique, fundamental challenges that female students face within their programmes, and the authors describe a programmatic framework based on Self-Determination Theory as an intervention for the recruitment and retention of female engineering students. Data from focus groups and interviews show how students were supported in their undergraduate experiences and how inclusive learning environments are needed to further improve outcomes. Conceptual issues and methodological considerations of our outcomes are presented.

  12. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  13. Persistance of a surrogate for a genetically engineered cellulolytic microorganism and effects on aquatic community and ecosystem properties: Mesocosm and stream comparisons

    International Nuclear Information System (INIS)

    Bott, T.L.; Kaplan, L.A.

    1993-01-01

    The accidental or deliberate release of genetically engineered microorganisms (GEMs) into the environment raises concerns related to their potential to alter natural processes and biological communities. Research was conducted to determine the persistance of an introduced surrogate for a GEM in lotic habitats, to test the responses to the introduced bacterial, and to evaluate the utility of flowing water mesocosms as tools for assessing the fates and effects of bacteria introduced into streams. Cellulolomonas cellulose-degrading bacteria were selcted as the GEM surrogate because cellulose superdegrader bacteria are being genetically engineered and are of interest to the food and paper industries and in the conversion of biomass to fuels. Cellulomonas densities were determined using fluorescent antibodies, and declined from postinoculation maxima faster in sediments than in Chlorophyta growths and leaf packs. Cellulomonas persisted in leaf packs at densities much greater than background. Cellulomonas had no statistically significant effects on primary productivity, community respiration, photosynthesis/respiration ratios, assimilation ratios, bacterial productivity, and rates of leaf litter decomposition. Cellulase concentrations were positively correlated with Cellulolomonas densities ≥7x10 8 cells/g dry mass in fresh leaf litter for 2 d following exposure. Mesocosms were good tools for studying bacterial population dynamics in leaf litter and physiological aspects of litter degradation. 45 refs., 8 figs., 5 tabs

  14. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  15. Using Self-Determination Theory to Build Communities of Support to Aid in the Retention of Women in Engineering

    Science.gov (United States)

    Dell, Elizabeth M.; Verhoeven, Yen; Christman, Jeanne W.; Garrick, Robert D.

    2018-01-01

    Diverse perspectives are required to address the technological problems facing our world. Although women perform as well as their male counterparts in math and science prior to entering college, the numbers of women students entering and completing engineering programmes are far below their representation in the workforce. This paper reports on a…

  16. Sessile macro-epibiotic community of solitary ascidians, ecosystem engineers in soft substrates of Potter Cove, Antarctica

    OpenAIRE

    Rimondino, Clara; Torre, Luciana; Sahade, Ricardo Jose; Tatian, Marcos

    2016-01-01

    The muddy bottoms of inner Potter Cove, King George Island (Isla 25 de Mayo), South Shetlands, Antarctica, show a high density and richness of macrobenthic species, particularly ascidians. In other areas, ascidians have been reported to play the role of ecosystem engineers, as they support a significant number of epibionts, increasing benthic diversity. In this study, a total of 21 sessile macro-epibiotic taxa present on the ascidian species Corella antarctica Sluiter, 1905, Cnemidocarpa verr...

  17. Neural Networks Methodology and Applications

    CERN Document Server

    Dreyfus, Gérard

    2005-01-01

    Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...

  18. Neural networks with discontinuous/impact activations

    CERN Document Server

    Akhmet, Marat

    2014-01-01

    This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

  19. Fluids engineering

    International Nuclear Information System (INIS)

    Anon.

    1991-01-01

    Fluids engineering has played an important role in many applications, from ancient flood control to the design of high-speed compact turbomachinery. New applications of fluids engineering, such as in high-technology materials processing, biotechnology, and advanced combustion systems, have kept up unwaining interest in the subject. More accurate and sophisticated computational and measurement techniques are also constantly being developed and refined. On a more fundamental level, nonlinear dynamics and chaotic behavior of fluid flow are no longer an intellectual curiosity and fluid engineers are increasingly interested in finding practical applications for these emerging sciences. Applications of fluid technology to new areas, as well as the need to improve the design and to enhance the flexibility and reliability of flow-related machines and devices will continue to spur interest in fluids engineering. The objectives of the present seminar were: to exchange current information on arts, science, and technology of fluids engineering; to promote scientific cooperation between the fluids engineering communities of both nations, and to provide an opportunity for the participants and their colleagues to explore possible joint research programs in topics of high priority and mutual interest to both countries. The Seminar provided an excellent forum for reviewing the current state and future needs of fluids engineering for the two nations. With the Seminar ear-marking the first formal scientific exchange between Korea and the United States in the area of fluids engineering, the scope was deliberately left broad and general

  20. Estimation of Conditional Quantile using Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1999-01-01

    The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing con-sistency on a mild set of assumptions is provided. The constructed structure constitutes a basis...... for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating...... the capabilities of the elaborated neural network are also given....

  1. Effects of ecological engineered oxygenation on the bacterial community structure in an anoxic fjord in western Sweden

    DEFF Research Database (Denmark)

    Forth, M.; Liljebladh, B.; Stigebrandt, A.

    2015-01-01

    Oxygen-depleted bodies of water are becoming increasingly common in marine ecosystems. Solutions to reverse this trend are needed and under development, for example, by the Baltic deep-water OXygenation (BOX) project. In the framework of this project, the Swedish Byfjord was chosen for a pilot...... in the lower water column and the benthic zone up to 110 mumol l(-1).We used molecular ecologic methods to study changes in bacterial community structure in response to the oxygenation in the Byfjord. Water column samples from before, during and after the oxygenation as well as from two nearby control fjords...

  2. Applicability of a septic tank/engineered wetland coupled system in the treatment and recycling of wastewater from a small community.

    Science.gov (United States)

    Mbuligwe, Stephen E

    2005-01-01

    A septic tank (ST)/engineered wetland coupled system used to treat and recycle wastewater from a small community in Dar es Salaam, Tanzania was monitored to assess its performance. The engineered wetland system (EWS) had two parallel units each with two serial beds packed with different sizes of media and vegetated differently. The larger-sized medium bed was upstream and was planted with Phragmites (reeds) and the smaller-sized medium bed was downstream and was planted with Typha (cattails). The ST/EWS coupled system was able to remove ammonia by an average of 60%, nitrate by 71%, sulfate by 55%, chemical oxygen demand by 91%, and fecal coliform as well as total coliform by almost 100%. The effluent from the ST/EWS coupled system is used for irrigation. Notably, users of the recycled irrigation water do not harbor any negative feelings about it. This study demonstrates that it is possible to treat and recycle domestic wastewater using ST/ EWS coupled systems. The study also brings attention to the fact that an ST/EWS coupled system has operation and maintenance (O&M) needs that must be fulfilled for its effectiveness and acceptability. These include removal of unwanted weeds, harvesting of wetland plants when the EWS becomes unappealingly bushy, and routine repair.

  3. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  4. An introduction to neural network methods for differential equations

    CERN Document Server

    Yadav, Neha; Kumar, Manoj

    2015-01-01

    This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks, and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed...

  5. Non-invasive neural stimulation

    Science.gov (United States)

    Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

    2017-05-01

    Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

  6. Knowledge and periconceptional use of folic acid for the prevention of neural tube defects in ethnic communities in the United Kingdom: systematic review and meta-analysis.

    Science.gov (United States)

    Peake, Jordana N; Copp, Andrew J; Shawe, Jill

    2013-07-01

    It is widely accepted that periconceptional supplementation with folic acid can prevent a significant proportion of neural tube defects (NTDs). The present study evaluated how folic acid knowledge and periconceptional use for NTD prevention varies by ethnicity in the United Kingdom (U.K.). A literature search was conducted to identify studies that included assessment of folic acid knowledge or use in U.K. women of different ethnicities. Only research and referenced sources published after 1991, the year of the landmark Medical Research Council's Vitamin Study, were included. A meta-analysis was performed of studies that assessed preconceptional folic acid use in Caucasians and non-Caucasians. Five studies met the inclusion criteria for assessment of knowledge and/or use of folic acid supplements in U.K. women including non-Caucasians. The available evidence indicates that South Asians specifically have less knowledge and lower periconceptional use of folic acid than Caucasians; one study found that West Indian and African women also had lower folic acid uptake. A synthesis of results from three of the studies, in a meta-analysis, shows that Caucasians are almost three times more likely to take folic acid before conception than non-Caucasians. From the limited evidence available, U.K. women of non-Caucasian ethnicity appear to have less knowledge and a lower uptake of folic acid supplementation than Caucasians during the periconceptional period. Implementing targeted, innovative education campaigns together with a mandatory fortification policy, including the fortification of ethnic minority foods, will be required for maximum prevention of folic acid-preventable NTDs across different ethnic groups. Copyright © 2013 Wiley Periodicals, Inc.

  7. THE READINESS OF FOREIGN WORKERS REGULATIONS IN THE ENGINEERING AND MEDICAL PRACTITIONERS SECTOR ENTERING THE ASEAN ECONOMIC COMMUNITY

    Directory of Open Access Journals (Sweden)

    Agusmidah

    2016-01-01

    Full Text Available Protection of the domestic labor market and prevention of skilled foreign workers entry through negative list are not in accordance with free market principle of the ASEAN Economic Community (AEC to be implemented in ASEAN countries such as Indonesia in the second half of 2015. However, restrictions are still practiced by some Indonesian government institutions, such as Ministry of Health for doctors, dentists, and nurses, the Ministry of Public Works for surveyors, and the Ministry of Tourism for tourism profesionals. Through literature study and legal analysis, it was found that foreign workers restriction by professional associations according to certain competency standards aims to prevent domestic work from being monopolized by skilled foreign workers in the AEC 2015 era.

  8. A 12-Week Physical and Cognitive Exercise Program Can Improve Cognitive Function and Neural Efficiency in Community-Dwelling Older Adults: A Randomized Controlled Trial.

    Science.gov (United States)

    Nishiguchi, Shu; Yamada, Minoru; Tanigawa, Takanori; Sekiyama, Kaoru; Kawagoe, Toshikazu; Suzuki, Maki; Yoshikawa, Sakiko; Abe, Nobuhito; Otsuka, Yuki; Nakai, Ryusuke; Aoyama, Tomoki; Tsuboyama, Tadao

    2015-07-01

    To investigate whether a 12-week physical and cognitive exercise program can improve cognitive function and brain activation efficiency in community-dwelling older adults. Randomized controlled trial. Kyoto, Japan. Community-dwelling older adults (N = 48) were randomized into an exercise group (n = 24) and a control group (n = 24). Exercise group participants received a weekly dual task-based multimodal exercise class in combination with pedometer-based daily walking exercise during the 12-week intervention phase. Control group participants did not receive any intervention and were instructed to spend their time as usual during the intervention phase. The outcome measures were global cognitive function, memory function, executive function, and brain activation (measured using functional magnetic resonance imaging) associated with visual short-term memory. Exercise group participants had significantly greater postintervention improvement in memory and executive functions than the control group (P cognitive exercise program can improve the efficiency of brain activation during cognitive tasks in older adults, which is associated with improvements in memory and executive function. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.

  9. assessment of neural networks performance in modeling rainfall ...

    African Journals Online (AJOL)

    Sholagberu

    neural network architecture for precipitation prediction of Myanmar, World Academy of. Science, Engineering and Technology, 48, pp. 130 – 134. Kumarasiri, A.D. and Sonnadara, D.U.J. (2006). Rainfall forecasting: an artificial neural network approach, Proceedings of the Technical Sessions,. 22, pp. 1-13 Institute of Physics ...

  10. Engineering qualifications for competence

    International Nuclear Information System (INIS)

    Levy, J.C.

    1990-01-01

    The Engineering Council has a responsibility across all fields of engineering to set standards for those who are registered as Chartered Engineers, Incorporated Engineers or Engineering Technicians. These standards amount to a basic specification of professional competence to which must be added the features needed in each different branch of engineering, such as nuclear, and each particular occupation, such as quality assurance. This article describes The Engineering Council's general standards and includes a guide to the roles and responsibilities which should lie within the domain of those who are registered with the Council. The concluding section describes the title of European Engineer and its relationship to the European Community directive governing the movement of professionals across community frontiers. (author)

  11. The Engineering 4 Health Challenge - an interdisciplinary and intercultural initiative to foster student engagement in B.C. and improve health care for children in under-serviced communities.

    Science.gov (United States)

    Price, Morgan; Weber-Jahnke, Jens H

    2009-01-01

    This paper describes the Engineering 4 Health (E4H) Challenge, an interdisciplinary and intercultural initiative that, on the one hand, seeks to improve health education of children in under-serviced communities and, on the other, seeks to attract students in British Columbia to professions in engineering and health. The E4H Challenge engages high school and university students in BC to cooperatively design and develop health information and communication technology (ICT) to educate children living in under-serviced communities. The E4H Challenge works with the One Laptop Per Child (OLPC) program to integrate applications for health awareness into the school programs of communities in developing countries. Although applications developed by the E4H Challenge use the low-cost, innovative XO laptop (the "$100 laptop" developed by the OLPC foundation) the software can also be used with other inexpensive hardware.

