WorldWideScience

Sample records for neural engineering community

  1. 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.

  2. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    methods. That is why it is becoming popular in various fields including coastal engineering. Waves and tides will play important roles in coastal erosion or accretion. This paper briefly describes the back-propagation neural networks and its application...

  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. ARTIFICIAL NEURAL NETWORK OPTIMIZATION MODELING ON ENGINE PERFORMANCE OF DIESEL ENGINE USING BIODIESEL FUEL

    National Research Council Canada - National Science Library

    M R Shukri; M M Rahman; D Ramasamy; K Kadirgama

    2015-01-01

      This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling...

  9. 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.

  10. 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.

    . International conference on COPEDEC VII, Dubai (UAE), paper no- 27, 1-11. Mandal, S. 2001. Tides prediction using back propagation neural networks, Proc. International Conference in Ocean Engineering, ICOE, IIT Madras, 499-504. Mandal, S. and Prabhaharan, N...

  11. 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...

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

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Izadi-Zamanabadi, Roozbeh

    2002-01-01

    This paper concerns fault detection and isolation based on neural network modeling. A neural network is trained to recognize the input-output behavior of a nonlinear plant, and faults are detected if the output estimated by the network differs from the measured plant output by more than a specified...... 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...

  13. 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

  14. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  16. Adhesion molecule-modified biomaterials for neural tissue engineering

    Directory of Open Access Journals (Sweden)

    Shreyas S Rao

    2009-06-01

    Full Text Available Adhesion molecules (AMs represent one class of biomolecules that promote central nervous system regeneration. These tethered molecules provide cues to regenerating neurons that recapitulate the native brain environment. Improving cell adhesive potential of non-adhesive biomaterials is therefore a common goal in neural tissue engineering. This review discusses common AMs used in neural biomaterials and the mechanism of cell attachment to these AMs. Methods to modify materials with AMs are discussed and compared. Additionally, patterning of AMs for achieving specific neuronal responses is explored.

  17. FPGA Simulation Engine for Customized Construction of Neural Microcircuits

    Science.gov (United States)

    Blair, Hugh T.; Cong, Jason; Wu, Di

    2014-01-01

    In this paper we describe an FPGA-based platform for high-performance and low-power simulation of neural microcircuits composed from integrate-and-fire (IAF) neurons. Based on high-level synthesis, our platform uses design templates to map hierarchies of neuron model to logic fabrics. This approach bypasses high design complexity and enables easy optimization and design space exploration. We demonstrate the benefits of our platform by simulating a variety of neural microcircuits that perform oscillatory path integration, which evidence suggests may be a critical building block of the navigation system inside a rodent’s brain. Experiments show that our FPGA simulation engine for oscillatory neural microcircuits can achieve up to 39× speedup compared to software benchmarks on commodity CPU, and 232× energy reduction compared to embedded ARM core. PMID:25584120

  18. Evolutionary swarm neural network game engine for Capture Go.

    Science.gov (United States)

    Cai, Xindi; Venayagamoorthy, Ganesh K; Wunsch, Donald C

    2010-03-01

    Evaluation of the current board position is critical in computer game engines. In sufficiently complex games, such a task is too difficult for a traditional brute force search to accomplish, even when combined with expert knowledge bases. This motivates the investigation of alternatives. This paper investigates the combination of neural networks, particle swarm optimization (PSO), and evolutionary algorithms (EAs) to train a board evaluator from zero knowledge. By enhancing the survivors of an EA with PSO, the hybrid algorithm successfully trains the high-dimensional neural networks to provide an evaluation of the game board through self-play. Experimental results, on the benchmark game of Capture Go, demonstrate that the hybrid algorithm can be more powerful than its individual parts, with the system playing against EA and PSO trained game engines. Also, the winning results of tournaments against a Hill-Climbing trained game engine confirm that the improvement comes from the hybrid algorithm itself. The hybrid game engine is also demonstrated against a hand-coded defensive player and a web player. Copyright 2009 Elsevier Ltd. All rights reserved.

  19. 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.

  20. Community structure of complex networks based on continuous neural network

    Science.gov (United States)

    Dai, Ting-ting; Shan, Chang-ji; Dong, Yan-shou

    2017-09-01

    As a new subject, the research of complex networks has attracted the attention of researchers from different disciplines. Community structure is one of the key structures of complex networks, so it is a very important task to analyze the community structure of complex networks accurately. In this paper, we study the problem of extracting the community structure of complex networks, and propose a continuous neural network (CNN) algorithm. It is proved that for any given initial value, the continuous neural network algorithm converges to the eigenvector of the maximum eigenvalue of the network modularity matrix. Therefore, according to the stability of the evolution of the network symbol will be able to get two community structure.

  1. Engineering neural systems for high-level problem solving.

    Science.gov (United States)

    Sylvester, Jared; Reggia, James

    2016-07-01

    There is a long-standing, sometimes contentious debate in AI concerning the relative merits of a symbolic, top-down approach vs. a neural, bottom-up approach to engineering intelligent machine behaviors. While neurocomputational methods excel at lower-level cognitive tasks (incremental learning for pattern classification, low-level sensorimotor control, fault tolerance and processing of noisy data, etc.), they are largely non-competitive with top-down symbolic methods for tasks involving high-level cognitive problem solving (goal-directed reasoning, metacognition, planning, etc.). Here we take a step towards addressing this limitation by developing a purely neural framework named galis. Our goal in this work is to integrate top-down (non-symbolic) control of a neural network system with more traditional bottom-up neural computations. galis is based on attractor networks that can be "programmed" with temporal sequences of hand-crafted instructions that control problem solving by gating the activity retention of, communication between, and learning done by other neural networks. We demonstrate the effectiveness of this approach by showing that it can be applied successfully to solve sequential card matching problems, using both human performance and a top-down symbolic algorithm as experimental controls. Solving this kind of problem makes use of top-down attention control and the binding together of visual features in ways that are easy for symbolic AI systems but not for neural networks to achieve. Our model can not only be instructed on how to solve card matching problems successfully, but its performance also qualitatively (and sometimes quantitatively) matches the performance of both human subjects that we had perform the same task and the top-down symbolic algorithm that we used as an experimental control. We conclude that the core principles underlying the galis framework provide a promising approach to engineering purely neurocomputational systems for problem

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

    National Research Council Canada - National Science Library

    Prasada Rao, K; Victor Babu, T; Anuradha, G; Appa Rao, B.V

    ...) 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...

  3. 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

  4. Design and implementation of a random neural network routing engine.

    Science.gov (United States)

    Kocak, T; Seeber, J; Terzioglu, H

    2003-01-01

    Random neural network (RNN) is an analytically tractable spiked neural network model that has been implemented in software for a wide range of applications for over a decade. This paper presents the hardware implementation of the RNN model. Recently, cognitive packet networks (CPN) is proposed as an alternative packet network architecture where there is no routing table, instead the RNN based reinforcement learning is used to route packets. Particularly, we describe implementation details for the RNN based routing engine of a CPN network processor chip: the smart packet processor (SPP). The SPP is a dual port device that stores, modifies, and interprets the defining characteristics of multiple RNN models. In addition to hardware design improvements over the software implementation such as the dual access memory, output calculation step, and reduced output calculation module, this paper introduces a major modification to the reinforcement learning algorithm used in the original CPN specification such that the number of weight terms are reduced from 2n/sup 2/ to 2n. This not only yields significant memory savings, but it also simplifies the calculations for the steady state probabilities (neuron outputs in RNN). Simulations have been conducted to confirm the proper functionality for the isolated SPP design as well as for the multiple SPP's in a networked environment.

  5. Multifunctional nanowire scaffolds for neural tissue engineering applications

    Science.gov (United States)

    Bechara, Samuel Leo

    Unlike other regions of the body, the nervous system is extremely vulnerable to damage and injury because it has a limited ability to self-repair. Over 250,000 people in the United States have spinal cord injuries and due to the complicated pathophysiology of such injuries, there are few options available for functional regeneration of the spinal column. Furthermore, peripheral nerve damage is troublingly common in the United States, with an estimated 200,000 patients treated surgically each year. The current gold standard in treatment for peripheral nerve damage is a nerve autograft. This technique was pioneered over 45 years ago, but suffers from a major drawback. By transecting a nerve from another part of the body, function is regained at the expense of destroying a nerve connection elsewhere. Because of these issues, the investigation of different materials for regenerating nervous tissue is necessary. This work examines multi-functional nanowire scaffolds to provide physical and chemical guidance cues to neural stem cells to enhance cellular activity from a biomedical engineering perspective. These multi-functional scaffolds include a unique nanowire nano-topography to provide physical cues to guide cellular adhesion. The nanowires were then coated with an electrically conductive polymer to further enhance cellular activity. Finally, nerve growth factor was conjugated to the surface of the scaffolds to provide chemical cues for the neural stem cells. The results in this work suggest that these multifunctional nanowire scaffolds could be used in vivo to repair nervous system tissue.

  6. Configurable analog-digital conversion using the neural engineering framework.

    Science.gov (United States)

    Mayr, Christian G; Partzsch, Johannes; Noack, Marko; Schüffny, Rene

    2014-01-01

    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.

  7. Methodology of Neural Design: Applications in Microwave Engineering

    OpenAIRE

    Z. Raida; P. Pomenka

    2006-01-01

    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 formulat...

  8. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  9. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, Moh' d Sami S. [Department of Mechanical Engineering, The Hashemite University, Zarqa 13115 (Jordan)

    2008-02-15

    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 guarantees 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. (author)

  11. 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.

  12. 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.

  13. 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.

  14. 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.

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

    African Journals Online (AJOL)

    The method uses an independent radial basis function (RBF) neural network model to model engine dynamics, and the modelling errors are used to form the basis for ... The simulation results show that all the simulated faults can be clearly detected and isolated in dynamic conditions throughout the engine operating range.

  16. What Counts as Outcomes? Community Perspectives of an Engineering Partnership

    Science.gov (United States)

    Reynolds, Nora Pillard

    2014-01-01

    This study explored the perspectives of community organization representatives and community residents about a partnership between a College of Engineering and a rural municipality in Nicaragua. The intended community outcomes described by university participants during interviews corresponded with tangible project outcomes, such as access to…

  17. Developing a Virtual Engineering Management Community

    Science.gov (United States)

    Hewitt, Bill; Kidd, Moray; Smith, Robin; Wearne, Stephen

    2016-01-01

    The paper reviews the lessons of planning and running an "Engineering Management" practitioner development programme in a partnership between BP and the University of Manchester. This distance-learning programme is for professional engineers in mid-career experienced in the engineering and support activities for delivering safe,…

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.…

  3. 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…

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

    OpenAIRE

    K. Prasada Rao; T. Victor Babu; Anuradha, G.; B.V. Appa Rao

    2016-01-01

    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 evaluat...

  5. 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.

  6. 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

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

    Science.gov (United States)

    Subramanian, Anuradha; Krishnan, Uma Maheswari; Sethuraman, Swaminathan

    2009-11-25

    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.

  8. 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.

  9. Accommodating a Game Engine for Community Adoption

    OpenAIRE

    Stien, Joakim N.; Stensby, Sverre B.

    2015-01-01

    Thesis on improving and expanding a game engine, to make it a better alternative for game development. We have added particles, network capabilities, normal and specular maps. We have improved the implementation of the audio, the shadow generaton, and API.

  10. 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.

  11. 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...

  12. 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.

  13. 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.

  14. Developing a virtual engineering management community

    Science.gov (United States)

    Hewitt, Bill; Kidd, Moray; Smith, Robin; Wearne, Stephen

    2016-03-01

    The paper reviews the lessons of planning and running an Engineering Management practitioner development programme in a partnership between BP and the University of Manchester. This distance-learning programme is for professional engineers in mid-career experienced in the engineering and support activities for delivering safe, compliant and reliable projects and operations worldwide. The programme concentrates on the why and how of leadership and judgement in managing the engineering of large and small projects and operational support. Two intensive residential weeks are combined with a virtual learning environment over one year. Assessed assignments between and after the residential weeks provide opportunities for individual reflective learning for each delegate through applying concepts and the lessons of case studies to their experience, current challenges and expected responsibilities. This successful partnership between a major global company and a university rich in research and teaching required a significant dedication of intellectual and leadership effort by all concerned. The rewards for both parties and most importantly for the engineers themselves are extensive.