  12. Decay extent evaluation of wood degraded by a fungal community using NIRS: application for ecological engineering structures used for natural hazard mitigation

    Science.gov (United States)

    Baptiste Barré, Jean; Bourrier, Franck; Bertrand, David; Rey, Freddy

    2015-04-01

    Ecological engineering corresponds to the design of efficient solutions for protection against natural hazards such as shallow landslides and soil erosion. In particular, bioengineering structures can be composed of a living part, made of plants, cuttings or seeds, and an inert part, a timber logs structure. As wood is not treated by preservatives, fungal degradation can occur from the start of the construction. It results in wood strength loss, which practitioners try to evaluate with non-destructive tools (NDT). Classical NDT are mainly based on density measurements. However, the fungal activity reduces the mechanical properties (modulus of elasticity - MOE) well before well before a density change could be measured. In this context, it would be useful to provide a tool for assessing the residual mechanical strength at different decay stages due to a fungal community. Near-infrared spectroscopy (NIRS) can be used for that purpose, as it can allow evaluating wood mechanical properties as well as wood chemical changes due to brown and white rots. We monitored 160 silver fir samples (30x30x6000mm) from green state to different levels of decay. The degradation process took place in a greenhouse and samples were inoculated with silver fir decayed debris in order to accelerate the process. For each sample, we calculated the normalized bending modulus of elasticity loss (Dw moe) and defined it as decay extent. Near infrared spectra collected from both green and decayed ground samples were corrected by the subtraction of baseline offset. Spectra of green samples were averaged into one mean spectrum and decayed spectra were subtracted from the mean spectrum to calculate the absorption loss. Partial least square regression (PLSR) has been performed between the normalized MOE loss Dw moe (0 wood decay extent in the context of ecological engineering structures used for natural hazard mitigation.

  13. Time to address the problems at the neural interface

    Science.gov (United States)

    Durand, Dominique M.; Ghovanloo, Maysam; Krames, Elliot

    2014-04-01

    interface with the CNS. In 2013, two symposia were held independently to discuss this problem: one was held at the International Neuromodulation Society's 11th World Congress in Berlin and supported by the International Neuromodulation Society1 and the other at the 6th International Neural Engineering conference in San Diego2 and was supported by the NSF. Clearly, the neuromodulation and the neural engineering communities are keen to solve this problem. Experts from the field were assembled to discuss the problems and potential solutions. Although many important points were raised, few emerged as key issues. (1) The ability to access remotely and reliably internal neural signals . Although some of the technological problems have already been solved, this ability to access neural signals is still a significant problem since reliable and robust transcutaneous telemetry systems with large numbers of signals, each with wide bandwidth, are not readily available to researchers. (2) A translation strategy taking basic research to the clinic . The lack of understanding of the biological response to implanted constructs and the inability to monitor the sites and match the mechanical properties of the probe to the neural tissue properties continue to be an unsolved problem. In addition, the low levels of collaboration among neuroscientists, clinicians, patients and other stakeholders throughout different phases of research and development were considered to be significant impediments to progress. (3) Fundamental tools development procedures for neural interfacing . There are many laboratories testing various devices with different sets of criteria, but there is no consensus on the failure modes. The reliability, robustness of metrics and testing standards for such devices have not been established, either in academia or in industry. To start addressing this problem, the FDA has established a laboratory to test the reliability of some neural devices. Although the discussion was mostly

  14. Neural Network Based Models for Fusion Applications

    Science.gov (United States)

    Meneghini, Orso; Tema Biwole, Arsene; Luda, Teobaldo; Zywicki, Bailey; Rea, Cristina; Smith, Sterling; Snyder, Phil; Belli, Emily; Staebler, Gary; Canty, Jeff

    2017-10-01

    Whole device modeling, engineering design, experimental planning and control applications demand models that are simultaneously physically accurate and fast. This poster reports on the ongoing effort towards the development and validation of a series of models that leverage neural-­network (NN) multidimensional regression techniques to accelerate some of the most mission critical first principle models for the fusion community, such as: the EPED workflow for prediction of the H-Mode and Super H-Mode pedestal structure the TGLF and NEO models for the prediction of the turbulent and neoclassical particle, energy and momentum fluxes; and the NEO model for the drift-kinetic solution of the bootstrap current. We also applied NNs on DIII-D experimental data for disruption prediction and quantifying the effect of RMPs on the pedestal and ELMs. All of these projects were supported by the infrastructure provided by the OMFIT integrated modeling framework. Work supported by US DOE under DE-SC0012656, DE-FG02-95ER54309, DE-FC02-04ER54698.

  15. Building Community and Fostering Success in STEM Through the Women in Science & Engineering (WiSE) Program at the University of Nevada, Reno

    Science.gov (United States)

    Langus, T. C.; Tempel, R. N.

    2017-12-01

    The Women in Science & Engineering (WiSE) program at the University of Nevada, Reno (UNR) aims to recruit and retain a diverse population of women in STEM fields. During the WiSE Program's 10 years in service, we have primarily functioned as a resource for 364 young women to expand their pre-professional network by building valuable relationships with like-minded women. More recently, we have introduced key changes to better benefit our WiSE scholars, establishing a new residence hall, the Living Learning Community (LLC). The introduction of the LLC, resident assistants, and academic mentors helped to provide support to a diverse culture of women with varying thoughts, values, attitudes, and identities. To evaluate the progress of our program, demographic data was statistically analyzed using SPSS to identify correlations between math preparation, performance in foundational courses, average time to graduation, and retention in STEM majors. Initial programmatic assessment indicates that students participating in WiSE are provided a more well-rounded experience while pursuing higher education. We have maintained a 90% retention rate of females graduating with bachelor's degrees in STEM disciplines (n=187), with many graduates completing advanced masters and doctoral degrees and seamlessly entering into post-graduate internships, professional, and industry careers. The success of the WiSE program is attributed to a focused initiative in fostering supportive classroom environments through common course enrollment, professional development, and engaging women in their community through service learning. As a continued focus, we aim to increase the inclusivity and representation of women at UNR in underrepresented fields such as physics, math, and the geosciences. Further program improvements will be based on ongoing research, including a qualitative approach to explore how providing gender equitable resources influences the persistence of women in STEM.

  16. Novel quantum inspired binary neural network algorithm

    Indian Academy of Sciences (India)

    This parameter is taken as the threshold of neuron for learning of neural network. This algorithm is tested with three benchmark datasets and ... Author Affiliations. OM PRAKASH PATEL1 ARUNA TIWARI. Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore 453552, India ...

  17. Modular representation of layered neural networks.

    Science.gov (United States)

    Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

    2018-01-01

    Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Neural Network Approach to Locating Cryptography in Object Code

    Energy Technology Data Exchange (ETDEWEB)

    Jason L. Wright; Milos Manic

    2009-09-01

    Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.

  19. Qualitative analysis and control of complex neural networks with delays

    CERN Document Server

    Wang, Zhanshan; Zheng, Chengde

    2016-01-01

    This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

  20. Waste Isolation Pilot Plant Materials Interface Interactions Test: Papers presented at the Commission of European Communities workshop on in situ testing of radioactive waste forms and engineered barriers

    International Nuclear Information System (INIS)

    Molecke, M.A.; Sorensen, N.R.

    1993-08-01

    The three papers in this report were presented at the second international workshop to feature the Waste Isolation Pilot Plant (WIPP) Materials Interface Interactions Test (MIIT). This Workshop on In Situ Tests on Radioactive Waste Forms and Engineered Barriers was held in Corsendonk, Belgium, on October 13--16, 1992, and was sponsored by the Commission of the European Communities (CEC). The Studiecentrum voor Kernenergie/Centre D'Energie Nucleaire (SCK/CEN, Belgium), and the US Department of Energy (via Savannah River) also cosponsored this workshop. Workshop participants from Belgium, France, Germany, Sweden, and the United States gathered to discuss the status, results and overviews of the MIIT program. Nine of the twenty-five total workshop papers were presented on the status and results from the WIPP MIIT program after the five-year in situ conclusion of the program. The total number of published MIIT papers is now up to almost forty. Posttest laboratory analyses are still in progress at multiple participating laboratories. The first MIIT paper in this document, by Wicks and Molecke, provides an overview of the entire test program and focuses on the waste form samples. The second paper, by Molecke and Wicks, concentrates on technical details and repository relevant observations on the in situ conduct, sampling, and termination operations of the MIIT. The third paper, by Sorensen and Molecke, presents and summarizes the available laboratory, posttest corrosion data and results for all of the candidate waste container or overpack metal specimens included in the MIIT program

  1. 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

  2. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  3. 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 ...

  4. UNM in the Community.

    Science.gov (United States)

    Quantum: Research & Scholarship, 1998

    1998-01-01

    Profiles 10 University of New Mexico community programs: University Art Museum, Rio Grande and Four Corners Writing Projects, Blacks in the Southwest (exhibit), New Mexico Engineering Research Institute's Environmental Finance Center, Adolescent Social Action Program, Minority Engineering Programs, Rural Community College Initiative, Valencia…

  5. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  6. Design of efficient and safe neural stimulators a multidisciplinary approach

    CERN Document Server

    van Dongen, Marijn

    2016-01-01

    This book discusses the design of neural stimulator systems which are used for the treatment of a wide variety of brain disorders such as Parkinson’s, depression and tinnitus. Whereas many existing books treating neural stimulation focus on one particular design aspect, such as the electrical design of the stimulator, this book uses a multidisciplinary approach: by combining the fields of neuroscience, electrophysiology and electrical engineering a thorough understanding of the complete neural stimulation chain is created (from the stimulation IC down to the neural cell). This multidisciplinary approach enables readers to gain new insights into stimulator design, while context is provided by presenting innovative design examples. Provides a single-source, multidisciplinary reference to the field of neural stimulation, bridging an important knowledge gap among the fields of bioelectricity, neuroscience, neuroengineering and microelectronics;Uses a top-down approach to understanding the neural activation proc...

  7. Educating the humanitarian engineer.

    Science.gov (United States)

    Passino, Kevin M

    2009-12-01

    The creation of new technologies that serve humanity holds the potential to help end global poverty. Unfortunately, relatively little is done in engineering education to support engineers' humanitarian efforts. Here, various strategies are introduced to augment the teaching of engineering ethics with the goal of encouraging engineers to serve as effective volunteers for community service. First, codes of ethics, moral frameworks, and comparative analysis of professional service standards lay the foundation for expectations for voluntary service in the engineering profession. Second, standard coverage of global issues in engineering ethics educates humanitarian engineers about aspects of the community that influence technical design constraints encountered in practice. Sample assignments on volunteerism are provided, including a prototypical design problem that integrates community constraints into a technical design problem in a novel way. Third, it is shown how extracurricular engineering organizations can provide a theory-practice approach to education in volunteerism. Sample completed projects are described for both undergraduates and graduate students. The student organization approach is contrasted with the service-learning approach. Finally, long-term goals for establishing better infrastructure are identified for educating the humanitarian engineer in the university, and supporting life-long activities of humanitarian engineers.

  8. 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.

  9. Neural Representations of Physics Concepts.

    Science.gov (United States)

    Mason, Robert A; Just, Marcel Adam

    2016-06-01

    We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations. Using factor analysis to reduce the number of dimensions of activation, we obtained four physics-related factors that were mapped to sets of voxels. The four factors were interpretable as causal motion visualization, periodicity, algebraic form, and energy flow. The individual concepts were identifiable from their fMRI signatures with a mean rank accuracy of .75 using a machine-learning (multivoxel) classifier. Furthermore, there was commonality in participants' neural representation of physics; a classifier trained on data from all but one participant identified the concepts in the left-out participant (mean accuracy = .71 across all nine participant samples). The findings indicate that abstract scientific concepts acquired in an educational setting evoke activation patterns that are identifiable and common, indicating that science education builds abstract knowledge using inherent, repurposed brain systems. © The Author(s) 2016.

  10. Humanitarian engineering in the engineering curriculum

    Science.gov (United States)

    Vandersteen, Jonathan Daniel James

    There are many opportunities to use engineering skills to improve the conditions for marginalized communities, but our current engineering education praxis does not instruct on how engineering can be a force for human development. In a time of great inequality and exploitation, the desire to work with the impoverished is prevalent, and it has been proposed to adjust the engineering curriculum to include a larger focus on human needs. This proposed curriculum philosophy is called humanitarian engineering. Professional engineers have played an important role in the modern history of power, wealth, economic development, war, and industrialization; they have also contributed to infrastructure, sanitation, and energy sources necessary to meet human need. Engineers are currently at an important point in time when they must look back on their history in order to be more clear about how to move forward. The changing role of the engineer in history puts into context the call for a more balanced, community-centred engineering curriculum. Qualitative, phenomenographic research was conducted in order to understand the need, opportunity, benefits, and limitations of a proposed humanitarian engineering curriculum. The potential role of the engineer in marginalized communities and details regarding what a humanitarian engineering program could look like were also investigated. Thirty-two semi-structured research interviews were conducted in Canada and Ghana in order to collect a pool of understanding before a phenomenographic analysis resulted in five distinct outcome spaces. The data suggests that an effective curriculum design will include teaching technical skills in conjunction with instructing about issues of social justice, social location, cultural awareness, root causes of marginalization, a broader understanding of technology, and unlearning many elements about the role of the engineer and the dominant economic/political ideology. Cross-cultural engineering development

  11. The gerontechnology engineer

    NARCIS (Netherlands)

    Bronswijk, van J.E.M.H.; Brink, M.; Vlies, van der R.D.

    2011-01-01

    Pushing supportive technology to serve an aging society originated from the social sciences. Only about 20 years ago did engineers discover the field and formulated it as gerontechnology. The question arises whether engineers and social scientists have succeeded to form a community of practice with

  12. Engineering Encounters: Engineering Adaptations

    Science.gov (United States)

    Gatling, Anne; Vaughn, Meredith Houle

    2015-01-01

    Engineering is not a subject that has historically been taught in elementary schools, but with the emphasis on engineering in the "Next Generation Science Standards," curricula are being developed to explicitly teach engineering content and design. However, many of the scientific investigations already conducted with students have…

  13. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  14. The Laplacian spectrum of neural networks

    Science.gov (United States)

    de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

    2014-01-01

    The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

  15. Diabetic retinopathy screening using deep neural network.

    Science.gov (United States)

    Ramachandran, Nishanthan; Hong, Sheng Chiong; Sime, Mary J; Wilson, Graham A

    2017-09-07

    There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

  16. Application of neural networks to group technology

    Science.gov (United States)

    Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II

    1991-08-01

    Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.

  17. 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.

  18. Building a Community of Scholars: One University's Story of Students Engaged in Learning Science, Mathematics, and Engineering through a NSF S-STEM Grant--Part II

    Science.gov (United States)

    Kalevitch, Maria; Maurer, Cheryl; Badger, Paul; Holdan, Greg; Sirinterlikci, Arif

    2015-01-01

    The School of Engineering, Mathematics, and Science (SEMS) at Robert Morris University (RMU) was awarded a five-year grant from the National Science Foundation (NSF) to fund scholarships to 21 academically talented but financially challenged students majoring in the disciplines of science, technology, engineering, and mathematics (STEM). Each…

  19. Measuring the utility of the Science, Technology, Engineering, Mathematics (STEM) Academy Measurement Tool in assessing the development of K-8 STEM academies as professional learning communities

    Science.gov (United States)

    Irish, Teresa J.

    The aim of this study was to provide insights addressing national concerns in Science, Technology, Engineering, and Mathematics (STEM) education by examining how a set of six perimeter urban K-12 schools were transformed into STEM-focused professional learning communities (PLC). The concept of a STEM Academy as a STEM-focused PLC emphasizes the development of a STEM culture where professional discourse and teaching are focused on STEM learning. The STEM Academies examined used the STEM Academy Measurement Tool and Rubric (Tool) as a catalyst for discussion and change. This Tool was developed with input from stakeholders and used for school-wide initiatives, teacher professional development and K-12 student engagement to improve STEM teaching and learning. Two primary goals of this study were to assess the levels of awareness and use of the tool by all stakeholders involved in the project and to determine how the Tool assisted in the development and advancement of these schools as STEM PLCs. Data from the STEM Academy Participant Survey was analyzed to determine stakeholders' perceptions of the Tool in terms of (i) how aware stakeholders were of the Tool, (ii) whether they participated in the use of the Tool, (iii) how the characteristics of PLCs were perceived in their schools, and finally (iv) how the awareness of the Tool influenced teachers' perceptions of the presence of PLC characteristics. Findings indicate that school faculty were aware of the Tool on a number of different levels and evidence exists that the use of the Tool assisted in the development of STEM Academies, however impact varied from school to school. Implications of this study suggest that the survey should be used for a longer period of time to gain more in-depth knowledge on teachers' perceptions of the Tool as a catalyst across time. Additional findings indicate that the process for using the Tool should be ongoing and involve the stakeholders to have the greatest impact on school culture

  20. 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...

  1. Trends in Environmental Health Engineering

    Science.gov (United States)

    Rowe, D. R.

    1972-01-01

    Reviews the trends in environmental health engineering and describes programs in environmental engineering technology and the associated environmental engineering courses at Western Kentucky University (four-year program), Wytheville Community College (two-year program), and Rensselaer Polytechnic Institute (four-year program). (PR)

  2. Reflections on Software Engineering Education

    NARCIS (Netherlands)

    van Vliet, H.

    2006-01-01

    In recent years, the software engineering community has focused on organizing its existing knowledge and finding opportunities to transform that knowledge into a university curriculum. SWEBOK (the Guide to the Software Engineering Body of Knowledge) and Software Engineering 2004 are two initiatives

  3. Introduction to neural networks with electric power applications

    International Nuclear Information System (INIS)

    Wildberger, A.M.; Hickok, K.A.

    1990-01-01

    This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

  4. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

  5. Teaching methodology for modeling reference evapotranspiration with artificial neural networks

    OpenAIRE

    Martí, Pau; Pulido Calvo, Inmaculada; Gutiérrez Estrada, Juan Carlos

    2015-01-01

    [EN] Artificial neural networks are a robust alternative to conventional models for estimating different targets in irrigation engineering, among others, reference evapotranspiration, a key variable for estimating crop water requirements. This paper presents a didactic methodology for introducing students in the application of artificial neural networks for reference evapotranspiration estimation using MatLab c . Apart from learning a specific application of this software wi...

  6. Prototype-Incorporated Emotional Neural Network.

    Science.gov (United States)

    Oyedotun, Oyebade K; Khashman, Adnan

    2017-08-15

    Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

  7. Relation Classification via Recurrent Neural Network

    OpenAIRE

    Zhang, Dongxu; Wang, Dong

    2015-01-01

    Deep learning has gained much success in sentence-level relation classification. For example, convolutional neural networks (CNN) have delivered competitive performance without much effort on feature engineering as the conventional pattern-based methods. Thus a lot of works have been produced based on CNN structures. However, a key issue that has not been well addressed by the CNN-based method is the lack of capability to learn temporal features, especially long-distance dependency between no...

  8. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  9. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  10. Engineering Encounters: Reverse Engineering

    Science.gov (United States)

    McGowan, Veronica Cassone; Ventura, Marcia; Bell, Philip

    2017-01-01

    This column presents ideas and techniques to enhance your science teaching. This month's issue shares information on how students' everyday experiences can support science learning through engineering design. In this article, the authors outline a reverse-engineering model of instruction and describe one example of how it looked in our fifth-grade…

  11. Static human face recognition using artificial neural networks

    International Nuclear Information System (INIS)

    Qamar, R.; Shah, S.H.; Javed-ur-Rehman

    2003-01-01

    This paper presents a novel method of human face recognition using digital computers. A digital PC camera is used to take the BMP images of the human faces. An artificial neural network using Back Propagation Algorithm is developed as a recognition engine. The BMP images of the faces serve as the input patterns for this engine. A software 'Face Recognition' has been developed to recognize the human faces for which it is trained. Once the neural network is trained for patterns of the faces, the software is able to detect and recognize them with success rate of about 97%. (author)

  12. 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.

  13. 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

  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. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  16. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-01-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

  17. The Pursuit of Chronically Reliable Neural Interfaces: A Materials Perspective.

    Science.gov (United States)

    Guo, Liang

    2016-01-01

    Brain-computer interfaces represent one of the most astonishing technologies in our era. However, the grand challenge of chronic instability and limited throughput of the electrode-tissue interface has significantly hindered the further development and ultimate deployment of such exciting technologies. A multidisciplinary research workforce has been called upon to respond to this engineering need. In this paper, I briefly review this multidisciplinary pursuit of chronically reliable neural interfaces from a materials perspective by analyzing the problem, abstracting the engineering principles, and summarizing the corresponding engineering strategies. I further draw my future perspectives by extending the proposed engineering principles.

  18. 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...

  19. Engineer Ethics

    International Nuclear Information System (INIS)

    Lee, Dae Sik; Kim, Yeong Pil; Kim, Yeong Jin

    2003-03-01

    This book tells of engineer ethics such as basic understanding of engineer ethics with history of engineering as a occupation, definition of engineering and specialized job and engineering, engineer ethics as professional ethics, general principles of ethics and its limitation, ethical theory and application, technique to solve the ethical problems, responsibility, safety and danger, information engineer ethics, biotechnological ethics like artificial insemination, life reproduction, gene therapy and environmental ethics.

  20. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  1. Neural networks for aircraft control

    Science.gov (United States)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  2. Active Neural Localization

    OpenAIRE

    Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan

    2018-01-01

    Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

  3. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  4. 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

  5. Model Driven Engineering

    Science.gov (United States)

    Gaševic, Dragan; Djuric, Dragan; Devedžic, Vladan

    A relevant initiative from the software engineering community called Model Driven Engineering (MDE) is being developed in parallel with the Semantic Web (Mellor et al. 2003a). The MDE approach to software development suggests that one should first develop a model of the system under study, which is then transformed into the real thing (i.e., an executable software entity). The most important research initiative in this area is the Model Driven Architecture (MDA), which is Model Driven Architecture being developed under the umbrella of the Object Management Group (OMG). This chapter describes the basic concepts of this software engineering effort.

  6. Software engineers and nuclear engineers: teaming up to do testing

    International Nuclear Information System (INIS)

    Kelly, D.; Cote, N.; Shepard, T.

    2007-01-01

    The software engineering community has traditionally paid little attention to the specific needs of engineers and scientists who develop their own software. Recently there has been increased recognition that specific software engineering techniques need to be found for this group of developers. In this case study, a software engineering group teamed with a nuclear engineering group to develop a software testing strategy. This work examines the types of testing that proved to be useful and examines what each discipline brings to the table to improve the quality of the software product. (author)

  7. Do neural nets learn statistical laws behind natural language?

    Directory of Open Access Journals (Sweden)

    Shuntaro Takahashi

    Full Text Available The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.

  8. 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...

  9. Liquefaction Microzonation of Babol City Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

    2012-01-01

    that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... microzonation map is produced for research area. Based on the obtained results, it can be stated that the trained neural network is capable in prediction of liquefaction potential with an acceptable level of confidence. At the end, zoning of the city is carried out based on the prediction of liquefaction...... that can be classified as machine learning. Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. In order to address the collective knowledge built-up in conventional liquefaction engineering, an alternative general regression neural network model...

  10. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

    Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

  11. Engineering Technical Support Center (ETSC)

    Science.gov (United States)

    ETSC is EPA’s technical support and resource centers responsible for providing specialized scientific and engineering support to decision-makers in the Agency’s ten regional offices, states, communities, and local businesses.

  12. Using artificial neural network approach for modelling rainfall–runoff ...

    Indian Academy of Sciences (India)

    Department of Civil Engineering, National Pingtung University of Science and Technology, Neipu Hsiang,. Pingtung ... study, a model for estimating runoff by using rainfall data from a river basin is developed and a neural ... For example, 2009 typhoon Morakot in Pingtung ... Tokar and Markus (2000) applied ANN to predict.

  13. Application of artificial neural networks to improve power transfer ...

    African Journals Online (AJOL)

    Application of artificial neural networks to improve power transfer capability through OLTC. ... International Journal of Engineering, Science and Technology ... Numerical results show that the setting of OLTC transformer in terms of the load model has a major effect on the maximum power transfer in power systems and the ...

  14. Parallel consensual neural networks.

    Science.gov (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

    1997-01-01

    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  15. Neural networks. A new analytical tool, applicable also in nuclear technology

    Energy Technology Data Exchange (ETDEWEB)

    Stritar, A [Inst. Jozef Stefan, Ljubljana (Slovenia)

    1992-07-01

    The basic concept of neural networks and back propagation learning algorithm are described. The behaviour of typical neural network is demonstrated on a simple graphical case. A short literature survey about the application of neural networks in nuclear science and engineering is made. The application of the neural network to the probability density calculation is shown. (author) [Slovenian] Opisana je osnova nevronskih mrez in back propagation nacina njihovega ucenja. Obnasanje enostavne nevronske mreze je prikazano na graficnem primeru. Podan je kratek pregled literaure o uporabi nevronskih mrez v jedrski znanosti in tehnologiji. Prikazana je tudi uporaba nevronske mreze pri izracunu verjetnostne porazdelitve. (author)

  16. Engineering opportunities in nuclear engineering

    International Nuclear Information System (INIS)

    Walton, D.G.

    1980-01-01

    The pattern of education and training of Nuclear Engineers in the UK is outlined under the headings; degree courses for professional engineers, postgraduate courses, education of technician engineers. Universities which offer specific courses are stated and useful addresses listed. (UK)

  17. Discourse Communities and Communities of Practice

    DEFF Research Database (Denmark)

    Pogner, Karl-Heinz

    2005-01-01

    This paper aims at giving a more detailed description and discussion of two concepts of `community' developed in the research areas of text production/ writing and social learning / information management / knowledge sharing and comparing them with each other. The purpose of this theoretical exer...... production at different Danish workplaces (a consulting engi-neering company, a university department and a bank) and discusses their significance in the context of co-located as well as geographically distrib-uted communities....

  18. 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

  19. Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

    Science.gov (United States)

    Kuznetsov, A. V.; Makaryants, G. M.

    2018-01-01

    There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

  20. Sustainable Development in Engineering Education

    Science.gov (United States)

    Taoussanidis, Nikolaos N.; Antoniadou, Myrofora A.

    2006-01-01

    The principles and practice of environmentally and socially sustainable engineering are in line with growing community expectations and the strengthening voice of civil society in engineering interventions. Pressures towards internationalization and globalization are reflected in new course accreditation criteria and higher education structures.…

  1. Civil Engineering Technology Needs Assessment.

    Science.gov (United States)

    Oakland Community Coll., Farmington, MI. Office of Institutional Planning and Analysis.

    In 1991, a study was conducted by Oakland Community College (OCC) to evaluate the need for a proposed Civil Engineering Technology program. An initial examination of the literature focused on industry needs and the job market for civil engineering technicians. In order to gather information on local area employers' hiring practices and needs, a…

  2. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  3. Software and Network Engineering

    CERN Document Server

    2012-01-01

    The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence – quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution - this permits a rapid and broad dissemination of research results.   The purpose of the first ACIS International Symposium on Software and Network Engineering held on Decembe...

  4. Sacred or Neural?

    DEFF Research Database (Denmark)

    Runehov, Anne Leona Cesarine

    Are religious spiritual experiences merely the product of the human nervous system? Anne L.C. Runehov investigates the potential of contemporary neuroscience to explain religious experiences. Following the footsteps of Michael Persinger, Andrew Newberg and Eugene d'Aquili she defines...... the terminological bounderies of "religious experiences" and explores the relevant criteria for the proper evaluation of scientific research, with a particular focus on the validity of reductionist models. Runehov's theis is that the perspectives looked at do not necessarily exclude each other but can be merged....... The question "sacred or neural?" becomes a statement "sacred and neural". The synergies thus produced provide manifold opportunities for interdisciplinary dialogue and research....