  15. 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

  16. Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

    Directory of Open Access Journals (Sweden)

    Mohamed A. Shahin

    2009-01-01

    Full Text Available Artificial neural networks (ANNs are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

  17. Novel nanofibrous spiral scaffolds for neural tissue engineering

    Science.gov (United States)

    Valmikinathan, Chandra M.; Tian, Jingjing; Wang, Junping; Yu, Xiaojun

    2008-12-01

    Due to several drawbacks associated with autografts and allografts, tissue-engineering approaches have been widely used to repair peripheral nerve injuries. Most of the traditional tissue-engineered scaffolds in use are either tubular (single or multi-lumen) or hydrogel-based cylindrical grafts, which provide limited surface area for cell attachment and regeneration. Here, we show a novel poly(lactide-co-glycotide) (PLGA) microsphere-based spiral scaffold design with a nanofibrous surface that has enhanced surface areas and possesses sufficient mechanical properties and porosities to support the nerve regeneration process. These scaffolds have an open architecture that goes evenly throughout the scaffolds hence leaving enough volume for media influx and deeper cell penetration into the scaffolds. The in vitro tests conducted using Schwann cells show that the nanofibrous spiral scaffolds promote higher cell attachment and proliferation when compared to contemporary tubular scaffolds or nanofiber-based tubular scaffolds. Also, the nanofiber coating on the surfaces enhances the surface area, mimics the extracellular matrix and provides unidirectional alignment of cells along its direction. Hence, we propose that these scaffolds could alleviate some drawbacks in current nerve grafts and could potentially be used in nerve regeneration.

  18. Vertical Interaction in Open Software Engineering Communities

    Science.gov (United States)

    2009-03-01

    from companies like Sun, DEC, and HP. At the same time, however, Richard Stallman was laying the foundation for the Free Software movement through...distributing software, Richard Stall- 2 CHAPTER 1. INTRODUCTION man was recognized with a MacArthur foundation “genius” grant. A nascent community formed...Associates, Se- bastapol, CA, Oct. 1999. [94] RAYMOND, E. S. The Art of UNIX Programming, 1 ed. Addison-Wesley Profes- sional, Oct. 2003. [95] RICHARDS , J

  19. Modeling and adaptive control of a camless engine using neural networks and estimation techniques

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-08-09

    A system to control the cylinder air charge (CAC) in a camless internal combustion (IC) engine was recently developed. The performance of an IC engine connected to an adaptive artificial neural network (ANN) based feedback controller was then investigated. A control oriented model for the engine intake process was created based on thermodynamics laws and was validated against engine experimental data. Input-output data at a speed of 1500 RPM was generated and used to train an ANN model for the engine. The inputs were the intake valve lift (IVL) and closing timing (IVC). The output was the CAC. The controller consisted of a feedforward controller, CAC estimator, and on-line ANN parameter estimator. The feedforward controller provided IVL and IVC that satisfied the driver's torque demand and was the inverse of the engine ANN model. The on-line ANN used the error between the CAC measurement from the CAC estimator and its predicted value from the ANN to update the network's parameters. The feedforward controller was therefore adapted since its operation depended on the ANN model. The adaptation scheme improved the ANN prediction accuracy when the engine parts degraded, the speed changed or when modeling errors occurred. The engine controller exhibited good CAC tracking performance. Computer simulation demonstrated the capability of the camless engine controller. 17 refs., 5 figs.

  20. 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.

  1. 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.

  2. In vitro and in vivo analysis and characterization of engineered spinal neural implants (Conference Presentation)

    Science.gov (United States)

    Shor, Erez; Shoham, Shy; Levenberg, Shulamit

    2016-03-01

    Spinal cord injury is a devastating medical condition. Recent developments in pre-clinical and clinical research have started to yield neural implants inducing functional recovery after spinal cord transection injury. However, the functional performance of the transplants was assessed using histology and behavioral experiments which are unable to study cell dynamics and the therapeutic response. Here, we use neurophotonic tools and optogenetic probes to investigate cellular level morphology and activity characteristics of neural implants over time at the cellular level. These methods were used in-vitro and in-vivo, in a mouse spinal cord injury implant model. Following previous attempts to induce recovery after spinal cord injury, we engineered a pre-vascularized implant to obtain better functional performance. To image network activity of a construct implanted in a mouse spinal cord, we transfected the implant to express GCaMP6 calcium activity indicators and implanted these constructs under a spinal cord chamber enabling 2-photon chronic in vivo neural activity imaging. Activity and morphology analysis image processing software was developed to automatically quantify the behavior of the neural and vascular networks. Our experimental results and analyses demonstrate that vascularized and non-vascularized constructs exhibit very different morphologic and activity patterns at the cellular level. This work enables further optimization of neural implants and also provides valuable tools for continuous cellular level monitoring and evaluation of transplants designed for various neurodegenerative disease models.

  3. Graphene in the Design and Engineering of Next-Generation Neural Interfaces.

    Science.gov (United States)

    Kostarelos, Kostas; Vincent, Melissa; Hebert, Clement; Garrido, Jose A

    2017-11-01

    Neural interfaces are becoming a powerful toolkit for clinical interventions requiring stimulation and/or recording of the electrical activity of the nervous system. Active implantable devices offer a promising approach for the treatment of various diseases affecting the central or peripheral nervous systems by electrically stimulating different neuronal structures. All currently used neural interface devices are designed to perform a single function: either record activity or electrically stimulate tissue. Because of their electrical and electrochemical performance and their suitability for integration into flexible devices, graphene-based materials constitute a versatile platform that could help address many of the current challenges in neural interface design. Here, how graphene and other 2D materials possess an array of properties that can enable enhanced functional capabilities for neural interfaces is illustrated. It is emphasized that the technological challenges are similar for all alternative types of materials used in the engineering of neural interface devices, each offering a unique set of advantages and limitations. Graphene and 2D materials can indeed play a commanding role in the efforts toward wider clinical adoption of bioelectronics and electroceuticals. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. 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.

  5. 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.

  6. Microbial community engineering for biopolymer production from glycerol

    NARCIS (Netherlands)

    Moralejo-Gárate, H.; Mar'atusalihat, E.; Kleerebezem, R.; Van Loosdrecht, M.C.M.

    2011-01-01

    In this work, the potential of using microbial community engineering for production of polyhydroxyalkanoates (PHA) from glycerol was explored. Crude glycerol is a by-product of the biofuel (biodiesel and bioethanol) industry and potentially a good substrate for bioplastic production. A PHA-producing

  7. 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.

  8. 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…

  9. Engineering microbial communities using thermodynamic principles and electrical interfaces.

    Science.gov (United States)

    Zerfaß, Christian; Chen, Jing; Soyer, Orkun S

    2017-12-18

    Microbial communities present the next research frontier. We argue here that understanding and engineering microbial communities requires a holistic view that considers not only species-species, but also species-environment interactions, and feedbacks between ecological and evolutionary dynamics (eco-evo feedbacks). Due this multi-level nature of interactions, we predict that approaches aimed soley at altering specific species populations in a community (through strain enrichment or inhibition), would only have a transient impact, and species-environment and eco-evo feedbacks would eventually drive the microbial community to its original state. We propose a higher-level engineering approach that is based on thermodynamics of microbial growth, and that considers specifically microbial redox biochemistry. Within this approach, the emphasis is on enforcing specific environmental conditions onto the community. These are expected to generate higher-level thermodynamic bounds onto the system, which the community structure and function can then adapt to. We believe that the resulting end-state can be ecologically and evolutionarily stable, mimicking the natural states of complex communities. Toward designing the exact nature of the environmental enforcement, thermodynamics and redox biochemistry can act as coarse-grained principles, while the use of electrodes-as electron providing or accepting redox agents-can provide implementation with spatiotemporal control. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  10. 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

  11. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

    Science.gov (United States)

    Amozegar, M; Khorasani, K

    2016-04-01

    In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. 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.

  13. Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

    Science.gov (United States)

    Colin, Guillaume; Chamaillard, Yann; Bloch, Gérard; Corde, Gilles

    2007-07-01

    Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.

  14. Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

    Science.gov (United States)

    Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

    2017-06-01

    We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

  15. Neural network controller development and implementation for spark ignition engines with high EGR levels.

    Science.gov (United States)

    Vance, Jonathan Blake; Singh, Atmika; Kaul, Brian C; Jagannathan, Sarangapani; Drallmeier, James A

    2007-07-01

    Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10%-25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in NOx from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine.

  16. 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).

  17. 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

  18. Reliability, maintainability, and availability engineering for integrated community energy systems

    Energy Technology Data Exchange (ETDEWEB)

    Wang, P.Y.; Mavec, J.; Wolosewicz, R.M.; Calm, J.M.; Chopra, P.S.

    1979-12-01

    The reliability, maintainability, and availability (RMA) engineering methodologies for integrated community energy systems (ICES) are reported. Since the tangible and intangible costs of a system failure may outweigh the benefits of the ICES approaches, RMA consideration must be an integral part of ICES engineering. The effectiveness of system planning and design depends on component reliability information and on forecasts of community load profiles. Supply subsystems must provide sufficient capacity to meet demands in spite of maintenance and unscheduled outages. This allowance is the major task of probabilistic system planning. Because reliability and maintainability performance is partially a random process, probabilistic methodology is required for analysis and comparison. Portions of a community energy system being modified or expanded may already be in use before additional portions reach the design stage. This situation presents an opportunity to extend the RMA assessment of the existing system and to improve the additions that will be made to it. RMA engineering is essential to all phases of planning, design, construction, and operation of ICES. The methodologies presented provide a systematic, disciplined approach to predict and analyze RMA, to determine corrective actions necessary, and to achieve performance goals.

  19. DЕTERMINATION OF THE DRUM MILLS’ ENGINE CAPACITY BY USING NEURAL NETWORK WITH SUBORDINATE INPUT PARAMETERS

    Directory of Open Access Journals (Sweden)

    Teodora HRISTOVA

    2012-05-01

    Full Text Available A successful experiment has been done to train the neural network to determine the drum mills’ engine capacity by using the program „QwikNet 2.23”. As a result we get a trained neural network with a maximum error of 1.00619.10-5 which can be used for assessing the capacity of the electric motors of drum mills and can be considered an accurate mathematical model

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures

    OpenAIRE

    Tosun, Erdi; Aydin, Kadir; Bilgili, Mehmet

    2016-01-01

    This study deals with usage of linear regression (LR) and artificial neural network (ANN) modeling to predict engine performance; torque and exhaust emissions; and carbon monoxide, oxides of nitrogen (CO, NOx) of a naturally aspirated diesel engine fueled with standard diesel, peanut biodiesel (PME) and biodiesel-alcohol (EME, MME, PME) mixtures. Experimental work was conducted to obtain data to train and test the models. Backpropagation algorithm was used as a learning algorithm of ANN in th...

  9. 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

  10. 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

  11. 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.

  12. Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    1998-01-01

    The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

  13. 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".

  14. 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...

  15. 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.

  16. An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory

    Directory of Open Access Journals (Sweden)

    Naiqi Song

    2013-01-01

    Full Text Available Functional diversity in plant communities is a key driver of ecosystem processes. The effective methods for measuring functional diversity are important in ecological studies. A new method based on neural network, self-organizing feature map (SOFM index, was put forward and described. A case application to the study of functional diversity of Phellodendron amurense communities in Xiaolongmen Forest Park of Beijing was carried out in this paper. The results showed that SOFM index was an effective method in the evaluation of functional diversity and its change in plant communities. Significant nonlinear correlations of SOFM index with the common used methods, FAD, MFAD, FDp, FDc, FRic, and FDiv indices, also proved that SOFM index is useful in the studies of functional diversity.

  17. 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…

  18. 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.

  19. Engineering effect of Pinna nobilis shells on benthic communities

    Directory of Open Access Journals (Sweden)

    Lotfi Rabaoui

    2015-07-01

    Full Text Available Within the framework of the possibility of using the Mediterranean pen shell Pinna nobilis in restoration and conservation plans of benthic habitats, an in situ experiment was conducted using empty P. nobilis shells. The latter were transplanted in a bare soft-bottomed area and their associated fauna were followed along 120 days and compared at different temporal points and with the assemblages living in the surrounding soft-sediment area. Compared to soft-sediment communities, an evidently increasing succession of species richness, abundance, and diversity descriptors (Shannon-Wiener H′ and Pielou's evenness J′ was observed with the community inhabiting empty Pinna shells. Among the forty-five (45 species found in association with the transplanted empty shells, seventeen (17 were found constantly in the three temporal points; the other twenty-eight (28 species appeared in the samples collected in the second and/or third sampling time. While motile and sessile species associated to Pinna shells showed an increasing pattern of appearance and abundance along the experiment time, those of soft sediment remained almost constant. The comparison between Pinna shells and soft-sediment associated communities showed that the species richness was slightly different between the two different sample types (49 for soft sediment versus 45 for empty Pinna shells; however the total abundance was found more important with empty Pinna shells. The results obtained herein argue in favor of the important engineering effect of P. nobilis in soft benthic habitats and therefore for the necessity of its conservation.

  20. 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

  1. 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.

  2. 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

  3. 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…

  4. 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…

  5. 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.

  6. 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.

  7. Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires.

    Science.gov (United States)

    Conant, Genevieve; Ahadian, Samad; Zhao, Yimu; Radisic, Milica

    2017-09-18

    Kinase inhibitors are often used as cancer targeting agents for their ability to prevent the activation of cell growth and proliferation signals. Cardiotoxic effects have been identified for some marketed kinase inhibitors that were not detected during clinical trials. We hypothesize that more predictive cardiac functional assessments of kinase inhibitors on human myocardium can be established by combining a high-throughput two-dimensional (2D) screening assay and a high-content three-dimensional (3D) engineered cardiac tissue (BiowireTM) based assay, and using human induced pluripotent stem cell-derived CMs (hiPSC-CMs). A subset (80) of compounds from the GlaxoSmithKline published kinase inhibitor set were tested on hiPSC-CM monolayers and significant effects on cell viability, calcium transients, and contraction frequency were observed. Artificial neural network modelling was then used to analyze the experimental results in an efficient and unbiased manner to select for kinase inhibitors with minimal effects on cell viability and function. Inhibitors of specific interest based on the modeling were evaluated in the 3D Biowire tissues. The three-dimensional Biowire platform eliminated oversensitivity in detecting both Ca2+ transient amplitude enhancements as well as the acute detrimental effects on cell viability due to the kinase inhibitor application as compared to the monolayer testing.

  8. 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

  9. Iterative Radial Basis Functions Neural Networks as Metamodels of Stochastic Simulations of the Quality of Search Engines in the World Wide Web.

    Science.gov (United States)

    Meghabghab, George

    2001-01-01

    Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…

  10. 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)

  11. Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks.

    Science.gov (United States)

    Lesnik, Keaton Larson; Liu, Hong

    2017-09-19

    The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including eight separate substrates and three different wastewaters. Artificial Neural Networks (ANNs) were used to establish mathematical relationships between wastewater/solution characteristics, biofilm communities, and reactor performance. ANN models that incorporated biotic interactions predicted reactor performance outcomes more accurately than those that did not. The average percent error of power density predictions was 16.01 ± 4.35%, while the average percent error of Coulombic efficiency and COD removal rate predictions were 1.77 ± 0.57% and 4.07 ± 1.06%, respectively. Predictions of power density improved to within 5.76 ± 3.16% percent error through classifying taxonomic data at the family versus class level. Results suggest that the microbial communities and performance of bioelectrochemical systems can be accurately predicted using data-mining, machine-learning techniques.

  12. 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.

  13. An interpretation of neural networks as inference engines with application to transformer failure diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Adriana R. Garcez; Miranda, Vladimiro [Instituto de Engenharia de Sistemas e Computadores do Porto, INESC Porto (Portugal)

    2005-12-01

    An artificial neural network concept has been developed for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). A new methodology for mapping the neural network into a rule-based inference system is described. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a Fuzzy Inference System. Some studies are reported, illustrating the good results obtained. (author)

  14. Engineering Applications of Neural Computing: A State-of-the-Art Survey

    Science.gov (United States)

    1991-05-01

    Papachristou. C. A., "Training of a Neural Network for Pattern Classifi- cation Based on Entropy Measure," Proceedings of 1988 IEEE International Conference...diction and System Modeling," Technical Report LA-UR-87-2662, Los Alamos National Lab- oratoiy, 1987. 10. Levy, W. B., "Maximum Entropy Prediction in Neural...Lawrence Erlbaum, Hillsdale, NJ, 1990. 53. MacGregor, R. J., Neural and Brain Modeling, The Academic Press, New York, 1987. 54. Marr , D., Vision, San

  15. Transplantation of tissue engineering neural network and formation of neuronal relay into the transected rat spinal cord.

    Science.gov (United States)

    Lai, Bi-Qin; Che, Ming-Tian; Du, Bao-Ling; Zeng, Xiang; Ma, Yuan-Huan; Feng, Bo; Qiu, Xue-Chen; Zhang, Ke; Liu, Shu; Shen, Hui-Yong; Wu, Jin-Lang; Ling, Eng-Ang; Zeng, Yuan-Shan

    2016-12-01

    Severe spinal cord injury (SCI) causes loss of neural connectivity and permanent functional deficits. Re-establishment of new neuronal relay circuits after SCI is therefore of paramount importance. The present study tested our hypothesis if co-culture of neurotrophin-3 (NT-3) gene-modified Schwann cells (SCs, NT-3-SCs) and TrkC (NT-3 receptor) gene-modified neural stem cells (NSCs, TrkC-NSCs) in a gelatin sponge scaffold could construct a tissue engineering neural network for re-establishing an anatomical neuronal relay after rat spinal cord transection. Eight weeks after transplantation, the neural network created a favorable microenvironment for axonal regeneration and for survival and synaptogenesis of NSC-derived neurons. Biotin conjugates of cholera toxin B subunit (b-CTB, a transneuronal tracer) was injected into the crushed sciatic nerve to label spinal cord neurons. Remarkably, not only ascending and descending nerve fibers, but also propriospinal neurons, made contacts with b-CTB positive NSC-derived neurons. Moreover, b-CTB positive NSC-derived neurons extended their axons making contacts with the motor neurons located in areas caudal to the injury/graft site of spinal cord. Further study showed that NT-3/TrkC interactions activated the PI3K/AKT/mTOR pathway and PI3K/AKT/CREB pathway affecting synaptogenesis of NSC-derived neurons. Together, our findings suggest that NT-3-mediated TrkC signaling plays an essential role in constructing a tissue engineering neural network thus representing a promising avenue for effective exogenous neuronal relay-based treatment for SCI. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    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

  17. Engineering

    National Research Council Canada - National Science Library

    Includes papers in the following fields: Aerospace Engineering, Agricultural Engineering, Chemical Engineering, Civil Engineering, Electrical Engineering, Environmental Engineering, Industrial Engineering, Materials Engineering, Mechanical...

  18. Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures

    Directory of Open Access Journals (Sweden)

    Erdi Tosun

    2016-12-01

    Full Text Available This study deals with usage of linear regression (LR and artificial neural network (ANN modeling to predict engine performance; torque and exhaust emissions; and carbon monoxide, oxides of nitrogen (CO, NOx of a naturally aspirated diesel engine fueled with standard diesel, peanut biodiesel (PME and biodiesel-alcohol (EME, MME, PME mixtures. Experimental work was conducted to obtain data to train and test the models. Backpropagation algorithm was used as a learning algorithm of ANN in the multilayered feedforward networks. Engine speed (rpm and fuel properties, cetane number (CN, lower heating value (LHV and density (ρ were used as input parameters in order to predict performance and emission parameters. It was shown that while linear regression modeling approach was deficient to predict desired parameters, more accurate results were obtained with the usage of ANN.

  19. An Engineering Approach to Management of Occupational and Community Noise Exposure at NASA Lewis Research Center

    Science.gov (United States)

    Cooper, Beth A.

    1997-01-01

    Workplace and environmental noise issues at NASA Lewis Research Center are effectively managed via a three-part program that addresses hearing conservation, community noise control, and noise control engineering. The Lewis Research Center Noise Exposure Management Program seeks to limit employee noise exposure and maintain community acceptance for critical research while actively pursuing engineered controls for noise generated by more than 100 separate research facilities and the associated services required for their operation.

  20. The Many Faces of a Software Engineer in a Research Community

    Energy Technology Data Exchange (ETDEWEB)

    Marinovici, Maria C.; Kirkham, Harold

    2013-10-14

    The ability to gather, analyze and make decisions based on real world data is changing nearly every field of human endeavor. These changes are particularly challenging for software engineers working in a scientific community, designing and developing large, complex systems. To avoid the creation of a communications gap (almost a language barrier), the software engineers should possess an ‘adaptive’ skill. In the science and engineering research community, the software engineers must be responsible for more than creating mechanisms for storing and analyzing data. They must also develop a fundamental scientific and engineering understanding of the data. This paper looks at the many faces that a software engineer should have: developer, domain expert, business analyst, security expert, project manager, tester, user experience professional, etc. Observations made during work on a power-systems scientific software development are analyzed and extended to describe more generic software development projects.

  1. Applying systems engineering to implement an evidence-based intervention at a community health center.

    Science.gov (United States)

    Tu, Shin-Ping; Feng, Sherry; Storch, Richard; Yip, Mei-Po; Sohng, HeeYon; Fu, Mingang; Chun, Alan

    2012-11-01

    Impressive results in patient care and cost reduction have increased the demand for systems-engineering methodologies in large health care systems. This Report from the Field describes the feasibility of applying systems-engineering techniques at a community health center currently lacking the dedicated expertise and resources to perform these activities.

  2. 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.

  3. 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

  4. 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…

  5. Community Needs Assessment for an Electronics and Computer Engineering Technology Program at Maui, Molokai, and Lanai.

    Science.gov (United States)

    Pezzoli, Jean A.

    In June 1992, Maui Community College (MCC), in Hawaii, conducted a survey of the communities of Maui, Molokai, Lanai, and Hana to determine perceived needs for an associate degree and certificate program in electronics and computer engineering. Questionnaires were mailed to 500 firms utilizing electronic or computer services, seeking information…

  6. RE-Wissen.de - a requirements engineering community portal in Germany

    OpenAIRE

    Adam, Sebastian; Doerr, Joerg; Eisenbarth, Michael

    2007-01-01

    RE-Wissen.de is a community portal to support enterprises in the area of requirements engineering by capturing and providing state-of-the-practice and state-of-the-art know-how and industrial experience. This paper provides an introduction to the community portal, its capabilities, challenges and future directions.

  7. 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.

  8. 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.

  9. Development of a Civil Engineer Corps Community Portal Prototype

    National Research Council Canada - National Science Library

    Rader, Neil

    2002-01-01

    ... of reference would be beneficial to all members, The community is wide spread and requires the ability to disseminate information as efficiently as possible to all corners of the world, Currently...

  10. Anomaly detection of turbopump vibration in Space Shuttle Main Engine using statistics and neural networks

    Science.gov (United States)

    Lo, C. F.; Wu, K.; Whitehead, B. A.

    1993-01-01

    The statistical and neural networks methods have been applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. The anomalies are detected based on the amplitude of peaks of fundamental and harmonic frequencies in the power spectral density. These data are reduced to the proper format from sensor data measured by strain gauges and accelerometers. Both methods are feasible to detect the vibration anomalies. The statistical method requires sufficient data points to establish a reasonable statistical distribution data bank. This method is applicable for on-line operation. The neural networks method also needs to have enough data basis to train the neural networks. The testing procedure can be utilized at any time so long as the characteristics of components remain unchanged.

  11. Treatment of spinal cord injury: a review of engineering using neural and mesenchymal stem cells.

    Science.gov (United States)

    Mortazavi, Martin M; Harmon, Olivia A; Adeeb, Nimer; Deep, Aman; Tubbs, R Shane

    2015-01-01

    Over time, various treatment modalities for spinal cord injury have been trialed, including pharmacological and nonpharmacological methods. Among these, replacement of the injured neural and paraneural tissues via cellular transplantation of neural and mesenchymal stem cells has been the most attractive. Extensive experimental studies have been done to identify the safety and effectiveness of this transplantation in animal and human models. Herein, we review the literature for studies conducted, with a focus on the human-related studies, recruitment, isolation, and transplantation, of these multipotent stem cells, and associated outcomes. © 2014 Wiley Periodicals, Inc.

  12. Learning Engineers to Reflect: Obstacles and Remedies in an Engineering Community

    Science.gov (United States)

    Aase, Karina; Olsen, Odd Einar; Pedersen, Cathrine

    2007-01-01

    The article reports results from a research facilitated learning project carried out in an engineering department in an oil and gas company. The objective of the project was to enhance an awareness of and the ability to use, dialogue and reflection-based learning approaches. The results document that the project-based engineering setting induces…

  13. 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…

  14. 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...