  5. Engineering Institute

    Science.gov (United States)

    Projects Past Projects Publications NSEC » Engineering Institute Engineering Institute Multidisciplinary engineering research that integrates advanced modeling and simulations, novel sensing systems and new home of Engineering Institute Contact Institute Director Charles Farrar (505) 665-0860 Email UCSD EI

  6. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  7. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  8. Mechanical engineering

    CERN Document Server

    Darbyshire, Alan

    2010-01-01

    Alan Darbyshire's best-selling text book provides five-star high quality content to a potential audience of 13,000 engineering students. It explains the most popular specialist units of the Mechanical Engineering, Manufacturing Engineering and Operations & Maintenance Engineering pathways of the new 2010 BTEC National Engineering syllabus. This challenging textbook also features contributions from specialist lecturers, ensuring that no stone is left unturned.

  9. 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

  10. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  11. Neural correlates of consciousness

    African Journals Online (AJOL)

    neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

  12. 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.

  13. 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.

  14. 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...

  15. Neural underpinnings of music

    DEFF Research Database (Denmark)

    Vuust, Peter; Gebauer, Line K; Witek, Maria A G

    2014-01-01

    . According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we...

  16. Biomaterials for tissue engineering applications.

    Science.gov (United States)

    Keane, Timothy J; Badylak, Stephen F

    2014-06-01

    With advancements in biological and engineering sciences, the definition of an ideal biomaterial has evolved over the past 50 years from a substance that is inert to one that has select bioinductive properties and integrates well with adjacent host tissue. Biomaterials are a fundamental component of tissue engineering, which aims to replace diseased, damaged, or missing tissue with reconstructed functional tissue. Most biomaterials are less than satisfactory for pediatric patients because the scaffold must adapt to the growth and development of the surrounding tissues and organs over time. The pediatric community, therefore, provides a distinct challenge for the tissue engineering community. Copyright © 2014. Published by Elsevier Inc.

  17. Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network

    NARCIS (Netherlands)

    Park, Y.S.; Verdonschot, P.F.M.; Chon, T.S.; Lek, S.

    2003-01-01

    A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers,

  18. Artificial Intelligence in Civil Engineering

    Directory of Open Access Journals (Sweden)

    Pengzhen Lu

    2012-01-01

    Full Text Available Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

  19. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  20. 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.

  1. Applications of neural networks to mechanics

    International Nuclear Information System (INIS)

    1997-01-01

    Neural networks have become powerful tools in engineer's techniques. The aim of this conference was to present their application to concrete cases in the domain of mechanics, including the preparation and use of materials. Artificial neurons are non-linear organs which provide an output signal that depends on several differently weighted input signals. Their connection into networks allows to solve problems for which the driving laws are not well known. The applications discussed during this conference deal with: the driving of machines or processes, the control of machines, materials or products, the simulation and forecasting, and the optimization. Three papers dealing with the control of spark ignition engines, the regulation of heating floors and the optimization of energy consumptions in industrial buildings were selected for ETDE and one paper dealing with the optimization of the management of a reprocessed plutonium stock was selected for INIS. (J.S.)

  2. Neural network application to diesel generator diagnostics

    International Nuclear Information System (INIS)

    Logan, K.P.

    1990-01-01

    Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

  3. Improved Extension Neural Network and Its Applications

    Directory of Open Access Journals (Sweden)

    Yu Zhou

    2014-01-01

    Full Text Available Extension neural network (ENN is a new neural network that is a combination of extension theory and artificial neural network (ANN. The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data and the borders between clusters (boundary data are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.

  4. Stability and synchronization control of stochastic neural networks

    CERN Document Server

    Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing

    2016-01-01

    This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.

  5. Using Neural Networks to Predict the Hardness of Aluminum Alloys

    Directory of Open Access Journals (Sweden)

    B. Zahran

    2015-02-01

    Full Text Available Aluminum alloys have gained significant industrial importance being involved in many of the light and heavy industries and especially in aerospace engineering. The mechanical properties of aluminum alloys are defined by a number of principal microstructural features. Conventional mathematical models of these properties are sometimes very complex to be analytically calculated. In this paper, a neural network model is used to predict the correlations between the hardness of aluminum alloys in relation to certain alloying elements. A backpropagation neural network is trained using a thorough dataset. The impact of certain elements is documented and an optimum structure is proposed.

  6. Civil Engineering: Improving the Quality of Life.

    Science.gov (United States)

    One Feather, Sandra

    2002-01-01

    American Indian civil engineers describe the educational paths that led them to their engineering careers, applications of civil engineering in reservation communities, necessary job skills, opportunities afforded by internship programs, continuing education, and the importance of early preparation in math and science. Addresses of 12 resource Web…

  7. Engineering Cartilage

    Science.gov (United States)

    ... Research Matters NIH Research Matters March 3, 2014 Engineering Cartilage Artistic rendering of human stem cells on ... situations has been a major goal in tissue engineering. Cartilage contains water, collagen, proteoglycans, and chondrocytes. Collagens ...

  8. Industrial Engineering

    DEFF Research Database (Denmark)

    Karlsson, Christer

    2015-01-01

    Industrial engineering is a discipline that is concerned with increasing the effectiveness of (primarily) manufacturing and (occasionally).......Industrial engineering is a discipline that is concerned with increasing the effectiveness of (primarily) manufacturing and (occasionally)....

  9. Computer Engineers.

    Science.gov (United States)

    Moncarz, Roger

    2000-01-01

    Looks at computer engineers and describes their job, employment outlook, earnings, and training and qualifications. Provides a list of resources related to computer engineering careers and the computer industry. (JOW)

  10. Engineering _ litteraturliste

    DEFF Research Database (Denmark)

    Sillasen, Martin Krabbe; Daugbjerg, Peer; Nielsen, Keld

    2017-01-01

    Litteraturliste udarbejdet som grundlag for artiklen ”Engineering – svaret på naturfagenes udfordringer?”......Litteraturliste udarbejdet som grundlag for artiklen ”Engineering – svaret på naturfagenes udfordringer?”...

  11. Intelligent techniques in engineering management theory and applications

    CERN Document Server

    Onar, Sezi

    2015-01-01

    This book presents recently developed intelligent techniques with applications and theory in the area of engineering management. The involved applications of intelligent techniques such as neural networks, fuzzy sets, Tabu search, genetic algorithms, etc. will be useful for engineering managers, postgraduate students, researchers, and lecturers. The book has been written considering the contents of a classical engineering management book but intelligent techniques are used for handling the engineering management problem areas. This comprehensive characteristics of the book makes it an excellent reference for the solution of complex problems of engineering management. The authors of the chapters are well-known researchers with their previous works in the area of engineering management.

  12. SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

    Science.gov (United States)

    Jimenez-Romero, Cristian; Johnson, Jeffrey

    2017-01-01

    The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

  13. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  14. Deep Neural Yodelling

    OpenAIRE

    Pfäffli, Daniel (Autor/in)

    2018-01-01

    Yodel music differs from most other genres by exercising the transition from chest voice to falsetto with an audible glottal stop which is recognised even by laymen. Yodel often consists of a yodeller with a choir accompaniment. In Switzerland, it is differentiated between the natural yodel and yodel songs. Today's approaches to music generation with machine learning algorithms are based on neural networks, which are best described by stacked layers of neurons which are connected with neurons...

  15. Neural networks for triggering

    International Nuclear Information System (INIS)

    Denby, B.; Campbell, M.; Bedeschi, F.; Chriss, N.; Bowers, C.; Nesti, F.

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab

  16. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  17. Rotation Invariance Neural Network

    OpenAIRE

    Li, Shiyuan

    2017-01-01

    Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using thos...

  18. Neural Mechanisms of Foraging

    OpenAIRE

    Kolling, Nils; Behrens, Timothy EJ; Mars, Rogier B; Rushworth, Matthew FS

    2012-01-01

    Behavioural economic studies, involving limited numbers of choices, have provided key insights into neural decision-making mechanisms. By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey/food. On each encounter the animal chooses to engage or whether the environment is sufficiently rich that searching elsewhere is merited. The cost of foraging is also critical. We demonstrate humans can alternate between two modes of choice, comparative decision-ma...

  19. Computational engineering

    CERN Document Server

    2014-01-01

    The book presents state-of-the-art works in computational engineering. Focus is on mathematical modeling, numerical simulation, experimental validation and visualization in engineering sciences. In particular, the following topics are presented: constitutive models and their implementation into finite element codes, numerical models in nonlinear elasto-dynamics including seismic excitations, multiphase models in structural engineering and multiscale models of materials systems, sensitivity and reliability analysis of engineering structures, the application of scientific computing in urban water management and hydraulic engineering, and the application of genetic algorithms for the registration of laser scanner point clouds.

  20. Neural Based Orthogonal Data Fitting The EXIN Neural Networks

    CERN Document Server

    Cirrincione, Giansalvo

    2008-01-01

    Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

  1. Architectural Engineers

    DEFF Research Database (Denmark)

    Petersen, Rikke Premer

    engineering is addresses from two perspectives – as an educational response and an occupational constellation. Architecture and engineering are two of the traditional design professions and they frequently meet in the occupational setting, but at educational institutions they remain largely estranged....... The paper builds on a multi-sited study of an architectural engineering program at the Technical University of Denmark and an architectural engineering team within an international engineering consultancy based on Denmark. They are both responding to new tendencies within the building industry where...... the role of engineers and architects increasingly overlap during the design process, but their approaches reflect different perceptions of the consequences. The paper discusses some of the challenges that design education, not only within engineering, is facing today: young designers must be equipped...

  2. Tissue engineering

    CERN Document Server

    Fisher, John P; Bronzino, Joseph D

    2007-01-01

    Increasingly viewed as the future of medicine, the field of tissue engineering is still in its infancy. As evidenced in both the scientific and popular press, there exists considerable excitement surrounding the strategy of regenerative medicine. To achieve its highest potential, a series of technological advances must be made. Putting the numerous breakthroughs made in this field into a broad context, Tissue Engineering disseminates current thinking on the development of engineered tissues. Divided into three sections, the book covers the fundamentals of tissue engineering, enabling technologies, and tissue engineering applications. It examines the properties of stem cells, primary cells, growth factors, and extracellular matrix as well as their impact on the development of tissue engineered devices. Contributions focus on those strategies typically incorporated into tissue engineered devices or utilized in their development, including scaffolds, nanocomposites, bioreactors, drug delivery systems, and gene t...

  3. Neural Networks for Modeling and Control of Particle Accelerators

    Science.gov (United States)

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

  4. Vibration analysis in nuclear power plant using neural networks

    International Nuclear Information System (INIS)

    Loskiewicz-Buczak, A.; Alguindigue, I.E.

    1993-01-01

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper documents the authors' work on the design of a vibration monitoring methodology enhanced by neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to handle data which may be distorted or noisy. This paper describes three neural networks-based methods for the automation of some of the activities related to motion and vibration monitoring in engineering systems

  5. Neural Elements for Predictive Coding

    Directory of Open Access Journals (Sweden)

    Stewart SHIPP

    2016-11-01

    possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome’, combining its extrinsic projection (forward, backward or subcortical with evaluation of its intrinsic network (e.g. unidirectional versus bidirectional connections with other pyramidal neurons.

  6. Neural Elements for Predictive Coding.

    Science.gov (United States)

    Shipp, Stewart

    2016-01-01

    Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural

  7. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  8. Integrating rehabilitation engineering technology with biologics.

    Science.gov (United States)

    Collinger, Jennifer L; Dicianno, Brad E; Weber, Douglas J; Cui, Xinyan Tracy; Wang, Wei; Brienza, David M; Boninger, Michael L

    2011-06-01

    Rehabilitation engineers apply engineering principles to improve function or to solve challenges faced by persons with disabilities. It is critical to integrate the knowledge of biologics into the process of rehabilitation engineering to advance the field and maximize potential benefits to patients. Some applications in particular demonstrate the value of a symbiotic relationship between biologics and rehabilitation engineering. In this review we illustrate how researchers working with neural interfaces and integrated prosthetics, assistive technology, and biologics data collection are currently integrating these 2 fields. We also discuss the potential for further integration of biologics and rehabilitation engineering to deliver the best technologies and treatments to patients. Engineers and clinicians must work together to develop technologies that meet clinical needs and are accessible to the intended patient population. Copyright © 2011 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

  9. Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhisheng Zhang

    2016-01-01

    Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

  10. Do we educate engineers that can engineer?

    DEFF Research Database (Denmark)

    Nyborg, Mads; Probst, Christian W.

    2017-01-01

    results, we decided to target students who have at least passed the first four terms, and companies that have hosted a significant number of students in the last 3 years in internships or for the final thesis. These companies interact with the students for almost one year at the end of their studies......, providing a good foundation for the company supervisors to answer questions about the students’ abilities as an engineer. In this paper, we discuss the design and result of the questionnaire, and the obtained results. As mentioned above, the survey will give us and the CDIO community detailed insights...

  11. Engineering mechanics

    CERN Document Server

    Gross, Dietmar; Schröder, Jörg; Wall, Wolfgang A; Rajapakse, Nimal

    Statics is the first volume of a three-volume textbook on Engineering Mechanics. The authors, using a time-honoured straightforward and flexible approach, present the basic concepts and principles of mechanics in the clearest and simplest form possible to advanced undergraduate engineering students of various disciplines and different educational backgrounds. An important objective of this book is to develop problem solving skills in a systematic manner. Another aim of this volume is to provide engineering students as well as practising engineers with a solid foundation to help them bridge the gap between undergraduate studies on the one hand and advanced courses on mechanics and/or practical engineering problems on the other. The book contains numerous examples, along with their complete solutions. Emphasis is placed upon student participation in problem solving. The contents of the book correspond to the topics normally covered in courses on basic engineering mechanics at universities and colleges. Now in i...