  15. Building Communities of Engineers to Share Technical Expertise

    Science.gov (United States)

    Topousis, Daria E.; Dennehy, Cornelius J.; Fesq, Lorraine M.

    2012-01-01

    Developed by the core community to describe our vision of an approach to ensure a sufficiently technically advanced and affordable AR&D technology base is available to support future NASA missions. The goal of this strategy is to create an environment exploiting reusable technology elements for an AR&D system design and development process which is: a) Lower-Risk. b) More Versatile/Scalable. c) Reliable & Crew-Safe. d) More Affordable.

  16. Engineering effect of Pinna nobilis shells on benthic communities

    OpenAIRE

    Rabaoui, Lotfi; Belgacem, Walid; Ben Ismail, Dorsaf; Mansour, Lamjed; Tlig-Zouari, Sabiha

    2015-01-01

    Within the framework of the possibility of using the Mediterranean pen shell Pinna nobilis in restoration and conservation plans of benthic habitats, an in situ experiment was conducted using empty P. nobilis shells. The latter were transplanted in a bare soft-bottomed area and their associated fauna were followed along 120 days and compared at different temporal points and with the assemblages living in the surrounding soft-sediment area. Compared to soft-sediment communities, an evidently i...

  17. Artificial neural networks as an engine of Internet based hypertension prediction tool.

    Science.gov (United States)

    Polak, Sebastian; Mendyk, Aleksander

    2004-01-01

    Hypertension is the most common cause of death. Therefore it is recognized as a major civilization disease next to diabetes, hyperuricemia, asthma etc. The objective was to use artificial neural networks (ANNs) to handle demographic data and to produce system of hypertension risk prediction. Database used in the development of hypertension risk model was obtained from CDC (BRFSS--Behavioral Risk Factor Surveillance System). Screening for optimal ANN architecture was performed among various backpropagation and fuzzy neural networks with use of 10-fold cross-validation scheme. Single ANNs as well as experts committees were tested. Best results were found to be around 75%--expressed as total classification rate. Java applet was designed to be the interface between ANN system and end user. Spreadsheet form was chosen to facilitate navigation and used by healthcare non-specialists. Free of charge Internet publication is expected soon at the address [url: see text].

  18. 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

  19. 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.

  20. 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.

  1. Theater as a Community-Building Strategy for Women in Engineering: Theory and Practice

    Science.gov (United States)

    Chesler, Naomi C.; Chesler, Mark A.

    Previously, the authors have suggested that peer mentoring through a caring community would improve the quality of life for female faculty members in engineering and could have a positive effect on retention and career advancement. Here, the authors present the background psychosocial literature for choosing participatory theater as a strategy to develop a caring community and report on a pilot study in which participatory theater activities were used within a workshop format for untenured female faculty members in engineering. The authors identify the key differences between participatory theater and other strategies for community building that may enhance participants' sense of commonality and the strength and utility of their community as a mentoring and support mechanism and discuss the ways in which these efforts could have a broader, longer term impact.

  2. 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.

  3. 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.

  4. 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...

  5. 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…

  6. 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.

  7. 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...

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

    CERN Document Server

    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.

  9. 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.

  10. 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

  11. Polypyrrole/Alginate Hybrid Hydrogels: Electrically Conductive and Soft Biomaterials for Human Mesenchymal Stem Cell Culture and Potential Neural Tissue Engineering Applications.

    Science.gov (United States)

    Yang, Sumi; Jang, LindyK; Kim, Semin; Yang, Jongcheol; Yang, Kisuk; Cho, Seung-Woo; Lee, Jae Young

    2016-11-01

    Electrically conductive biomaterials that can efficiently deliver electrical signals to cells or improve electrical communication among cells have received considerable attention for potential tissue engineering applications. Conductive hydrogels are desirable particularly for neural applications, as they can provide electrical signals and soft microenvironments that can mimic native nerve tissues. In this study, conductive and soft polypyrrole/alginate (PPy/Alg) hydrogels are developed by chemically polymerizing PPy within ionically cross-linked alginate hydrogel networks. The synthesized hydrogels exhibit a Young's modulus of 20-200 kPa. Electrical conductance of the PPy/Alg hydrogels could be enhanced by more than one order of magnitude compared to that of pristine alginate hydrogels. In vitro studies with human bone marrow-derived mesenchymal stem cells (hMSCs) reveal that cell adhesion and growth are promoted on the PPy/Alg hydrogels. Additionally, the PPy/Alg hydrogels support and greatly enhance the expression of neural differentiation markers (i.e., Tuj1 and MAP2) of hMSCs compared to tissue culture plate controls. Subcutaneous implantation of the hydrogels for eight weeks induces mild inflammatory reactions. These soft and conductive hydrogels will serve as a useful platform to study the effects of electrical and mechanical signals on stem cells and/or neural cells and to develop multifunctional neural tissue engineering scaffolds. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Engineered neural tissue with Schwann cell differentiated human dental pulp stem cells: potential for peripheral nerve repair?

    Science.gov (United States)

    Sanen, Kathleen; Martens, Wendy; Georgiou, Melanie; Ameloot, Marcel; Lambrichts, Ivo; Phillips, James

    2017-01-04

    Despite the spontaneous regenerative capacity of the peripheral nervous system, large gap peripheral nerve injuries (PNIs) require bridging strategies. The limitations and suboptimal results obtained with autografts or hollow nerve conduits in the clinic urge the need for alternative treatments. Recently, we have described promising neuroregenerative capacities of Schwann cells derived from differentiated human dental pulp stem cells (d-hDPSCs) in vitro. Here, we extended the in vitro assays to show the pro-angiogenic effects of d-hDPSCs, such as enhanced endothelial cell proliferation, migration and differentiation. In addition, for the first time we evaluated the performance of d-hDPSCs in an in vivo rat model of PNI. Eight weeks after transplantation of NeuraWrap™ conduits filled with engineered neural tissue (EngNT) containing aligned d-hDPSCs in 15-mm rat sciatic nerve defects, immunohistochemistry and ultrastructural analysis revealed ingrowing neurites, myelinated nerve fibres and blood vessels along the construct. Although further research is required to optimize the delivery of this EngNT, our findings suggest that d-hDPSCs are able to exert a positive effect in the regeneration of nerve tissue in vivo. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

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

    Energy Technology Data Exchange (ETDEWEB)

    Li, Chun-Hsien, E-mail: chli@nknucc.nknu.edu.tw [Department of Mathematics, National Kaohsiung Normal University, Yanchao District, Kaohsiung City 82444, Taiwan (China); Yang, Suh-Yuh, E-mail: syyang@math.ncu.edu.tw [Department of Mathematics, National Central University, Jhongli City, Taoyuan County 32001, Taiwan (China)

    2014-03-01

    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.

  14. Error and attack tolerance of synchronization in Hindmarsh-Rose neural networks with community structure

    Science.gov (United States)

    Li, Chun-Hsien; Yang, Suh-Yuh

    2014-03-01

    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.

  15. 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.

  16. 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

  17. Developing and assessing a holistic living-learning community for engineering and science freshmen

    Science.gov (United States)

    Light, Jennifer

    Learning communities and their strategies for enrolling cohort groups of students in a common set of classes organized around a theme or linked with residence life have come to light over the past twenty years. However, living-learning communities (LLC) and their role in retention, engagement, and intellectual development for engineering and science students have yet to be fully explored. What aspects of a LLC are most beneficial to science and engineering students? What are the learning needs of engineering and science students that are best met with LLCs? These questions were the basis for assessment of a new LLC program developed at Washington State University specifically to increase retention, academic achievement, and engagement of engineering and science students. A first-year semester-long pilot LLC program was developed at Washington State University specifically for entering engineering majors. The program was expanded the following year to include biotech science majors. The first LLC had 55 self-selected engineering participants. Students were housed in the same residence hall, registered for three common classes, and participated in a non-credit bearing weekly peer-facilitated study group. The second year 81 students self-selected into the program; 59 engineering and 22 biotech majors. Students were housed in a common residence hall and registered for three common classes. Students participated in a two-credit freshman seminar class instead of the once-weekly study group used the previous year. Results indicate students were engaged with peers and in college activities, had mixed academic improvement, and engineering students were retained at higher rates in their major when compared to non-participating peers and biotech participants. Second year LLC students had higher grade averages than comparison peers despite lower incoming preparedness. Higher engagement levels were confirmed by triangulation with national survey comparisons, observations, focus

  18. 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.

  19. Star poly(ethylene glycol) as a tunable scaffold for neural tissue engineering

    Science.gov (United States)

    Zustiak, Silviya Petrova

    The primary focus of this work was to develop a novel synthetic hydrogel scaffold as an in vitro model to enable future detailed studies of how neurons grow in environments with controllable diffusion profiles of soluble cues and tunable neuronmatrix interactions. The development of in vitro models that enable elucidation of the mechanisms of system performance is a recently emerging goal of tissue engineering. The design of three-dimensional (3D) scaffolds in particular, is motivated by the need to develop model systems that better mimic native tissue as compared to conventional two-dimensional (2D) cell culture substrates. An ideal scaffold is degradable, porous, biocompatible, with mechanical properties to match those of the tissues of interest and with a suitable surface chemistry for cell attachment, proliferation, and differentiation. Although naturally derived materials are more versatile in providing complex biological cues, synthetic polymers are preferable for the design of in vitro models as they provide wider range of properties, controllable degradation rates, and easier processing. Most importantly, their mechanical properties can be decoupled from their biological properties, a crucial issue in interpreting cell responses. The synthetic material provides the structural backbone of the scaffold while biochemical function is added via incorporation of ligands or proteins aimed at triggering specific cell behaviors. As presented in this dissertation, we have developed and characterized a new synthetic 3D hydrogel scaffold from cross-linked poly(ethylene glycol) (PEG). PEG was selected because it is hydrophilic, non-toxic, biocompatible, and inert to protein adhesion. The chosen cross-linking chemistry was a highly specific reaction that occurred under physiological conditions so that cells could be embedded within the gel prior to cross-linking. Controllable degradability was imparted via series of hydrolytically degradable PEG cross-linkers. Thorough

  20. 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.

  1. 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.

  2. 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.

  3. Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling.

    Science.gov (United States)

    Struzyna, Laura A; Adewole, Dayo O; Gordián-Vélez, Wisberty J; Grovola, Michael R; Burrell, Justin C; Katiyar, Kritika S; Petrov, Dmitriy; Harris, James P; Cullen, D Kacy

    2017-05-31

    Functional recovery rarely occurs following injury or disease-induced degeneration within the central nervous system (CNS) due to the inhibitory environment and the limited capacity for neurogenesis. We are developing a strategy to simultaneously address neuronal and axonal pathway loss within the damaged CNS. This manuscript presents the fabrication protocol for micro-tissue engineered neural networks (micro-TENNs), implantable constructs consisting of neurons and aligned axonal tracts spanning the extracellular matrix (ECM) lumen of a preformed hydrogel cylinder hundreds of microns in diameter that may extend centimeters in length. Neuronal aggregates are delimited to the extremes of the three-dimensional encasement and are spanned by axonal projections. Micro-TENNs are uniquely poised as a strategy for CNS reconstruction, emulating aspects of brain connectome cytoarchitecture and potentially providing means for network replacement. The neuronal aggregates may synapse with host tissue to form new functional relays to restore and/or modulate missing or damaged circuitry. These constructs may also act as pro-regenerative "living scaffolds" capable of exploiting developmental mechanisms for cell migration and axonal pathfinding, providing synergistic structural and soluble cues based on the state of regeneration. Micro-TENNs are fabricated by pouring liquid hydrogel into a cylindrical mold containing a longitudinally centered needle. Once the hydrogel has gelled, the needle is removed, leaving a hollow micro-column. An ECM solution is added to the lumen to provide an environment suitable for neuronal adhesion and axonal outgrowth. Dissociated neurons are mechanically aggregated for precise seeding within one or both ends of the micro-column. This methodology reliably produces self-contained miniature constructs with long-projecting axonal tracts that may recapitulate features of brain neuroanatomy. Synaptic immunolabeling and genetically encoded calcium

  4. 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.

  5. Neural stem cell differentiation by electrical stimulation using a cross-linked PEDOT substrate: Expanding the use of biocompatible conjugated conductive polymers for neural tissue engineering.

    Science.gov (United States)

    Pires, Filipa; Ferreira, Quirina; Rodrigues, Carlos A V; Morgado, Jorge; Ferreira, Frederico Castelo

    2015-06-01

    The use of conjugated polymers allows versatile interactions between cells and flexible processable materials, while providing a platform for electrical stimulation, which is particularly relevant when targeting differentiation of neural stem cells and further application for therapy or drug screening. Materials were tested for cytotoxicity following the ISO10993-5. PSS was cross-linked. ReNcellVM neural stem cells (NSC) were seeded in laminin coated surfaces, cultured for 4 days in the presence of EGF (20 ng/mL), FGF-2 (20 ng/mL) and B27 (20 μg/mL) and differentiated over eight additional days in the absence of those factors under 100Hz pulsed DC electrical stimulation, 1V with 10 ms pulses. NSC and neuron elongation aspect ratio as well as neurite length were assessed using ImageJ. Cells were immune-stained for Tuj1 and GFAP. F8T2, MEH-PPV, P3HT and cross-linked PSS (x PSS) were assessed as non-cytotoxic. L929 fibroblast population was 1.3 higher for x PSS than for glass control, while F8T2 presents moderate proliferation. The population of neurons (Tuj1) was 1.6 times higher with longer neurites (73 vs 108 μm) for cells cultured under electrical stimulus, with cultured NSC. Such stimulus led also to longer neurons. x PSS was, for the first time, used to elongate human NSC through the application of pulsed current, impacting on their differentiation towards neurons and contributing to longer neurites. The range of conductive conjugated polymers known as non-cytotoxic was expanded. x PSS was introduced as a stable material, easily processed from solution, to interface with biological systems, in particular NSC, without the need of in-situ polymerization. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. 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

  7. 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

    2015-06-01

    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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. 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.

  9. 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

  10. 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.

  11. Enabling Innovation and Collaboration Across Geography and Culture: A Case Study of NASA's Systems Engineering Community of Practice

    Science.gov (United States)

    Topousis, Daria E.; Murphy, Keri; Robinson, Greg

    2008-01-01

    In 2004, NASA faced major knowledge sharing challenges due to geographically isolated field centers that inhibited personnel from sharing experiences and ideas. Mission failures and new directions for the agency demanded better collaborative tools. In addition, with the push to send astronauts back to the moon and to Mars, NASA recognized that systems engineering would have to improve across the agency. Of the ten field centers, seven had not built a spacecraft in over 30 years, and had lost systems engineering expertise. The Systems Engineering Community of Practice came together to capture the knowledge of its members using the suite of collaborative tools provided by the NASA Engineering Network (NEN.) The NEN provided a secure collaboration space for over 60 practitioners across the agency to assemble and review a NASA systems engineering handbook. Once the handbook was complete, they used the open community area to disseminate it. This case study explores both the technology and the social networking that made the community possible, describes technological approaches that facilitated rapid setup and low maintenance, provides best practices that other organizations could adopt, and discusses the vision for how this community will continue to collaborate across the field centers to benefit the agency as it continues exploring the solar system.

  12. eLoom and Flatland: specification, simulation and visualization engines for the study of arbitrary hierarchical neural architectures.

    Science.gov (United States)

    Caudell, Thomas P; Xiao, Yunhai; Healy, Michael J

    2003-01-01

    eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. Visualization is a useful method for understanding both learning and computation in artificial neural networks. Through 3D animated pictorially representations of the state and flow of information in the network, a better understanding of network functionality is achieved. ART-1, LAPART-II, MLP, and SOM neural networks are presented to illustrate eLoom and Flatland's capabilities.

  13. Engineering Human Microbiota: Influencing Cellular and Community Dynamics for Therapeutic Applications.

    Science.gov (United States)

    Woloszynek, S; Pastor, S; Mell, J C; Nandi, N; Sokhansanj, B; Rosen, G L

    2016-01-01

    The complex relationship between microbiota, human physiology, and environmental perturbations has become a major research focus, particularly with the arrival of culture-free and high-throughput approaches for studying the microbiome. Early enthusiasm has come from results that are largely correlative, but the correlative phase of microbiome research has assisted in defining the key questions of how these microbiota interact with their host. An emerging repertoire for engineering the microbiome places current research on a more experimentally grounded footing. We present a detailed look at the interplay between microbiota and host and how these interactions can be exploited. A particular emphasis is placed on unstable microbial communities, or dysbiosis, and strategies to reestablish stability in these microbial ecosystems. These include manipulation of intermicrobial communication, development of designer probiotics, fecal microbiota transplantation, and synthetic biology. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Novel high-viscosity polyacrylamidated chitosan for neural tissue engineering: fabrication of anisotropic neurodurable scaffold via molecular disposition of persulfate-mediated polymer slicing and complexation.

    Science.gov (United States)

    Kumar, Pradeep; Choonara, Yahya E; du Toit, Lisa C; Modi, Girish; Naidoo, Dinesh; Pillay, Viness

    2012-10-29

    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, superior hydrophilicity as well as

  15. 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

  16. P01.22GENERATION OF GENETICALLY ENGINEERED INDUCED PLURIPOTENT STEM CELL-DERIVED NEURAL STEM CELLS WITHOUT USING VIRAL VECTORS

    Science.gov (United States)

    Yamasaki, T.; Kawaji, H.; Kamio, Y.; Amano, S.; Sameshima, T.; Sakai, N.; Tokuyama, T.; Namba, H.

    2014-01-01

    Suicide gene therapy using genetically engineered stem cells are is one of the most feasible and promising approaches for glioma therapy. Various stem cells, such as neural and mesenchymal stem cells, have been tested for their tumor tropic activity. We have tested stem cells transduced with the herpes simplex virus-thymidine kinase gene (HSV-TK, suicide gene) encoding a viral thymidine kinase which phosphorylates prodrug ganciclovir (GCV) for experimental glioma in rodents, because the HSV-TK/GCV system generates a potent bystander effect. Recently we found that induced pluripotent stem cells (iPSs) also had tumor tropic activity under both in vitro and in vivo conditions. One of the major limitations of use of HSV-TK gene-trasnduced iPSs in clinical field is the use of virus vectors that randomly integrate into the host genome and are associated with the risk of malignant transformation due to insertional mutagenesis. In the present study, we present a non-viral transfection method for obtaining HSV-TK expressing iPS-derived neural stem cells to circumvent the concerns associated with use of viral vectors. Mouse iPS cells were generated from somatic cells by the plasmid vectors expressing Oct4, Sox2, Klf4, c-Myc and Nanog-GFP-IRES-Puror. We differentiated the mouse iPS cells into neural stem cells and then transfected with the plasmid containing HSV-TK gene using electroporation method. In this way, we obtained HSV-TK gene-trasnduced iPSs-derived neural stem cells without using viral vectors for further pre-clinical expariments of glioma therapy.

  17. 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...

  18. 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.

    2017-07-01

    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.

  19. 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.

  20. Living Learning Communities: An Intervention in Keeping Women Strong in Science, Technology, Engineering, and Mathematics

    Science.gov (United States)

    Belichesky, Jennifer

    The purpose of this study was to expand on the current research pertaining to women in science, technology, engineering, and mathematics (STEM) majors, better understand the experiences of undergraduate women in the sciences, identify barriers to female persistence in their intended STEM majors, and understand the impact of the STEM co-educational Living Learning Community (LLC) model on female persistence. This study employed a mixed-methods approach that was grounded in standpoint methodology. The qualitative data were collected through focus groups and one-on-one interviews with the female participants and was analyzed through a critical feminist lens utilizing standpoint methodology and coded utilizing inductive analysis. The quantitative data were collected and analyzed utilizing a simple statistical analysis of key academic variables indicative of student success: cumulative high school GPAs, SAT scores, first year cumulative GPAs, freshman persistence patterns in the intended major, and freshman retention patterns at the university. The findings of this study illustrated that the co-educational LLC model created an inclusive academic and social environment that positively impacted the female participants' experiences and persistence in STEM. The findings also found the inclusion of men in the community aided in the demystification of male superiority in the sciences for the female participants. This study also highlighted the significance of social identity in the decision making process to join a science LLC.

  1. Combustion Analysis of a CI Engine Performance Using Waste Cooking Biodiesel Fuel with an Artificial Neural Network Aid

    OpenAIRE

    Najafi, Gholamhassan; Ghobadian, Barat; TALAL F. YUSAF; Hadi RAHIMI

    2007-01-01

    A comprehensive combustion analysis has been conducted to evaluate the performance of a commercial DI engine, water cooled two cylinders, in-line, naturally aspirated, RD270 Ruggerini diesel engine using waste vegetable cooking oil as an alternative fuel. In order to compare the brake power and the torques values of the engine, it has been tested under same operating conditions with diesel fuel and waste cooking biodiesel fuel blends. The results were found to be very comparable. The properti...

  2. EDITORIAL: Focus on the neural interface Focus on the neural interface

    Science.gov (United States)

    Durand, Dominique M.

    2009-10-01

    The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that

  3. 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…

  4. Fabrication of Nerve Growth Factor Encapsulated Aligned Poly(ε-Caprolactone Nanofibers and Their Assessment as a Potential Neural Tissue Engineering Scaffold

    Directory of Open Access Journals (Sweden)

    Jue Hu

    2016-02-01

    Full Text Available Peripheral nerve injury is a serious clinical problem to be solved. There has been no breakthrough so far and neural tissue engineering offers a promising approach to promote the regeneration of peripheral neural injuries. In this study, emulsion electrospinning technique was introduced as a flexible and promising technique for the fabrication of random (R and aligned (A Poly(ε-caprolactone (PCL-Nerve Growth Factor (NGF&Bovine Serum Albumin (BSA nanofibrous scaffolds [(R/A-PCL-NGF&BSA], where NGF and BSA were encapsulated in the core while PCL form the shell. Random and aligned pure PCL, PCL-BSA, and PCL-NGF nanofibers were also produced for comparison. The scaffolds were characterized by Field Emission Scanning Electron Microscopy (FESEM and water contact angle test. Release study showed that, with the addition of stabilizer BSA, a sustained release of NGF from emulsion electrospun PCL nanofibers was observed over 28 days. [3-(4,5-dimethylthiazol-2-yl-5-(3-carboxymethoxyphenyl-2-(4-sulfophenyl-2H-tetrazolium, inner salt; MTS] assay revealed that (R/A-PCL-NGF and (R/A-PCL-NGF&BSA scaffolds favored cell growth and showed no cytotoxicity to PC12 cells. Laser scanning confocal microscope images exhibited that the A-PCL-NGF&BSA scaffold increased the length of neurites and directed neurites extension along the fiber axis, indicating that the A-PCL-NGF&BSA scaffold has a potential for guiding nerve tissue growth and promoting nerve regeneration.

  5. 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.

  6. Community grand rounds: re-engineering community and academic partnerships in health education-a partnership and programmatic evaluation.

    Science.gov (United States)

    Heaton, Kevin; Smith, George R; King, Kimberly; Watson, Natalie; Brown, Jen; Curry, Gina; Johnson, Brandon; Nichols, Betty; Pearson, Bernetta; Sanders, Ernest; Sanders, Norma; Miller, Doriane

    2014-01-01

    Community participation in population health improvement can assist university researchers in targeting intervention resources more effectively and efficiently, leading to more effective implementation of interventions, because of joint ownership of both process and product. Two academic health centers partnered with community based organizations to develop a bidirectional educational seminar series called "Community Grand Rounds" (CGR), which identified health concerns of Chicago's South Side residents and provided information regarding university and community resources that addressed community health concerns. We evaluated the community consultants' perceptions of the quality and effectiveness of the planning and implementation of the seminars that resulted from the partnership. We conducted one-on-one interviews and focus groups with community consultants to assess their perceptions of the partnership using a tailored version of a previously developed individual and focus group interview instrument. Analysis of the interview text was conducted using grounded theory where themes were coded as they emerged. CGR is an effective mechanism for providing needed community health information in an easily accessible format. Additional work is needed to determine whether this format represents a sustainable community-university partnership.

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

    National Research Council Canada - National Science Library

    Pauline Mosley; Yun Liu; S Keith Hargrove; Jayfus T Doswell

    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...