  12. Invisible Engineers

    Science.gov (United States)

    Ohashi, Hideo

    Questionnaire to ask “mention three names of scientists you know” and “three names of engineers you know” was conducted and the answers from 140 adults were analyzed. The results indicated that the image of scientists is represented by Nobel laureates and that of engineers by great inventors like Thomas Edison and industry founders like Soichiro Honda. In order to reveal the image of engineers among young generation, questionnaire was conducted for pupils in middle and high schools. Answers from 1,230 pupils were analyzed and 226 names mentioned as engineers were classified. White votes reached 60%. Engineers who are neither big inventors nor company founders collected less than 1% of named votes. Engineers are astonishingly invisible from young generation. Countermeasures are proposed.

  13. Global engineering

    International Nuclear Information System (INIS)

    Plass, L.

    2001-01-01

    This article considers the challenges posed by the declining orders in the plant engineering and contracting business in Germany, the need to remain competitive, and essential preconditions for mastering the challenge. The change in engineering approach is illustrated by the building of a methanol plant in Argentina by Lurgi with the basic engineering completed in Frankfurt with involvement of key personnel from Poland, completely engineered subsystems from a Brazilian subsupplier, and detailed engineering work in Frankfurt. The production of methanol from natural gas using the LurgiMega/Methanol process is used as a typical example of the industrial plant construction sector. The prerequisites for successful global engineering are listed, and error costs in plant construction, possible savings, and process intensification are discussed

  14. The implementation of artificial neural networks to model methane oxidation in landfill soil covers[Includes the CSCE forum on professional practice and career development : 1. international engineering mechanics and materials specialty conference : 1. international/3. coastal, estuarine and offshore engineering specialty conference : 2. international/8. construction specialty conference

    Energy Technology Data Exchange (ETDEWEB)

    Szeto, A.; Albanna, M.; Warith, M. [Ottawa Univ., ON (Canada). Faculty of Civil and Environmental Engineering

    2009-07-01

    The disposal of solid waste significantly contributes to the total anthropogenic emissions of methane (CH{sub 4}), a greenhouse gas that negatively affects climate change. The oxidation of methane in landfill bio-covers takes place through the use of methanotrophic bacteria which provides a sink for methane. The rate at which methane is biologically oxidized depends on several parameters. This study provided a better understanding of the oxidation of methane in landfill soil covers through modeling methane oxidation with artificial neural networks (ANNs). An ANN was trained and tested to model methane oxidation in various batch scale systems for 3 types of soils. Input data consisted of temperature, moisture content, soil composition and the nutrient content added to the system. Model results were in good agreement with experimental results reported by other researchers. It was concluded that the use of ANNs to model methane oxidation in batch scale bio-covers can address the large number of complicated physical and biochemical processes that occur within the landfill bio-cover. 10 refs., 7 tabs., 5 figs.

  15. Bridging the divide between neuroprosthetic design, tissue engineering and neurobiology

    Directory of Open Access Journals (Sweden)

    Jennie Leach

    2010-02-01

    Full Text Available Neuroprosthetic devices have made a major impact in the treatment of a variety of disorders such as paralysis and stroke. However, a major impediment in the advancement of this technology is the challenge of maintaining device performance during chronic implantation (months to years due to complex intrinsic host responses such as gliosis or glial scarring. The objective of this review is to bring together research communities in neurobiology, tissue engineering, and neuroprosthetics to address the major obstacles encountered in the translation of neuroprosthetics technology into long-term clinical use. This article draws connections between specific challenges faced by current neuroprosthetics technology and recent advances in the areas of nerve tissue engineering and neurobiology. Within the context of the device-nervous system interface and central nervous system (CNS implants, areas of synergistic opportunity are discussed, including platforms to present cells with multiple cues, controlled delivery of bioactive factors, three-dimensional constructs and in vitro models of gliosis and brain injury, nerve regeneration strategies, and neural stem/progenitor cell (NPC biology. Finally, recent insights gained from the fields of developmental neurobiology and cancer biology are discussed as examples of exciting new biological knowledge that may provide fresh inspiration towards novel technologies to address the complexities associated with long-term neuroprosthetic device performance.

  16. Modification of surface/neuron interfaces for neural cell-type specific responses: a review

    International Nuclear Information System (INIS)

    Chen, Cen; Kong, Xiangdong; Lee, In-Seop

    2016-01-01

    Surface/neuron interfaces have played an important role in neural repair including neural prostheses and tissue engineered scaffolds. This comprehensive literature review covers recent studies on the modification of surface/neuron interfaces. These interfaces are identified in cases both where the surfaces of substrates or scaffolds were in direct contact with cells and where the surfaces were modified to facilitate cell adhesion and controlling cell-type specific responses. Different sources of cells for neural repair are described, such as pheochromocytoma neuronal-like cell, neural stem cell (NSC), embryonic stem cell (ESC), mesenchymal stem cell (MSC) and induced pluripotent stem cell (iPS). Commonly modified methods are discussed including patterned surfaces at micro- or nano-scale, surface modification with conducting coatings, and functionalized surfaces with immobilized bioactive molecules. These approaches to control cell-type specific responses have enormous potential implications in neural repair. (paper)

  17. Application of neural network technology to setpoint control of a simulated reactor experiment loop

    International Nuclear Information System (INIS)

    Cordes, G.A.; Bryan, S.R.; Powell, R.H.; Chick, D.R.

    1991-01-01

    This paper describes the design, implementation, and application of artificial neural networks to achieve temperature and flow rate control for a simulation of a typical experiment loop in the Advanced Test Reactor (ATR) located at the Idaho National Engineering Laboratory (INEL). The goal of the project was to research multivariate, nonlinear control using neural networks. A loop simulation code was adapted for the project and used to create a training set and test the neural network controller for comparison with the existing loop controllers. The results for the best neural network design are documented and compared with existing loop controller action. The neural network was shown to be as accurate at loop control as the classical controllers in the operating region represented by the training set. 5 refs., 8 figs., 3 tabs

  18. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    OpenAIRE

    Francisco Javier Ordóñez; Daniel Roggen

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we pro...

  19. An Exploratory Application of Neural Networks to the Sortie Generation Forecasting Problem

    Science.gov (United States)

    1991-09-01

    research of Dr. David A. Diener, Major, USAF. As the initial research increment to be improved upon by future researchers, this study (1) provides a... David A. Diener, Major, USAF, who virtually transformed my dream of exploring neural network techniques into concrete reality. His talents in...New York: John Wiley & Sons, 1978. Barron R. L., Gilstrap, L. 0., and Shrier , S. "Polynomial al and Neural Networks: Analogies and Engineering

  20. Human engineering

    International Nuclear Information System (INIS)

    Yang, Seong Hwan; Park, Bum; Gang, Yeong Sik; Gal, Won Mo; Baek, Seung Ryeol; Choe, Jeong Hwa; Kim, Dae Sung

    2006-07-01

    This book mentions human engineering, which deals with introduction of human engineering, Man-Machine system like system design, and analysis and evaluation of Man-Machine system, data processing and data input, display, system control of man, human mistake and reliability, human measurement and design of working place, human working, hand tool and manual material handling, condition of working circumstance, working management, working analysis, motion analysis working measurement, and working improvement and design in human engineering.

  1. Engineering Electromagnetics

    International Nuclear Information System (INIS)

    Kim, Se Yun

    2009-01-01

    This book deals with engineering electromagnetics. It contains seven chapters, which treats understanding of engineering electromagnetics such as magnet and electron spin, current and a magnetic field and an electromagnetic wave, Essential tool for engineering electromagnetics on rector and scalar, rectangular coordinate system and curl vector, electrostatic field with coulomb rule and method of electric images, Biot-Savart law, Ampere law and magnetic force, Maxwell equation and an electromagnetic wave and reflection and penetration of electromagnetic plane wave.

  2. Information engineering

    Energy Technology Data Exchange (ETDEWEB)

    Hunt, D.N.

    1997-02-01

    The Information Engineering thrust area develops information technology to support the programmatic needs of Lawrence Livermore National Laboratory`s Engineering Directorate. Progress in five programmatic areas are described in separate reports contained herein. These are entitled Three-dimensional Object Creation, Manipulation, and Transport, Zephyr:A Secure Internet-Based Process to Streamline Engineering Procurements, Subcarrier Multiplexing: Optical Network Demonstrations, Parallel Optical Interconnect Technology Demonstration, and Intelligent Automation Architecture.

  3. Software engineering

    CERN Document Server

    Sommerville, Ian

    2010-01-01

    The ninth edition of Software Engineering presents a broad perspective of software engineering, focusing on the processes and techniques fundamental to the creation of reliable, software systems. Increased coverage of agile methods and software reuse, along with coverage of 'traditional' plan-driven software engineering, gives readers the most up-to-date view of the field currently available. Practical case studies, a full set of easy-to-access supplements, and extensive web resources make teaching the course easier than ever.

  4. Biological Systems Thinking for Control Engineering Design

    Directory of Open Access Journals (Sweden)

    D. J. Murray-Smith

    2004-01-01

    Full Text Available Artificial neural networks and genetic algorithms are often quoted in discussions about the contribution of biological systems thinking to engineering design. This paper reviews work on the neuromuscular system, a field in which biological systems thinking could make specific contributions to the development and design of automatic control systems for mechatronics and robotics applications. The paper suggests some specific areas in which a better understanding of this biological control system could be expected to contribute to control engineering design methods in the future. Particular emphasis is given to the nonlinear nature of elements within the neuromuscular system and to processes of neural signal processing, sensing and system adaptivity. Aspects of the biological system that are of particular significance for engineering control systems include sensor fusion, sensor redundancy and parallelism, together with advanced forms of signal processing for adaptive and learning control. 

  5. Mechanical Engineering | Classification | College of Engineering & Applied

    Science.gov (United States)

    Engineering Concentration on Ergonomics M.S. Program in Computer Science Interdisciplinary Concentration on Energy Doctoral Programs in Engineering Non-Degree Candidate Departments Biomedical Engineering Biomedical Engineering Industry Advisory Council Civil & Environmental Engineering Civil &

  6. Biomedical Engineering | Classification | College of Engineering & Applied

    Science.gov (United States)

    Engineering Concentration on Ergonomics M.S. Program in Computer Science Interdisciplinary Concentration on Energy Doctoral Programs in Engineering Non-Degree Candidate Departments Biomedical Engineering Biomedical Engineering Industry Advisory Council Civil & Environmental Engineering Civil &

  7. Materials Science & Engineering | Classification | College of Engineering &

    Science.gov (United States)

    Biomedical Engineering Concentration on Ergonomics M.S. Program in Computer Science Interdisciplinary Concentration on Energy Doctoral Programs in Engineering Non-Degree Candidate Departments Biomedical Engineering Biomedical Engineering Industry Advisory Council Civil & Environmental Engineering Civil &

  8. Electrical Engineering | Classification | College of Engineering & Applied

    Science.gov (United States)

    Engineering Concentration on Ergonomics M.S. Program in Computer Science Interdisciplinary Concentration on Energy Doctoral Programs in Engineering Non-Degree Candidate Departments Biomedical Engineering Biomedical Engineering Industry Advisory Council Civil & Environmental Engineering Civil &

  9. Engineering tribology

    CERN Document Server

    Stachowiak, Gwidon; Batchelor, A W; Batchelor, Andrew W

    2005-01-01

    As with the previous edition, the third edition of Engineering Tribology provides a thorough understanding of friction and wear using technologies such as lubrication and special materials. Tribology is a complex topic with its own terminology and specialized concepts, yet is vitally important throughout all engineering disciplines, including mechanical design, aerodynamics, fluid dynamics and biomedical engineering. This edition includes updated material on the hydrodynamic aspects of tribology as well as new advances in the field of biotribology, with a focus throughout on the engineering ap

  10. Unloading arm movement modeling using neural networks for a rotary hearth furnace

    Directory of Open Access Journals (Sweden)

    Iulia Inoan

    2011-12-01

    Full Text Available Neural networks are being applied in many fields of engineering having nowadays a wide range of application. Neural networks are very useful for modeling dynamic processes for which the mathematical modeling is hard to obtain, or for processes that can’t be modeled using mathematical equations. This paper describes the modeling process for the unloading arm movement from a rotary hearth furnace using neural networks with back propagation algorithm. In this case the designed network was trained using the simulation results from a previous calculated mathematical model.

  11. Prediction of welding shrinkage deformation of bridge steel box girder based on wavelet neural network

    Science.gov (United States)

    Tao, Yulong; Miao, Yunshui; Han, Jiaqi; Yan, Feiyun

    2018-05-01

    Aiming at the low accuracy of traditional forecasting methods such as linear regression method, this paper presents a prediction method for predicting the relationship between bridge steel box girder and its displacement with wavelet neural network. Compared with traditional forecasting methods, this scheme has better local characteristics and learning ability, which greatly improves the prediction ability of deformation. Through analysis of the instance and found that after compared with the traditional prediction method based on wavelet neural network, the rigid beam deformation prediction accuracy is higher, and is superior to the BP neural network prediction results, conform to the actual demand of engineering design.

  12. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  13. Neural Synchronization and Cryptography

    Science.gov (United States)

    Ruttor, Andreas

    2007-11-01

    Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.

  14. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  15. Neural networks at the Tevatron

    International Nuclear Information System (INIS)

    Badgett, W.; Burkett, K.; Campbell, M.K.; Wu, D.Y.; Bianchin, S.; DeNardi, M.; Pauletta, G.; Santi, L.; Caner, A.; Denby, B.; Haggerty, H.; Lindsey, C.S.; Wainer, N.; Dall'Agata, M.; Johns, K.; Dickson, M.; Stanco, L.; Wyss, J.L.