  8. Comparison of Artificial Neural Network (ANN Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium

    Directory of Open Access Journals (Sweden)

    Andy P. Dedecker

    2002-01-01

    Full Text Available Modelling has become an interesting tool to support decision making in water management. River ecosystem modelling methods have improved substantially during recent years. New concepts, such as artificial neural networks, fuzzy logic, evolutionary algorithms, chaos and fractals, cellular automata, etc., are being more commonly used to analyse ecosystem databases and to make predictions for river management purposes. In this context, artificial neural networks were applied to predict macroinvertebrate communities in the Zwalm River basin (Flanders, Belgium. Structural characteristics (meandering, substrate type, flow velocity and physical and chemical variables (dissolved oxygen, pH were used as predictive variables to predict the presence or absence of macroinvertebrate taxa in the headwaters and brooks of the Zwalm River basin. Special interest was paid to the frequency of occurrence of the taxa as well as the selection of the predictors and variables to be predicted on the prediction reliability of the developed models. Sensitivity analyses allowed us to study the impact of the predictive variables on the prediction of presence or absence of macroinvertebrate taxa and to define which variables are the most influential in determining the neural network outputs.

  9. 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...

  10. Significance of microbial communities and interactions in safeguarding reactive mine tailings by ecological engineering.

    Science.gov (United States)

    Nancucheo, Ivan; Johnson, D Barrie

    2011-12-01

    Pyritic mine tailings (mineral waste generated by metal mining) pose significant risk to the environment as point sources of acidic, metal-rich effluents (acid mine drainage [AMD]). While the accelerated oxidative dissolution of pyrite and other sulfide minerals in tailings by acidophilic chemolithotrophic prokaryotes has been widely reported, other acidophiles (heterotrophic bacteria that catalyze the dissimilatory reduction of iron and sulfur) can reverse the reactions involved in AMD genesis, and these have been implicated in the "natural attenuation" of mine waters. We have investigated whether by manipulating microbial communities in tailings (inoculating with iron- and sulfur-reducing acidophilic bacteria and phototrophic acidophilic microalgae) it is possible to mitigate the impact of the acid-generating and metal-mobilizing chemolithotrophic prokaryotes that are indigenous to tailing deposits. Sixty tailings mesocosms were set up, using five different microbial inoculation variants, and analyzed at regular intervals for changes in physicochemical and microbiological parameters for up to 1 year. Differences between treatment protocols were most apparent between tailings that had been inoculated with acidophilic algae in addition to aerobic and anaerobic heterotrophic bacteria and those that had been inoculated with only pyrite-oxidizing chemolithotrophs; these differences included higher pH values, lower redox potentials, and smaller concentrations of soluble copper and zinc. The results suggest that empirical ecological engineering of tailing lagoons to promote the growth and activities of iron- and sulfate-reducing bacteria could minimize their risk of AMD production and that the heterotrophic populations could be sustained by facilitating the growth of microalgae to provide continuous inputs of organic carbon.

  11. 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

  12. Making Leaders: Leadership Characteristics of Makers and Engineers in the Maker Community

    Science.gov (United States)

    Oplinger, James; Lande, Micah; Jordan, Shawn; Camarena, Leonor

    2016-01-01

    This study examines the emergence of leadership characteristics within a new organizational community of individuals: the Maker community. The Maker community is a group of individuals that classify themselves as "Makers" and have become innovators and entrepreneurs through the creation of technological gadgets, artistic projects, and…

  13. The effects of neural mobilization in addition to standard care in persons with carpal tunnel syndrome from a community hospital.

    Science.gov (United States)

    Heebner, Michelle L; Roddey, Toni S

    2008-01-01

    The purpose of this study was to determine whether neural mobilization in addition to standard care is more effective than standard care alone in the treatment of Carpal Tunnel Syndrome (CTS). Sixty participants were randomly assigned to one of two groups. Group 1 received standard care, and Group 2 performed a neurodynamic mobilization exercise in addition to standard care. Outcomes were assessed at baseline and at one and six months using the Disabilities of the Arm, Shoulder, and Hand Questionnaire, the Brigham and Woman's Hospital Carpal Tunnel Specific Questionnaire (CTSQ), and elbow extension range of motion during an upper limb median nerve tension test. There were no significant differences in the outcome measures between groups, except Group 1 had improved scores on the function status scale of the CTSQ compared to Group 2 at six months. The addition of neural mobilization to standard care did not result in improved outcomes in patients with CTS.

  14. 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

    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...

  15. Towards a global virtual community of female engineering students and professionals

    Science.gov (United States)

    Cotel, Aline; Rimer, Sara; Reddivari, Sahithya

    2014-11-01

    ct- The need for strategies to empower Liberian women is exemplified in the recent study carried out by ActionAid International, which examined the state of Liberian undergraduate women in urban areas. The results show that these women often face sexual intimidation by faculty and instructors, women are often excluded from student organizations, there exists a lack of institutional support for female organizations at the universities, and that the women do not feel safe in the university due to low security standards. The situation is even direr for the female engineering students with less than 5% of the engineering student population being women, therefore they are quite isolated in their engineering studies with minimal role models and professional support as they persist. We have planned a leadership camp for female Liberian engineering undergraduate women. The ultimate goal is to empower the Liberian women engineers with the skills, support and inspiration necessary to becoming successful engineering professionals. The leadership camp is planned and facilitated collaboratively by the members of the University of Michigan Society of Women Engineers (UM-SWE) student chapter and the Liberia Society of Women Engineers (L-SWE) student organization. The 2 week-long leadership camp has a workshop-based format with two themes: (i) academic and professional skills, and (ii) student organization development. Funded by UM CRLT, IRWG, STEM Africa.

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

    Science.gov (United States)

    2014-08-01

    302–313. Chapman M. G. and D. J. Blockley. 2009. Engineering novel habitats on urban infrastructure to increase intertidal biodiversity . Oecologia...Ecologically informed engineering reduces loss of intertidal biodiversity on artificial shorelines. Environ. Sci. Technol. 45:8204–8207. Chapman M. G. and A

  17. 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…

  18. 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…

  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. 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.

  1. Neural Crest Cell Implantation Restores Enteric Nervous System Function and Alters the Gastrointestinal Transcriptome in Human Tissue-Engineered Small Intestine.

    Science.gov (United States)

    Schlieve, Christopher R; Fowler, Kathryn L; Thornton, Matthew; Huang, Sha; Hajjali, Ibrahim; Hou, Xiaogang; Grubbs, Brendan; Spence, Jason R; Grikscheit, Tracy C

    2017-09-12

    Acquired or congenital disruption in enteric nervous system (ENS) development or function can lead to significant mechanical dysmotility. ENS restoration through cellular transplantation may provide a cure for enteric neuropathies. We have previously generated human pluripotent stem cell (hPSC)-derived tissue-engineered small intestine (TESI) from human intestinal organoids (HIOs). However, HIO-TESI fails to develop an ENS. The purpose of our study is to restore ENS components derived exclusively from hPSCs in HIO-TESI. hPSC-derived enteric neural crest cell (ENCC) supplementation of HIO-TESI establishes submucosal and myenteric ganglia, repopulates various subclasses of neurons, and restores neuroepithelial connections and neuron-dependent contractility and relaxation in ENCC-HIO-TESI. RNA sequencing identified differentially expressed genes involved in neurogenesis, gliogenesis, gastrointestinal tract development, and differentiated epithelial cell types when ENS elements are restored during in vivo development of HIO-TESI. Our findings validate an effective approach to restoring hPSC-derived ENS components in HIO-TESI and may implicate their potential for the treatment of enteric neuropathies. Published by Elsevier Inc.

  2. Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

    Science.gov (United States)

    Graham, Emily B; Knelman, Joseph E; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J M; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C; Glanville, Helen C; Jones, Davey L; Angel, Roey; Salminen, Janne; Newton, Ryan J; Bürgmann, Helmut; Ingram, Lachlan J; Hamer, Ute; Siljanen, Henri M P; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C; Lopes, Ana R; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S; Basiliko, Nathan; Nemergut, Diana R

    2016-01-01

    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.

  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. 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.

  5. 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.

  6. Volunteer Expert Readers: Drawing on the University Community to Provide Professional Feedback for Engineering Student Writers

    Science.gov (United States)

    Moskovitz, Cary

    2017-01-01

    This paper reports on a 3-year study utilizing a novel approach to providing students in an introductory engineering course with feedback on drafts of course writing projects. In the Volunteer Expert Reader (VER) approach, students are matched with university alumni or employees who have the background to give feedback from the perspective of the…

  7. 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,

  8. Neural Oscillators Programming Simplified

    Directory of Open Access Journals (Sweden)

    Patrick McDowell

    2012-01-01

    Full Text Available The neurological mechanism used for generating rhythmic patterns for functions such as swallowing, walking, and chewing has been modeled computationally by the neural oscillator. It has been widely studied by biologists to model various aspects of organisms and by computer scientists and robotics engineers as a method for controlling and coordinating the gaits of walking robots. Although there has been significant study in this area, it is difficult to find basic guidelines for programming neural oscillators. In this paper, the authors approach neural oscillators from a programmer’s point of view, providing background and examples for developing neural oscillators to generate rhythmic patterns that can be used in biological modeling and robotics applications.

  9. 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

  10. Assessment for Community Service Types of Experiential Learning in the Engineering Discipline

    Science.gov (United States)

    Chan, Cecilia Ka Yuk

    2012-01-01

    While experiential learning has been increasingly explored and adopted by higher education institutions, few have researched the appropriate assessment methods that can be aligned with the learning outcomes of experiential learning. A literature review on the diverse forms of assessment currently used for community service types of experiential…

  11. Impacts of Pristine and Transformed Ag and Cu Engineered Nanomaterials on Surficial Sediment Microbial Communities Appear Short-Lived.

    Science.gov (United States)

    Moore, Joe D; Stegemeier, John P; Bibby, Kyle; Marinakos, Stella M; Lowry, Gregory V; Gregory, Kelvin B

    2016-03-01

    Laboratory-based studies have shown that many soluble metal and metal oxide engineered nanomaterials (ENM) exert strong toxic effects on microorganisms. However, laboratory-based studies lack the complexity of natural systems and often use "as manufactured" ENMs rather than more environmentally relevant transformed ENMs, leaving open the question of whether natural ligands and seasonal variation will mitigate ENM impacts. Because ENMs will accumulate in subaquatic sediments, we examined the effects of pristine and transformed Ag and Cu ENMs on surficial sediment microbial communities in simulated freshwater wetlands. Five identical mesocosms were dosed through the water column with either Ag(0), Ag2S, CuO or CuS ENMs (nominal sizes of 4.67 ± 1.4, 18.1 ± 3.2, 31.1 ± 12, and 12.4 ± 4.1, respectively) or Cu(2+). Microbial communities were examined at 0, 7, 30, 90, 180, and 300 d using qPCR and high-throughput 16S rRNA gene sequencing. Results suggest differential short-term impacts of Ag(0) and Ag2S, similarities between CuO and CuS, and differences between Cu ENMs and Cu(2+). PICRUSt-predicted metagenomes displayed differential effects of Ag treatments on photosynthesis and of Cu treatments on methane metabolism. By 300 d, all metrics pointed to reconvergence of ENM-dosed mesocosm microbial community structure and composition, suggesting that the long-term microbial community impacts from a pulse of Ag or Cu ENMs are limited.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. The development of Sustainability Graduate Community (SGC) as a learning pathway for sustainability education - a framework for engineering programmes in Malaysia Technical Universities Network (MTUN)

    Science.gov (United States)

    Johan, Kartina; Mohd Turan, Faiz

    2016-11-01

    ‘Environmental and sustainability’ is one of the Program Outcome (PO) designated by the Board of Engineers Malaysia (BEM) as one of the accreditation program requirement. However, to-date the implementation of sustainability elements in engineering programme in the technical universities in Malaysia is within individual faculty's curriculum plan and lack of university-level structured learning pathway, which enable all students to have access to an education in sustainability across all disciplines. Sustainability Graduate Community (SGC) is a framework designed to provide a learning pathway in the curriculum of engineering programs to inculcate sustainability education among engineering graduates. This paper aims to study the required attributes in Sustainability Graduate Community (SGC) framework to produce graduates who are not just engineers but also skilful in sustainability competencies using Global Project Management (GPM) P5 Standard for Sustainability. The development of the conceptual framework is to provide a constructive teaching and learning plan for educators and policy makers to work on together in developing the Sustainability Graduates (SG), the new kind of graduates from Malaysia Technical Universities Network (MTUN) in Malaysia who are literate in sustainability practices. The framework also support the call for developing holistic students based on Malaysian Education Blueprint (Higher Education) and address the gap between the statuses of engineering qualification to the expected competencies from industries in Malaysia in particular by achieving the SG attributes outlined in the framework

  17. 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.