    1992-10-01

    This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF

  16. Neural Networks for the Beginner.

    Science.gov (United States)

    Snyder, Robin M.

    Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

  17. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  18. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Abdul Aziz Mohamed

    2001-01-01

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  19. Food Engineering

    NARCIS (Netherlands)

    Boom, R.M.; Janssen, A.E.M.

    2014-01-01

    Food engineering is a rapidly changing discipline. Traditionally, the main focus was on food preservation and stabilization, whereas trends now are on diversity, health, taste, and sustainable production. Next to a general introduction of the definition of food engineering, this article gives a

  20. Biomedical Engineering

    CERN Document Server

    Suh, Sang C; Tanik, Murat M

    2011-01-01

    Biomedical Engineering: Health Care Systems, Technology and Techniques is an edited volume with contributions from world experts. It provides readers with unique contributions related to current research and future healthcare systems. Practitioners and researchers focused on computer science, bioinformatics, engineering and medicine will find this book a valuable reference.

  1. Genetic Engineering

    Science.gov (United States)

    Phillips, John

    1973-01-01

    Presents a review of genetic engineering, in which the genotypes of plants and animals (including human genotypes) may be manipulated for the benefit of the human species. Discusses associated problems and solutions and provides an extensive bibliography of literature relating to genetic engineering. (JR)

  2. Corrosion Engineering.

    Science.gov (United States)

    White, Charles V.

    A description is provided for a Corrosion and Corrosion Control course offered in the Continuing Engineering Education Program at the General Motors Institute (GMI). GMI is a small cooperative engineering school of approximately 2,000 students who alternate between six-week periods of academic study and six weeks of related work experience in…

  3. Engineering surveying

    CERN Document Server

    Schofield, W

    2001-01-01

    The aim of Engineering Surveying has always been to impart and develop a clear understanding of the basic topics of the subject. The author has fully revised the book to make it the most up-to-date and relevant textbook available on the subject.The book also contains the latest information on trigonometric levelling, total stations and one-person measuring systems. A new chapter on satellites ensures a firm grasp of this vitally important topic.The text covers engineering surveying modules for civil engineering students on degree courses and forms a reference for the engineering surveying module in land surveying courses. It will also prove to be a valuable reference for practitioners.* Simple clear introduction to surveying for engineers* Explains key techniques and methods* Details reading systems and satellite position fixing

  4. Emotional engineering

    CERN Document Server

    In an age of increasing complexity, diversification and change, customers expect services that cater to their needs and to their tastes. Emotional Engineering vol 2. describes how their expectations can be satisfied and managed throughout the product life cycle, if producers focus their attention more on emotion. Emotional engineering provides the means to integrate products to create a new social framework and develops services beyond product realization to create of value across a full lifetime.  14 chapters cover a wide range of topics that can be applied to product, process and industry development, with special attention paid to the increasing importance of sensing in the age of extensive and frequent changes, including: • Multisensory stimulation and user experience  • Physiological measurement • Tactile sensation • Emotional quality management • Mental model • Kansei engineering.   Emotional Engineering vol 2 builds on Dr Fukuda’s previous book, Emotional Engineering, and provides read...

  5. Knowledge synthesis with maps of neural connectivity.

    Science.gov (United States)

    Tallis, Marcelo; Thompson, Richard; Russ, Thomas A; Burns, Gully A P C

    2011-01-01

    This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called "Knowledge Engineering from Experimental Design" (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

  6. Knowledge synthesis with maps of neural connectivity

    Directory of Open Access Journals (Sweden)

    Marcelo eTallis

    2011-11-01

    Full Text Available This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets.

  7. Computational methods in earthquake engineering

    CERN Document Server

    Plevris, Vagelis; Lagaros, Nikos

    2017-01-01

    This is the third book in a series on Computational Methods in Earthquake Engineering. The purpose of this volume is to bring together the scientific communities of Computational Mechanics and Structural Dynamics, offering a wide coverage of timely issues on contemporary Earthquake Engineering. This volume will facilitate the exchange of ideas in topics of mutual interest and can serve as a platform for establishing links between research groups with complementary activities. The computational aspects are emphasized in order to address difficult engineering problems of great social and economic importance. .

  8. Program For Engineering Electrical Connections

    Science.gov (United States)

    Billitti, Joseph W.

    1990-01-01

    DFACS is interactive multiuser computer-aided-engineering software tool for system-level electrical integration and cabling engineering. Purpose of program to provide engineering community with centralized data base for putting in and gaining access to data on functional definition of system, details of end-circuit pinouts in systems and subsystems, and data on wiring harnesses. Objective, to provide instantaneous single point of interchange of information, thus avoiding error-prone, time-consuming, and costly shuttling of data along multiple paths. Designed to operate on DEC VAX mini or micro computer using Version 5.0/03 of INGRES.

  9. Identifying Jets Using Artifical Neural Networks

    Science.gov (United States)

    Rosand, Benjamin; Caines, Helen; Checa, Sofia

    2017-09-01

    We investigate particle jet interactions with the Quark Gluon Plasma (QGP) using artificial neural networks modeled on those used in computer image recognition. We create jet images by binning jet particles into pixels and preprocessing every image. We analyzed the jets with a Multi-layered maxout network and a convolutional network. We demonstrate each network's effectiveness in differentiating simulated quenched jets from unquenched jets, and we investigate the method that the network uses to discriminate among different quenched jet simulations. Finally, we develop a greater understanding of the physics behind quenched jets by investigating what the network learnt as well as its effectiveness in differentiating samples. Yale College Freshman Summer Research Fellowship in the Sciences and Engineering.

  10. Biomaterial applications in neural therapy and repair

    Institute of Scientific and Technical Information of China (English)

    Harmanvir Ghuman; Michel Modo

    2017-01-01

    The use of biomaterials,such as hydrogels,as a scaffold to deliver cells and drugs is becoming increasingly common to treat neurological conditions,including stroke.With a limited intrinsic ability to regenerate after injury,innovative tissue engineering strategies have shown the potential of biomaterials in facilitating neural tissue regeneration and functional recovery.Using biomaterials can not only promote the survival and integration of transplanted cells in the existing circuitry,but also support controlled site specific delivery of therapeutic drugs.This review aims to provide the reader an understanding of the brain tissue microenvironment after injury,biomaterial criteria that support tissue repair,commonly used natural and synthetic biomaterials,benefits of incorporating cells and neurotrophic factors,as well as the potential of endogenous neurogenesis in repairing the injured brain.

  11. Statistical physics, neural networks, brain studies

    International Nuclear Information System (INIS)

    Toulouse, G.

    1999-01-01

    An overview of some aspects of a vast domain, located at the crossroads of physics, biology and computer science is presented: (1) During the last fifteen years, physicists advancing along various pathways have come into contact with biology (computational neurosciences) and engineering (formal neural nets). (2) This move may actually be viewed as one component in a larger picture. A prominent trend of recent years, observable over many countries, has been the establishment of interdisciplinary centers devoted to the study of: cognitive sciences; natural and artificial intelligence; brain, mind and behaviour; perception and action; learning and memory; robotics; man-machine communication, etc. What are the promising lines of development? What opportunities for physicists? An attempt will be made to address such questions and related issues

  12. Interacting neural networks

    Science.gov (United States)

    Metzler, R.; Kinzel, W.; Kanter, I.

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

  13. Neural circuitry and immunity

    Science.gov (United States)

    Pavlov, Valentin A.; Tracey, Kevin J.

    2015-01-01

    Research during the last decade has significantly advanced our understanding of the molecular mechanisms at the interface between the nervous system and the immune system. Insight into bidirectional neuroimmune communication has characterized the nervous system as an important partner of the immune system in the regulation of inflammation. Neuronal pathways, including the vagus nerve-based inflammatory reflex are physiological regulators of immune function and inflammation. In parallel, neuronal function is altered in conditions characterized by immune dysregulation and inflammation. Here, we review these regulatory mechanisms and describe the neural circuitry modulating immunity. Understanding these mechanisms reveals possibilities to use targeted neuromodulation as a therapeutic approach for inflammatory and autoimmune disorders. These findings and current clinical exploration of neuromodulation in the treatment of inflammatory diseases defines the emerging field of Bioelectronic Medicine. PMID:26512000

  14. Neural Darwinism and consciousness.

    Science.gov (United States)

    Seth, Anil K; Baars, Bernard J

    2005-03-01

    Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.

  15. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

    OpenAIRE

    Sharma, Neha; Om, Hari

    2015-01-01

    In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural...

  16. Glycosylation Engineering

    DEFF Research Database (Denmark)

    Clausen, Henrik; Wandall, Hans H.; Steentoft, Catharina

    2017-01-01

    Knowledge of the cellular pathways of glycosylation across phylogeny provides opportunities for designing glycans via genetic engineering in a wide variety of cell types including bacteria, fungi, plant cells, and mammalian cells. The commercial demand for glycosylation engineering is broad......, including production of biological therapeutics with defined glycosylation (Chapter 57). This chapter describes how knowledge of glycan structures and their metabolism (Parts I–III of this book) has led to the current state of glycosylation engineering in different cell types. Perspectives for rapid...

  17. Engineering mathematics

    CERN Document Server

    Bird, John

    2014-01-01

    A practical introduction to the core mathematics required for engineering study and practiceNow in its seventh edition, Engineering Mathematics is an established textbook that has helped thousands of students to succeed in their exams.John Bird's approach is based on worked examples and interactive problems. This makes it ideal for students from a wide range of academic backgrounds as the student can work through the material at their own pace. Mathematical theories are explained in a straightforward manner, being supported by practical engineering examples and applications in order to ensure

  18. Engineering mathematics

    CERN Document Server

    Stroud, K A

    2013-01-01

    A groundbreaking and comprehensive reference that's been a bestseller since it first debuted in 1970, the new seventh edition of Engineering Mathematics has been thoroughly revised and expanded. Providing a broad mathematical survey, this innovative volume covers a full range of topics from the very basic to the advanced. Whether you're an engineer looking for a useful on-the-job reference or want to improve your mathematical skills, or you are a student who needs an in-depth self-study guide, Engineering Mathematics is sure to come in handy time and time again.

  19. Studying the glial cell response to biomaterials and surface topography for improving the neural electrode interface

    Science.gov (United States)

    Ereifej, Evon S.

    . Confocal and SEM imaging illustrated cells from the brain tissue slices were aligned along the nanopattern on the PDMS pins. Decreases in several inflammatory markers (GFAP, TNFα, IL-1beta) determined from gene expression analysis, was shown with the nanopatterned PDMS pins. Results of this study confirm nanopatterning not only influences cell morphology, but alters molecular cascades within the cells as well. The results of these studies provide essential information for the neural electrode research community. There is a lack of information available in the scientific community on acceptable and effective materials for neural electrode fabrication. The results of the presented studies provide more information which could lead to classifying guidelines to create biocompatible neural electrode materials. This research project was partially supported by the Wayne State University President's Translational Enhancement Award and by the Kales Scholarship for Biomedical Engineering students.

  20. Sandia National Laboratories: Research: Research Foundations: Engineering

    Science.gov (United States)

    Foundations Bioscience Computing & Information Science Electromagnetics Engineering Science Geoscience Mexico Small Business Assistance Program Sandia Science & Technology Park Careers Community Library Events Careers View All Jobs Students & Postdocs Internships & Co-ops Fellowships

  1. The Fourth Revolution: Educating Engineers for Leadership.

    Science.gov (United States)

    Mark, Hans; Carver, Larry

    1988-01-01

    Urges a change in engineering education for developing leaders. Describes three previous revolutions in American higher education which responded to the needs of the community. Suggests lifelong education as the fourth revolution. (YP)

  2. VINE: A Variational Inference -Based Bayesian Neural Network Engine

    Science.gov (United States)

    2018-01-01

    word to be written back to the seed RAM. The results generated by every four LF-updaters are selected sequentially by 4 outputs with different...suitable for hardware implementation. Sequential mini-batch gradient descent (SMBGD) optimization is an update rule we propose to integrate with EASI...the layer computation block can perform forward propagation from at most eight input neurons to at most eight output neurons simultaneously . If a

  3. Animal models for studying neural crest development: is the mouse different?

    Science.gov (United States)

    Barriga, Elias H; Trainor, Paul A; Bronner, Marianne; Mayor, Roberto

    2015-05-01

    The neural crest is a uniquely vertebrate cell type and has been well studied in a number of model systems. Zebrafish, Xenopus and chick embryos largely show consistent requirements for specific genes in early steps of neural crest development. By contrast, knockouts of homologous genes in the mouse often do not exhibit comparable early neural crest phenotypes. In this Spotlight article, we discuss these species-specific differences, suggest possible explanations for the divergent phenotypes in mouse and urge the community to consider these issues and the need for further research in complementary systems. © 2015. Published by The Company of Biologists Ltd.

  4. Forecasting the mortality rates of Indonesian population by using neural network

    Science.gov (United States)

    Safitri, Lutfiani; Mardiyati, Sri; Rahim, Hendrisman

    2018-03-01

    A model that can represent a problem is required in conducting a forecasting. One of the models that has been acknowledged by the actuary community in forecasting mortality rate is the Lee-Certer model. Lee Carter model supported by Neural Network will be used to calculate mortality forecasting in Indonesia. The type of Neural Network used is feedforward neural network aligned with backpropagation algorithm in python programming language. And the final result of this study is mortality rate in forecasting Indonesia for the next few years

  5. Program Helps Simulate Neural Networks

    Science.gov (United States)

    Villarreal, James; Mcintire, Gary

    1993-01-01

    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  6. 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

  7. Cooperating attackers in neural cryptography.

    Science.gov (United States)

    Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

    2004-06-01

    A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

  8. Software engineering

    CERN Document Server

    Sommerville, Ian

    2016-01-01

    For courses in computer science and software engineering The Fundamental Practice of Software Engineering Software Engineering introduces readers to the overwhelmingly important subject of software programming and development. In the past few years, computer systems have come to dominate not just our technological growth, but the foundations of our world's major industries. This text seeks to lay out the fundamental concepts of this huge and continually growing subject area in a clear and comprehensive manner. The Tenth Edition contains new information that highlights various technological updates of recent years, providing readers with highly relevant and current information. Sommerville's experience in system dependability and systems engineering guides the text through a traditional plan-based approach that incorporates some novel agile methods. The text strives to teach the innovators of tomorrow how to create software that will make our world a better, safer, and more advanced place to live.