  18. Graphene-based materials for tissue engineering.

    Science.gov (United States)

    Shin, Su Ryon; Li, Yi-Chen; Jang, Hae Lin; Khoshakhlagh, Parastoo; Akbari, Mohsen; Nasajpour, Amir; Zhang, Yu Shrike; Tamayol, Ali; Khademhosseini, Ali

    2016-10-01

    Graphene and its chemical derivatives have been a pivotal new class of nanomaterials and a model system for quantum behavior. The material's excellent electrical conductivity, biocompatibility, surface area and thermal properties are of much interest to the scientific community. Two-dimensional graphene materials have been widely used in various biomedical research areas such as bioelectronics, imaging, drug delivery, and tissue engineering. In this review, we will highlight the recent applications of graphene-based materials in tissue engineering and regenerative medicine. In particular, we will discuss the application of graphene-based materials in cardiac, neural, bone, cartilage, skeletal muscle, and skin/adipose tissue engineering. We will also discuss the potential risk factors of graphene-based materials in tissue engineering. In conclusion, we will outline the opportunities in the usage of graphene-based materials for clinical applications. Published by Elsevier B.V.

  19. 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.

  20. 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...

  1. Biologically Inspired Modular Neural Networks

    OpenAIRE

    Azam, Farooq

    2000-01-01

    This dissertation explores the modular learning in artificial neural networks that mainly driven by the inspiration from the neurobiological basis of the human learning. The presented modularization approaches to the neural network design and learning are inspired by the engineering, complexity, psychological and neurobiological aspects. The main theme of this dissertation is to explore the organization and functioning of the brain to discover new structural and learning ...

  2. 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...... examined, and it appears that considering 'normal' neural network models with, say, 500 samples, the problem of over-fitting is neglible, and therefore it is not taken into consideration afterwards. Numerous model types, often met in control applications, are implemented as neural network models...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  3. 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.

  4. 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.

  5. Community

    African Journals Online (AJOL)

    study community related the mode of transmíssion to the bite of infective mosquitoes and 43.7% of ... mosquitoes. Mosquitoes are mainly believed to bite human beings at night (73,2%), breed in stagnant water (71%) and rest in dark places inside houses during daytime (44.3%). ... Maize and enset (false banana) are the.

  6. 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...

  7. 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...

  8. 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

  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. 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.

  11. 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

  12. 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.

  13. Neural network-based estimates of Southern Ocean net community production from in situ O2 / Ar and satellite observation: a methodological study

    Science.gov (United States)

    Chang, C.-H.; Johnson, N. C.; Cassar, N.

    2014-06-01

    Southern Ocean organic carbon export plays an important role in the global carbon cycle, yet its basin-scale climatology and variability are uncertain due to limited coverage of in situ observations. In this study, a neural network approach based on the self-organizing map (SOM) is adopted to construct weekly gridded (1° × 1°) maps of organic carbon export for the Southern Ocean from 1998 to 2009. The SOM is trained with in situ measurements of O2 / Ar-derived net community production (NCP) that are tightly linked to the carbon export in the mixed layer on timescales of one to two weeks and with six potential NCP predictors: photosynthetically available radiation (PAR), particulate organic carbon (POC), chlorophyll (Chl), sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD). This nonparametric approach is based entirely on the observed statistical relationships between NCP and the predictors and, therefore, is strongly constrained by observations. A thorough cross-validation yields three retained NCP predictors, Chl, PAR, and MLD. Our constructed NCP is further validated by good agreement with previously published, independent in situ derived NCP of weekly or longer temporal resolution through real-time and climatological comparisons at various sampling sites. The resulting November-March NCP climatology reveals a pronounced zonal band of high NCP roughly following the Subtropical Front in the Atlantic, Indian, and western Pacific sectors, and turns southeastward shortly after the dateline. Other regions of elevated NCP include the upwelling zones off Chile and Namibia, the Patagonian Shelf, the Antarctic coast, and areas surrounding the Islands of Kerguelen, South Georgia, and Crozet. This basin-scale NCP climatology closely resembles that of the satellite POC field and observed air-sea CO2 flux. The long-term mean area-integrated NCP south of 50° S from our dataset, 17.9 mmol C m-2 d-1, falls within the range of 8.3 to 24 mmol

  14. Neural network-based estimates of Southern Ocean net community production from in-situ O2 / Ar and satellite observation: a methodological study

    Science.gov (United States)

    Chang, C.-H.; Johnson, N. C.; Cassar, N.

    2013-10-01

    Southern Ocean organic carbon export plays an important role in the global carbon cycle, yet its basin-scale climatology and variability are uncertain due to limited coverage of in situ observations. In this study, a neural network approach based on the self-organizing map (SOM) is adopted to construct weekly gridded (1° × 1°) maps of organic carbon export for the Southern Ocean from 1998 to 2009. The SOM is trained with in situ measurements of O2 / Ar-derived net community production (NCP) that are tightly linked to the carbon export in the mixed layer on timescales of 1-2 weeks, and six potential NCP predictors: photosynthetically available radiation (PAR), particulate organic carbon (POC), chlorophyll (Chl), sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD). This non-parametric approach is based entirely on the observed statistical relationships between NCP and the predictors, and therefore is strongly constrained by observations. A thorough cross-validation yields three retained NCP predictors, Chl, PAR, and MLD. Our constructed NCP is further validated by good agreement with previously published independent in situ derived NCP of weekly or longer temporal resolution through real-time and climatological comparisons at various sampling sites. The resulting November-March NCP climatology reveals a pronounced zonal band of high NCP roughly following the subtropical front in the Atlantic, Indian and western Pacific sectors, and turns southeastward shortly after the dateline. Other regions of elevated NCP include the upwelling zones off Chile and Namibia, Patagonian Shelf, Antarctic coast, and areas surrounding the Islands of Kerguelen, South Georgia, and Crozet. This basin-scale NCP climatology closely resembles that of the satellite POC field and observed air-sea CO2 flux. The long-term mean area-integrated NCP south of 50° S from our dataset, 14 mmol C m-2 d-1, falls within the range of 8.3-24 mmol C m-2 d-1 from other model

  15. 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.

  16. 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...

  17. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    simulated process and compared. The closing chapter describes some practical experiments, where the different control concepts and training methods are tested on the same practical process operating in very noisy environments. All tests confirm that neural networks also have the potential to be trained......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...

  18. 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

  19. 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.

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

    OpenAIRE

    Clarens, Andres F.; Peters, Catherine A.

    2016-01-01

    Abstract 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. He...

  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

    study, investigating the effects of an engineered oxygenation on long-term anoxic bottom waters. The strong stratification of the water column of the Byfjord was broken up by pumping surface water into the deeper layers, triggering several inflows of oxygen-rich water and increasing oxygen levels...... found in oxic waters, showing that an engineered oxygenation of a large body of anoxic marine water is possible and emulates that of a natural oxygenation event....

  2. 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…

  3. Engineering liver

    OpenAIRE

    Linda G Griffith; Wells, Alan; Stolz, Donna Beer

    2013-01-01

    Interest in “engineering liver” arises from multiple communities: therapeutic replacement; mechanistic models of human processes; and drug safety and efficacy studies. An explosion of micro- and nano-fabrication, biomaterials, microfluidic, and other technologies potentially afford unprecedented opportunity to create microphysiological models of human liver, but engineering design principles for how to deploy these tools effectively towards specific applications, including how to define the e...

  4. 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.

  5. 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

  6. The dish-Rankine SCSTPE program (Engineering Experiment no. 1). [systems engineering and economic analysis for a small community solar thermal electric system

    Science.gov (United States)

    Pons, R. L.; Grigsby, C. E.

    1980-01-01

    Activities planned for phase 2 Of the Small Community Solar Thermal Power Experiment (PFDR) program are summarized with emphasis on a dish-Rankine point focusing distributed receiver solar thermal electric system. Major design efforts include: (1) development of an advanced concept indirect-heated receiver;(2) development of hardware and software for a totally unmanned power plant control system; (3) implementation of a hybrid digital simulator which will validate plant operation prior to field testing; and (4) the acquisition of an efficient organic Rankine cycle power conversion unit. Preliminary performance analyses indicate that a mass-produced dish-Rankine PFDR system is potentially capable of producing electricity at a levelized busbar energy cost of 60 to 70 mills per KWh and with a capital cost of about $1300 per KW.

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

    Energy Technology Data Exchange (ETDEWEB)

    Bott, T.L.; Kaplan, L.A. (Academy of Natural Sciences, Avondale, PA (United States))

    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 [ge]7[times]10[sup 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.

  8. PhD Topic Arrangement in "D"iscourse Communities of Engineers and Social Sciences/Humanities

    Science.gov (United States)

    Hasrati, Mostafa; Street, Brian

    2009-01-01

    This article is the result of a grounded theory investigation into the ways PhD topics are assigned by supervisors in engineering and selected by students in the social sciences/humanities in UK universities, broadly referred to as "topic arrangement", which can be regarded as one aspect of academic socialisation into academic Discourse…

  9. 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.

  10. 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...

  11. Applications of Pulse-Coupled Neural Networks

    CERN Document Server

    Ma, Yide; Wang, Zhaobin

    2011-01-01

    "Applications of Pulse-Coupled Neural Networks" explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields. This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science. Prof. Yide Ma conducts research on intelligent information processing, biomedical image processing, and embedded system development at the School of Information Sci

  12. 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...

  13. Understanding perception through neural "codes".

    Science.gov (United States)

    Freeman, Walter J

    2011-07-01

    A major challenge for cognitive scientists is to deduce and explain the neural mechanisms of the rapid transposition between stimulus energy and recalled memory-between the specific (sensation) and the generic (perception)-in both material and mental aspects. Researchers are attempting three explanations in terms of neural codes. The microscopic code: cellular neurobiologists correlate stimulus properties with the rates and frequencies of trains of action potentials induced by stimuli and carried by topologically organized axons. The mesoscopic code: cognitive scientists formulate symbolic codes in trains of action potentials from feature-detector neurons of phonemes, lines, odorants, vibrations, faces, etc., that object-detector neurons bind into representations of stimuli. The macroscopic code: neurodynamicists extract neural correlates of stimuli and associated behaviors in spatial patterns of oscillatory fields of dendritic activity, which self-organize and evolve on trajectories through high-dimensional brain state space. This multivariate code is expressed in landscapes of chaotic attractors. Unlike other scientific codes, such as DNA and the periodic table, these neural codes have no alphabet or syntax. They are epistemological metaphors that experimentalists need to measure neural activity and engineers need to model brain functions. My aim is to describe the main properties of the macroscopic code and the grand challenge it poses: how do very large patterns of textured synchronized oscillations form in cortex so quickly? © 2010 IEEE

  14. Neural recording and modulation technologies

    Science.gov (United States)

    Chen, Ritchie; Canales, Andres; Anikeeva, Polina

    2017-01-01

    In the mammalian nervous system, billions of neurons connected by quadrillions of synapses exchange electrical, chemical and mechanical signals. Disruptions to this network manifest as neurological or psychiatric conditions. Despite decades of neuroscience research, our ability to treat or even to understand these conditions is limited by the capability of tools to probe the signalling complexity of the nervous system. Although orders of magnitude smaller and computationally faster than neurons, conventional substrate-bound electronics do not recapitulate the chemical and mechanical properties of neural tissue. This mismatch results in a foreign-body response and the encapsulation of devices by glial scars, suggesting that the design of an interface between the nervous system and a synthetic sensor requires additional materials innovation. Advances in genetic tools for manipulating neural activity have fuelled the demand for devices that are capable of simultaneously recording and controlling individual neurons at unprecedented scales. Recently, flexible organic electronics and bio- and nanomaterials have been developed for multifunctional and minimally invasive probes for long-term interaction with the nervous system. In this Review, we discuss the design lessons from the quarter-century-old field of neural engineering, highlight recent materials-driven progress in neural probes and look at emergent directions inspired by the principles of neural transduction.

  15. Neural Tube Defects

    Science.gov (United States)

    ... vitamin, before and during pregnancy prevents most neural tube defects. Neural tube defects are usually diagnosed before the infant is ... or imaging tests. There is no cure for neural tube defects. The nerve damage and loss of function ...