  9. Harmonic engine

    Science.gov (United States)

    Bennett, Charles L [Livermore, CA

    2009-10-20

    A high efficiency harmonic engine based on a resonantly reciprocating piston expander that extracts work from heat and pressurizes working fluid in a reciprocating piston compressor. The engine preferably includes harmonic oscillator valves capable of oscillating at a resonant frequency for controlling the flow of working fluid into and out of the expander, and also preferably includes a shunt line connecting an expansion chamber of the expander to a buffer chamber of the expander for minimizing pressure variations in the fluidic circuit of the engine. The engine is especially designed to operate with very high temperature input to the expander and very low temperature input to the compressor, to produce very high thermal conversion efficiency.

  10. Engineering personnel

    International Nuclear Information System (INIS)

    Paskievici, W.

    The expansion of nuclear power is taxing human, material, and capital resources in developed and developing countries. This paper explores the human resources as represented by employment, graduation statistics, and educational curricula for nuclear engineers. (E.C.B.)

  11. Green Engineering

    Science.gov (United States)

    Green Engineering is the design, commercialization and use of processes and products that are feasible and economical while reducing the generation of pollution at the source and minimizing the risk to human health and the environment.

  12. Coastal Engineering

    NARCIS (Netherlands)

    Van der Velden, E.T.J.M.

    1989-01-01

    Introduction, waves, sediment transport, littoral transport, lonshore sediment transport, onshore-offshore sediment transport, coastal changes, dune erosion and storm surges, sedimentation in channels and trenches, coastal engineering in practice.

  13. 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.

  14. Geoenvironmental engineering

    International Nuclear Information System (INIS)

    Shin, Eun Cheol; Park, Jeong Jun

    2009-08-01

    This book deals with definition of soil and scope of clean-up of soil, trend of geoenvironmental engineering at home and foreign countries, main concern of geoenvironmental engineering in domestic and abroad, design and building of landfills such as summary, trend of landfill policy in Korea, post management of landfill facilities, stabilizing and stability of landfill, research method and soil pollution source, restoration technology of soil pollution like restoration technique of oil pollution with thermal processing.

  15. Microwave engineering

    CERN Document Server

    Pozar, David M

    2012-01-01

    The 4th edition of this classic text provides a thorough coverage of RF and microwave engineering concepts, starting from fundamental principles of electrical engineering, with applications to microwave circuits and devices of practical importance.  Coverage includes microwave network analysis, impedance matching, directional couplers and hybrids, microwave filters, ferrite devices, noise, nonlinear effects, and the design of microwave oscillators, amplifiers, and mixers. Material on microwave and RF systems includes wireless communications, radar, radiometry, and radiation hazards. A large

  16. Reliability Engineering

    International Nuclear Information System (INIS)

    Lee, Sang Yong

    1992-07-01

    This book is about reliability engineering, which describes definition and importance of reliability, development of reliability engineering, failure rate and failure probability density function about types of it, CFR and index distribution, IFR and normal distribution and Weibull distribution, maintainability and movability, reliability test and reliability assumption in index distribution type, normal distribution type and Weibull distribution type, reliability sampling test, reliability of system, design of reliability and functionality failure analysis by FTA.

  17. Systems Engineering

    OpenAIRE

    Vaughan, William W.

    2016-01-01

    The term “systems engineering” when entered into the Google search page, produces a significant number of results, evidence that systems engineering is recognized as being important for the success of essentially all products. Since most readers of this item will be rather well versed in documents concerning systems engineering, I have elected to share some of the points made on this subject in a document developed by the European Cooperation for Space Standardization (ECSS), a component of t...

  18. International Conference on Medical and Biological Engineering 2017

    CERN Document Server

    2017-01-01

    This volume presents the proceedings of the International Conference on Medical and Biological Engineering held from 16 to 18 March 2017 in Sarajevo, Bosnia and Herzegovina. Focusing on the theme of ‘Pursuing innovation. Shaping the future’, it highlights the latest advancements in Biomedical Engineering and also presents the latest findings, innovative solutions and emerging challenges in this field. Topics include: - Biomedical Signal Processing - Biomedical Imaging and Image Processing - Biosensors and Bioinstrumentation - Bio-Micro/Nano Technologies - Biomaterials - Biomechanics, Robotics and Minimally Invasive Surgery - Cardiovascular, Respiratory and Endocrine Systems Engineering - Neural and Rehabilitation Engineering - Molecular, Cellular and Tissue Engineering - Bioinformatics and Computational Biology - Clinical Engineering and Health Technology Assessment - Health Informatics, E-Health and Telemedicine - Biomedical Engineering Education - Pharmaceutical Engineering.

  19. Neutrons and Nuclear Engineering

    International Nuclear Information System (INIS)

    Ekkebus, Allen E.

    2007-01-01

    Oak Ridge National Laboratory hosted two workshops in April 2007 relevant to nuclear engineering education. In the Neutron Stress, Texture, and Phase Transformation for Industry workshop (http://neutrons.ornl.gov/workshops/nst2/), several invited speakers gave examples of neutron stress mapping for nuclear engineering applications. These included John Root of National Research Council of Canada, Mike Fitzpatrick of the UK's Open University, and Yan Gao of GE Global Research on their experiences with industrial and academic uses of neutron diffraction. Xun-Li Wang and Camden Hubbard described the new instruments at ORNL that can be used for such studies. This was preceded by the Neutrons for Materials Science and Engineering educational symposium (http://neutrons.ornl.gov/workshops/edsym2007). It was directed to the broad materials science and engineering community based in universities, industry and laboratories who wish to learn what the neutron sources in the US can provide for enhancing the understanding of materials behavior, processing and joining. Of particular interest was the presentation of Donald Brown of Los Alamos about using 'Neutron diffraction measurements of strain and texture to study mechanical behavior of structural materials.' At both workshops, the ORNL neutron scattering instruments relevant to nuclear engineering studies were described. The Neutron Residual Stress Mapping Facility (NRSF2) is currently in operation at the High Flux Isotope Reactor; the VULCAN Engineering Materials Diffractometer will begin commissioning in 2008 at the Spallation Neutron Source. For characteristics of these instruments, as well as details of other workshops, meetings, capabilities, and research proposal submissions, please visit http://neutrons.ornl.gov. To submit user proposals for time on NRSF2 contact Hubbard at hubbardcratornl.gov

  20. Neural components of altruistic punishment

    Directory of Open Access Journals (Sweden)

    Emily eDu

    2015-02-01

    Full Text Available Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

  1. Neural complexity, dissociation, and schizophrenia

    Czech Academy of Sciences Publication Activity Database

    Bob, P.; Šusta, M.; Chládek, Jan; Glaslová, K.; Fedor-Ferybergh, P.

    2007-01-01

    Roč. 13, č. 10 (2007), HY1-5 ISSN 1234-1010 Institutional research plan: CEZ:AV0Z20650511 Keywords : neural complexity * dissociation * schizophrenia Subject RIV: FH - Neurology Impact factor: 1.607, year: 2007

  2. Artificial intelligence: Deep neural reasoning

    Science.gov (United States)

    Jaeger, Herbert

    2016-10-01

    The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

  3. Optical Neural Network Classifier Architectures

    National Research Council Canada - National Science Library

    Getbehead, Mark

    1998-01-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and classification of high-dimensional data for Air...

  4. 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)

  5. A convolutional neural network neutrino event classifier

    International Nuclear Information System (INIS)

    Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.

    2016-01-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  6. Sequential neural models with stochastic layers

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

    2016-01-01

    How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

  7. Gasoline Engine Mechanics. Florida Vocational Program Guide.

    Science.gov (United States)

    University of South Florida, Tampa. Dept. of Adult and Vocational Education.

    This vocational program guide is intended to assist in the organization, operation, and evaluation of a program in gasoline engine mechanics in school districts, area vocational centers, and community colleges. The following topics are covered: job duties of small-engine mechanics; program content (curriculum framework and student performance…

  8. A performance study of grid workflow engines

    NARCIS (Netherlands)

    Stratan, C.; Iosup, A.; Epema, D.H.J.

    2008-01-01

    To benefit from grids, scientists require grid workflow engines that automatically manage the execution of inter-related jobs on the grid infrastructure. So far, the workflows community has focused on scheduling algorithms and on interface tools. Thus, while several grid workflow engines have been

  9. Hire a Milwaukee Engineer | College of Engineering & Applied Science

    Science.gov (United States)

    Electrical Engineering Instructional Laboratories Student Resources Industrial & Manufacturing Engineering Industrial & Manufacturing Engineering Academic Programs Industrial & Manufacturing Engineering Major Industrial & Manufacturing Engineering Minor Industrial & Manufacturing Engineering

  10. 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

  11. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  12. JPL Contamination Control Engineering

    Science.gov (United States)

    Blakkolb, Brian

    2013-01-01

    JPL has extensive expertise fielding contamination sensitive missions-in house and with our NASA/industry/academic partners.t Development and implementation of performance-driven cleanliness requirements for a wide range missions and payloads - UV-Vis-IR: GALEX, Dawn, Juno, WFPC-II, AIRS, TES, et al - Propulsion, thermal control, robotic sample acquisition systems. Contamination control engineering across the mission life cycle: - System and payload requirements derivation, analysis, and contamination control implementation plans - Hardware Design, Risk trades, Requirements V-V - Assembly, Integration & Test planning and implementation - Launch site operations and launch vehicle/payload integration - Flight ops center dot Personnel on staff have expertise with space materials development and flight experiments. JPL has capabilities and expertise to successfully address contamination issues presented by space and habitable environments. JPL has extensive experience fielding and managing contamination sensitive missions. Excellent working relationship with the aerospace contamination control engineering community/.

  13. Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

    International Nuclear Information System (INIS)

    Choobbasti, A J; Farrokhzad, F; Barari, A

    2009-01-01

    Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model's validity. (author)

  14. Engineering ethics challenges and opportunities

    CERN Document Server

    Bowen, W Richard

    2014-01-01

    Engineering Ethics: Challenges and Opportunities aims to set a new agenda for the engineering profession by developing a key challenge: can the great technical innovation of engineering be matched by a corresponding innovation in the acceptance and expression of ethical responsibility?  Central features of this stimulating text include:   ·         An analysis of engineering as a technical and ethical practice providing great opportunities for promoting the wellbeing and agency of individuals and communities. ·         Elucidation of the ethical opportunities of engineering in three key areas:             - Engineering for Peace, emphasising practical amelioration of the root causes of    conflict rather than military solutions.             - Engineering for Health, focusing on close collaboration with healthcare professionals      for both the promotion and restoration of health.             - Engineering for Development, providing effective solution...

  15. Using Genetically Engineered Animal Models in the Postgenomic Era to Understand Gene Function in Alcoholism

    Science.gov (United States)

    Reilly, Matthew T.; Harris, R. Adron; Noronha, Antonio

    2012-01-01

    Over the last 50 years, researchers have made substantial progress in identifying genetic variations that underlie the complex phenotype of alcoholism. Not much is known, however, about how this genetic variation translates into altered biological function. Genetic animal models recapitulating specific characteristics of the human condition have helped elucidate gene function and the genetic basis of disease. In particular, major advances have come from the ability to manipulate genes through a variety of genetic technologies that provide an unprecedented capacity to determine gene function in the living organism and in alcohol-related behaviors. Even newer genetic-engineering technologies have given researchers the ability to control when and where a specific gene or mutation is activated or deleted, allowing investigators to narrow the role of the gene’s function to circumscribed neural pathways and across development. These technologies are important for all areas of neuroscience, and several public and private initiatives are making a new generation of genetic-engineering tools available to the scientific community at large. Finally, high-throughput “next-generation sequencing” technologies are set to rapidly increase knowledge of the genome, epigenome, and transcriptome, which, combined with genetically engineered mouse mutants, will enhance insight into biological function. All of these resources will provide deeper insight into the genetic basis of alcoholism. PMID:23134044

  16. Predicting physical time series using dynamic ridge polynomial neural networks.

    Directory of Open Access Journals (Sweden)

    Dhiya Al-Jumeily

    Full Text Available Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.

  17. Neural substrate expansion for the restoration of brain function

    Directory of Open Access Journals (Sweden)

    Han-Chiao Isaac Chen

    2016-01-01

    Full Text Available Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks.

  18. Recurrent Neural Network for Computing the Drazin Inverse.

    Science.gov (United States)

    Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

    2015-11-01

    This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

  19. Nano-topography Enhances Communication in Neural Cells Networks

    KAUST Repository

    Onesto, V.

    2017-08-23

    Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can direct nerve cells assembly into computational efficient networks may provide new tools and criteria for tissue engineering and regenerative medicine. In this work, we used information theory approaches and functional multi calcium imaging (fMCI) techniques to examine how information flows in neural networks cultured on surfaces with controlled topography. We found that substrate roughness Sa affects networks topology. In the low nano-meter range, S-a = 0-30 nm, information increases with Sa. Moreover, we found that energy density of a network of cells correlates to the topology of that network. This reinforces the view that information, energy and surface nano-topography are tightly inter-connected and should not be neglected when studying cell-cell interaction in neural tissue repair and regeneration.

  20. Neural correlates of hate.

    Directory of Open Access Journals (Sweden)

    Semir Zeki

    Full Text Available In this work, we address an important but unexplored topic, namely the neural correlates of hate. In a block-design fMRI study, we scanned 17 normal human subjects while they viewed the face of a person they hated and also faces of acquaintances for whom they had neutral feelings. A hate score was obtained for the object of hate for each subject and this was used as a covariate in a between-subject random effects analysis. Viewing a hated face resulted in increased activity in the medial frontal gyrus, right putamen, bilaterally in premotor cortex, in the frontal pole and bilaterally in the medial insula. We also found three areas where activation correlated linearly with the declared level of hatred, the right insula, right premotor cortex and the right fronto-medial gyrus. One area of deactivation was found in the right superior frontal gyrus. The study thus shows that there is a unique pattern of activity in the brain in the context of hate. Though distinct from the pattern of activity that correlates with romantic love, this pattern nevertheless shares two areas with the latter, namely the putamen and the insula.

  1. 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

  2. Enhanced growth of neural networks on conductive cellulose-derived nanofibrous scaffolds

    International Nuclear Information System (INIS)

    Kuzmenko, Volodymyr; Kalogeropoulos, Theodoros; Thunberg, Johannes; Johannesson, Sara; Hägg, Daniel; Enoksson, Peter; Gatenholm, Paul

    2016-01-01

    The problem of recovery from neurodegeneration needs new effective solutions. Tissue engineering is viewed as a prospective approach for solving this problem since it can help to develop healthy neural tissue using supportive scaffolds. This study presents effective and sustainable tissue engineering methods for creating biomaterials from cellulose that can be used either as scaffolds for the growth of neural tissue in vitro or as drug screening models. To reach this goal, nanofibrous electrospun cellulose mats were made conductive via two different procedures: carbonization and addition of multi-walled carbon nanotubes. The resulting scaffolds were much more conductive than untreated cellulose material and were used to support growth and differentiation of SH-SY5Y neuroblastoma cells. The cells were evaluated by scanning electron microscopy and confocal microscopy methods over a period of 15 days at different time points. The results showed that the cellulose-derived conductive scaffolds can provide support for good cell attachment, growth and differentiation. The formation of a neural network occurred within 10 days of differentiation, which is a promising length of time for SH-SY5Y neuroblastoma cells. - Highlights: • The conductive scaffolds for neural tissue engineering are derived from cellulose. • The scaffolds are used to support growth and differentiation of SH-SY5Y cells. • Distinctive cell differentiation occurs within 10 days on conductive scaffolds. • Electrical conductivity and nanotopography improve neural network formation.

  3. Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

    How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

  4. Engineering physics

    CERN Document Server

    Mukherji, Uma

    2015-01-01

    ENGINEERING PHYSICS is designed as a textbook for first year engineering students of a two semester course in Applied Physics according to new revised syllabus. However the scope of this book is not only limited to undergraduate engineering students and science students, it can also serve as a reference book for practicing scientists.Advanced technological topics like LCD, Squid, Maglev system, Electron microscopes, MRI, Photonics - Photonic fibre, Nano-particles, CNT, Quantum computing etc., are explained with basic underlying principles of Physics.This text explained following topics with numerous solved, unsolved problems and questions from different angles. Part-I contains crystal structure, Liquid crystal, Thermo-electric effect, Thermionic emission, Ultrasonic, Acoustics, semiconductor and magnetic materials. Whereas Part-2 contains Optics, X-rays, Electron optics, Dielectric materials, Quantum Physics and Schrodinger wave equation, Laser, Fibre-optics and Holography, Radio-activity, Super-conductivity,...

  5. Music engineering

    CERN Document Server

    Brice, Richard

    2001-01-01

    Music Engineering is a hands-on guide to the practical aspects of electric and electronic music. It is both a compelling read and an essential reference guide for anyone using, choosing, designing or studying the technology of modern music. The technology and underpinning science are introduced through the real life demands of playing and recording, and illustrated with references to well known classic recordings to show how a particular effect is obtained thanks to the ingenuity of the engineer as well as the musician. In addition, an accompanying companion website containing over 50 specially chosen tracks for download, provides practical demonstrations of the effects and techniques described in the book. Written by a music enthusiast and electronic engineer, this book covers the electronics and physics of the subject as well as the more subjective aspects. The second edition includes an updated Digital section including MPEG3 and fact sheets at the end of each chapter to summarise the key electronics and s...

  6. Engineering surveying

    CERN Document Server

    Schofield, W

    2007-01-01

    Engineering surveying involves determining the position of natural and man-made features on or beneath the Earth's surface and utilizing these features in the planning, design and construction of works. It is a critical part of any engineering project. Without an accurate understanding of the size, shape and nature of the site the project risks expensive and time-consuming errors or even catastrophic failure.Engineering Surveying 6th edition covers all the basic principles and practice of this complex subject and the authors bring expertise and clarity. Previous editions of this classic text have given readers a clear understanding of fundamentals such as vertical control, distance, angles and position right through to the most modern technologies, and this fully updated edition continues that tradition.This sixth edition includes:* An introduction to geodesy to facilitate greater understanding of satellite systems* A fully updated chapter on GPS, GLONASS and GALILEO for satellite positioning in surveying* Al...

  7. Engineering Optics

    CERN Document Server

    Iizuka, Keigo

    2008-01-01

    Engineering Optics is a book for students who want to apply their knowledge of optics to engineering problems, as well as for engineering students who want to acquire the basic principles of optics. It covers such important topics as optical signal processing, holography, tomography, holographic radars, fiber optical communication, electro- and acousto-optic devices, and integrated optics (including optical bistability). As a basis for understanding these topics, the first few chapters give easy-to-follow explanations of diffraction theory, Fourier transforms, and geometrical optics. Practical examples, such as the video disk, the Fresnel zone plate, and many more, appear throughout the text, together with numerous solved exercises. There is an entirely new section in this updated edition on 3-D imaging.

  8. Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

    Science.gov (United States)

    Cabrera-Vives, Guillermo; Reyes, Ignacio; Förster, Francisco; Estévez, Pablo A.; Maureira, Juan-Carlos

    2017-02-01

    We introduce Deep-HiTS, a rotation-invariant convolutional neural network (CNN) model for classifying images of transient candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RFs). We show that our CNN significantly outperforms the RF model, reducing the error by almost half. Furthermore, for a fixed number of approximately 2000 allowed false transient candidates per night, we are able to reduce the misclassified real transients by approximately one-fifth. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope. We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS. Deep-HiTS is licensed under the terms of the GNU General Public License v3.0.

  9. Software packages for food engineering needs

    OpenAIRE

    Abakarov, Alik

    2011-01-01

    The graphic user interface (GUI) software packages “ANNEKs” and “OPT-PROx” are developed to meet food engineering needs. “OPT-RROx” (OPTimal PROfile) is software developed to carry out thermal food processing optimization based on the variable retort temperature processing and global optimization technique. “ANNEKs” (Artificial Neural Network Enzyme Kinetics) is software designed for determining the kinetics of enzyme hydrolysis of protein at different initial reaction parameters based on the...

  10. Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

    Science.gov (United States)

    Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

    2017-10-01

    This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

  11. Biochemistry engineering

    International Nuclear Information System (INIS)

    Jang, Ho Nam

    1993-01-01

    This deals with biochemistry engineering with nine chapters. It explains bionics on development and prospect, basics of life science on classification and structure, enzyme and metabolism, fundamentals of chemical engineering on viscosity, shear rate, PFR, CSTR, mixing, dispersion, measurement and response, Enzyme kinetics, competitive inhibition, pH profile, temperature profile, stoichiometry and fermentation kinetics, bio-reactor on Enzyme-reactor and microorganism-reactor, measurement and processing on data acquisition and data processing, separation and purification, waste water treatment and economics of bionics process.

  12. Micro Engineering

    DEFF Research Database (Denmark)

    Alting, Leo; Kimura, F.; Hansen, Hans Nørgaard

    2003-01-01

    The paper addresses the questions of how micro products are designed and how they are manufactured. Definitions of micro products and micro engineering are discussed and the presentation is aimed at describing typical issues, possibilities and tools regarding design of micro products. The implica......The paper addresses the questions of how micro products are designed and how they are manufactured. Definitions of micro products and micro engineering are discussed and the presentation is aimed at describing typical issues, possibilities and tools regarding design of micro products...

  13. Engineering tribology

    CERN Document Server

    Stachowiak, Gwidon

    2014-01-01

    Engineering Tribology, 4th Edition is an established introductory reference focusing on the key concepts and engineering implications of tribology. Taking an interdisciplinary view, the book brings together the relevant knowledge from different fields needed to achieve effective analysis and control of friction and wear. Updated to cover recent advances in tribology, this new edition includes new sections on ionic and mesogenic lubricants, surface texturing, and multiscale characterization of 3D surfaces and coatings. Current trends in nanotribology are discussed, such as those relating to

  14. Software engineering

    CERN Document Server

    Thorin, Marc

    1985-01-01

    Software Engineering describes the conceptual bases as well as the main methods and rules on computer programming. This book presents software engineering as a coherent and logically built synthesis and makes it possible to properly carry out an application of small or medium difficulty that can later be developed and adapted to more complex cases. This text is comprised of six chapters and begins by introducing the reader to the fundamental notions of entities, actions, and programming. The next two chapters elaborate on the concepts of information and consistency domains and show that a proc

  15. Corrosion engineering

    Energy Technology Data Exchange (ETDEWEB)

    Fontana, M.G.

    1986-01-01

    This book emphasizes the engineering approach to handling corrosion. It presents corrosion data by corrosives or environments rather than by materials. It discusses the corrosion engineering of noble metals, ''exotic'' metals, non-metallics, coatings, mechanical properties, and corrosion testing, as well as modern concepts. New sections have been added on fracture mechanics, laser alloying, nuclear waste isolation, solar energy, geothermal energy, and the Statue of Liberty. Special isocorrosion charts, developed by the author, are introduced as a quick way to look at candidates for a particular corrosive.

  16. Numerical Solution of Fuzzy Differential Equations with Z-numbers Using Bernstein Neural Networks

    Directory of Open Access Journals (Sweden)

    Raheleh Jafari

    2017-01-01

    Full Text Available The uncertain nonlinear systems can be modeled with fuzzy equations or fuzzy differential equations (FDEs by incorporating the fuzzy set theory. The solutions of them are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this paper, the solutions of FDEs are approximated by two types of Bernstein neural networks. Here, the uncertainties are in the sense of Z-numbers. Initially the FDE is transformed into four ordinary differential equations (ODEs with Hukuhara differentiability. Then neural models are constructed with the structure of ODEs. With modified back propagation method for Z- number variables, the neural networks are trained. The theory analysis and simulation results show that these new models, Bernstein neural networks, are effective to estimate the solutions of FDEs based on Z-numbers.

  17. Community Economics

    OpenAIRE

    武藤, 宣道; Nobumichi, MUTOH

    2000-01-01

    This paper examines the new field of community economics with respect to Japan. A number of studies in community economics have already been produced in OECD countries including the United States. Although these are of great interest, each country has its own historical, socioeconomic context and must therefore develop its own approach to community economics. Community-oriented economics is neither macro-nor micro-economics in the standard economics textbook sense. Most community economics st...

  18. Comparing Two Methods of Neural Networks to Evaluate Dead Oil Viscosity

    Directory of Open Access Journals (Sweden)

    Meysam Dabiri-Atashbeyk

    2018-01-01

    Full Text Available Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.

  19. Engineering applications of soft computing

    CERN Document Server

    Díaz-Cortés, Margarita-Arimatea; Rojas, Raúl

    2017-01-01

    This book bridges the gap between Soft Computing techniques and their applications to complex engineering problems. In each chapter we endeavor to explain the basic ideas behind the proposed applications in an accessible format for readers who may not possess a background in some of the fields. Therefore, engineers or practitioners who are not familiar with Soft Computing methods will appreciate that the techniques discussed go beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas. At the same time, the book will show members of the Soft Computing community how engineering problems are now being solved and handled with the help of intelligent approaches. Highlighting new applications and implementations of Soft Computing approaches in various engineering contexts, the book is divided into 12 chapters. Further, it has been structured so that each chapter can be read independently of the others.

  20. Cracking the neural code, treating paralysis and the future of bioelectronic medicine.

    Science.gov (United States)

    Bouton, C

    2017-07-01

    The human nervous system is a vast network carrying not only sensory and movement information, but also information to and from our organs, intimately linking it to our overall health. Scientists and engineers have been working for decades to tap into this network and 'crack the neural code' by decoding neural signals and learning how to 'speak' the language of the nervous system. Progress has been made in developing neural decoding methods to decipher brain activity and bioelectronic technologies to treat rheumatoid arthritis, paralysis, epilepsy and for diagnosing brain-related diseases such as Parkinson's and Alzheimer's disease. In a recent first-in-human study involving paralysis, a paralysed male study participant regained movement in his hand, years after his injury, through the use of a bioelectronic neural bypass. This work combined neural decoding and neurostimulation methods to translate and re-route signals around damaged neural pathways within the central nervous system. By extending these methods to decipher neural messages in the peripheral nervous system, status information from our bodily functions and specific organs could be gained. This, one day, could allow real-time diagnostics to be performed to give us a deeper insight into a patient's condition, or potentially even predict disease or allow early diagnosis. The future of bioelectronic medicine is extremely bright and is wide open as new diagnostic and treatment options are developed for patients around the world. © 2017 The Association for the Publication of the Journal of Internal Medicine.