  16. 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....

  17. 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.

  18. 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...

  19. Both host plant and ecosystem engineer identity influence leaf-tie impacts on the arthropod community of Quercus.

    Science.gov (United States)

    Wang, H George; Marquis, Robert J; Baer, Christina S

    2012-10-01

    Many insect herbivores build shelters on plants, which are then colonized by other arthropod species. To understand the impacts of such ecosystem engineering on associated species, the contributions of ecosystem engineer and host-plant identities must be understood. We investigated these contingencies at the patch scale using two species of leaf-tying caterpillars, which vary in size and tie construction mode, on eight species of oak (Quercus) trees, which vary in leaf size and leaf chemistry. We created three types of artificial leaf ties by clipping together pairs of adjacent leaves using metal hair clips. We left the first type of leaf tie empty while adding individuals of the leaf-tying caterpillars of either Pseudotelphusa quercinigracella or Psilocorsis cryptolechiella to the other two. We also created a control treatment of untied leaves by affixing clips to single leaves. Leaf ties increased occupancy in the early season and arthropod alpha diversity throughout the experiment, on average fourfold. Furthermore, the presence of leaf ties increased arthropod species density on average three times and abundance 10-35 times, depending on the plant species. The mean phenolic content of the leaves of each oak species was positively correlated with the leaf-tie effect on abundance and negatively correlated with the leaf-tie effect on species diversity. Species diversity, but not abundance, was affected by the identity of the tie-maker. Arthropod species composition differed between untied leaves and artificial leaf ties, and between ties made by the two leaf-tier species. Our results demonstrate that the presence of leaf ties adds to habitat diversity within the oak-herbivore system, not only by creating a new kind of microhabitat (the leaf tie) within trees, but also by exacerbating differences among the eight oak species in apparent habitat quality. The identity of the leaf-tying caterpillar adds to this heterogeneity by creating leaf ties of different size, thus

  20. [Neural repair].

    Science.gov (United States)

    Kitada, Masaaki; Dezawa, Mari

    2008-05-01

    Recent progress of stem cell biology gives us the hope for neural repair. We have established methods to specifically induce functional Schwann cells and neurons from bone marrow stromal cells (MSCs). The effectiveness of these induced cells was evaluated by grafting them either into peripheral nerve injury, spinal cord injury, or Parkinson' s disease animal models. MSCs-derived Schwann cells supported axonal regeneration and re-constructed myelin to facilitate the functional recovery in peripheral and spinal cord injury. MSCs-derived dopaminergic neurons integrated into host striatum and contributed to behavioral repair. In this review, we introduce the differentiation potential of MSCs and finally discuss about their benefits and drawbacks of these induction systems for cell-based therapy in neuro-traumatic and neuro-degenerative diseases.

  1. 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.

  2. 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

    2013-01-01

    BACKGROUND: 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.). METHODS: 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. RESULTS: 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. CONCLUSION: 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. Birth Defects Research (Part A) 97:444–451, 2013. © 2013 Wiley Periodicals, Inc. PMID:23873812

  3. Object Based Systems Engineering

    Science.gov (United States)

    2011-10-17

    Based Systems Engineering ( MBSE ) has shifted the emphasis of the Systems Engineering community away from documents towards view-based artifacts. These...Engineering lies primarily in these objects, not the containers that deliver them. FIGURE 1: Evolution of Systems Engineering Practice MBSE ...capture minority viewpoints and discussion threads associated with each object of interest. If the majority view doesn’t lead to success, this data may

  4. 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 brain regions associated with visual short-term memory, including the prefrontal cortex, in the exercise group (P physical and 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.

  5. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  6. 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.

  7. Neural network topology design for nonlinear control

    Science.gov (United States)

    Haecker, Jens; Rudolph, Stephan

    2001-03-01

    Neural networks, especially in nonlinear system identification and control applications, are typically considered to be black-boxes which are difficult to analyze and understand mathematically. Due to this reason, an in- depth mathematical analysis offering insight into the different neural network transformation layers based on a theoretical transformation scheme is desired, but up to now neither available nor known. In previous works it has been shown how proven engineering methods such as dimensional analysis and the Laplace transform may be used to construct a neural controller topology for time-invariant systems. Using the knowledge of neural correspondences of these two classical methods, the internal nodes of the network could also be successfully interpreted after training. As further extension to these works, the paper describes the latest of a theoretical interpretation framework describing the neural network transformation sequences in nonlinear system identification and control. This can be achieved By incorporation of the method of exact input-output linearization in the above mentioned two transform sequences of dimensional analysis and the Laplace transformation. Based on these three theoretical considerations neural network topologies may be designed in special situations by pure translation in the sense of a structural compilation of the known classical solutions into their correspondent neural topology. Based on known exemplary results, the paper synthesizes the proposed approach into the visionary goals of a structural compiler for neural networks. This structural compiler for neural networks is intended to automatically convert classical control formulations into their equivalent neural network structure based on the principles of equivalence between formula and operator, and operator and structure which are discussed in detail in this work.

  8. Digital Neural Networks for New Media

    Science.gov (United States)

    Spaanenburg, Lambert; Malki, Suleyman

    Neural Networks perform computationally intensive tasks offering smart solutions for many new media applications. A number of analog and mixed digital/analog implementations have been proposed to smooth the algorithmic gap. But gradually, the digital implementation has become feasible, and the dedicated neural processor is on the horizon. A notable example is the Cellular Neural Network (CNN). The analog direction has matured for low-power, smart vision sensors; the digital direction is gradually being shaped into an IP-core for algorithm acceleration, especially for use in FPGA-based high-performance systems. The chapter discusses the next step towards a flexible and scalable multi-core engine using Application-Specific Integrated Processors (ASIP). This topographic engine can serve many new media tasks, as illustrated by novel applications in Homeland Security. We conclude with a view on the CNN kaleidoscope for the year 2020.

  9. 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.

  10. 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.

  11. 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.

  12. Introduction to neural networks

    CERN Document Server

    James, Frederick E

    1994-02-02

    1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.

  13. 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.

  14. 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...

  15. 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

  16. 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 < Dw moe < 1) and the absorption loss, with a correlation coefficient R² equal to 0.85. Finally, the prediction of silver fir biodegradation rate by NIRS was significant (RMSEP = 0

  17. 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. Adnan Hamad, Dingli Yu, JB Gomm, Mahavir S Sangha. Abstract. Fault detection and isolation have become one of the most important aspects of automobile design. A fault detection (FD) scheme is developed for automotive engines in this paper.

  18. NASA Engineering Network (NEN)

    Science.gov (United States)

    Topousis, Daria; Trevarthen, Ellie; Yew, Manson

    2008-01-01

    This slide presentation reviews the NASA Engineering Network (NEN). NEN is designed to search documents over multiple repositories, submit and browse NASA Lessons Learned, collaborate and share ideas with other engineers via communities of practice, access resources from one portal, and find subject matter experts via the People, Organizations, Projects, Skills (POPS) locator.

  19. 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…

  20. Assessing Landslide Hazard Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, Farzad; Choobbasti, Asskar Janalizadeh; Barari, Amin

    2011-01-01

    failure" which is main concentration of the current research and "liquefaction failure". Shear failures along shear planes occur when the shear stress along the sliding surfaces exceed the effective shear strength. These slides have been referred to as landslide. An expert system based on artificial...... neural network has been developed for use in the stability evaluation of slopes under various geological conditions and engineering requirements. The Artificial neural network model of this research uses slope characteristics as input and leads to the output in form of the probability of failure...

  1. Applications of Wavelet Neural Network Model to Building Settlement Prediction: A Case Study

    Directory of Open Access Journals (Sweden)

    Qulin TAN

    2014-04-01

    Full Text Available Deformation monitoring is a significant work for engineering safety, which is performed throughout the entire process of engineering design, construction and operation. Based on the theoretic analysis of wavelet and neural network, we applied the improved BP neural network model, auxiliary wavelet neural network model and embedded wavelet neural network model to the settlement prediction in one practical engineering monitoring project with MATLAB software programming. The cumulative and the interval settlement was predicted and compared with measured data. The overall performances of the three models were analyzed and compared. The results show that the accuracies of two kinds of wavelet neural network models are roughly the same, which prediction errors of monitoring points are less than 1mm, obviously superior to the single BP neural network model.

  2. [Glutamate signaling and neural plasticity].

    Science.gov (United States)

    Watanabe, Masahiko

    2013-07-01

    Proper functioning of the nervous system relies on the precise formation of neural circuits during development. At birth, neurons have redundant synaptic connections not only to their proper targets but also to other neighboring cells. Then, functional neural circuits are formed during early postnatal development by the selective strengthening of necessary synapses and weakening of surplus connections. Synaptic connections are also modified so that projection fields of active afferents expand at the expense of lesser ones. We have studied the molecular mechanisms underlying these activity-dependent prunings and the plasticity of synaptic circuitry using gene-engineered mice defective in the glutamatergic signaling system. NMDA-type glutamate receptors are critically involved in the establishment of the somatosensory pathway ascending from the brainstem trigeminal nucleus to the somatosensory cortex. Without NMDA receptors, whisker-related patterning fails to develop, whereas lesion-induced plasticity occurs normally during the critical period. In contrast, mice lacking the glutamate transporters GLAST or GLT1 are selectively impaired in the lesion-induced critical plasticity of cortical barrels, although whisker-related patterning itself develops normally. In the developing cerebellum, multiple climbing fibers initially innervating given Purkinje cells are eliminated one by one until mono-innervation is achieved. In this pruning process, P/Q-type Ca2+ channels expressed on Purkinje cells are critically involved by the selective strengthening of single main climbing fibers against other lesser afferents. Therefore, the activation of glutamate receptors that leads to an activity-dependent increase in the intracellular Ca2+ concentration plays a key role in the pruning of immature synaptic circuits into functional circuits. On the other hand, glutamate transporters appear to control activity-dependent plasticity among afferent fields, presumably through adjusting

  3. Artificial Neural Network Metamodels of Stochastic Computer Simulations

    Science.gov (United States)

    1994-08-10

    23 Haddock, J. and O’Keefe, R., "Using Artificial Intelligence to Facilitate Manufacturing Systems Simulation," Computers & Industrial Engineering , Vol...Feedforward Neural Networks," Computers & Industrial Engineering , Vol. 21, No. 1- 4, (1991), pp. 247-251. 87 Proceedings of the 1992 Summer Computer...Using Simulation Experiments," Computers & Industrial Engineering , Vol. 22, No. 2 (1992), pp. 195-209. 119 Kuei, C. and Madu, C., "Polynomial

  4. Community Colleges, Catalysts for Mobility or Engines for Inequality? Addressing Selection Bias in the Estimation of Their Effects on Educational and Occupational Outcomes

    Science.gov (United States)

    Gonzalez Canche, Manuel Sacramento

    2012-01-01

    For the last 25 years, research on the effects of community colleges on baccalaureate degree attainment has concluded that community colleges drastically reduce the likelihood of attaining a bachelor's degree compared to the effects of four-year institutions on this likelihood. The thesis of this dissertation is that community colleges have…

  5. 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

  6. 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.

  7. 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.

  8. Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem

    Directory of Open Access Journals (Sweden)

    Jiao-Hong Yi

    2016-01-01

    Full Text Available Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified self-adaptive probabilistic neural networks 1 and 2 are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and self-adaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.

  9. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    In contemporary consciousness studies the phenomenon of neural plasticity has received little attention despite the fact that neural plasticity is of still increased interest in neuroscience. We will, however, argue that neural plasticity could be of great importance to consciousness studies....... If consciousness is related to neural processes it seems, at least prima facie, that the ability of the neural structures to change should be reflected in a theory of this relationship "Neural plasticity" refers to the fact that the brain can change due to its own activity. The brain is not static but rather...... a dynamic entity, which physical structure changes according to its use and environment. This change may take the form of growth of new neurons, the creation of new networks and structures, and change within network structures, that is, changes in synaptic strengths. Plasticity raises questions about...

  10. The Quantum Engineering Conundrum

    Science.gov (United States)

    Monroe, Christopher

    2017-04-01

    There is newfound rush and excitement in Quantum Information Science, as this field seems to be moving toward an industrial/engineering phase. However, this evolution will require that quantum science, long the domain of academics and other researchers, make the leap to sustained engineering efforts in order to fabricate practical devices. I will address the conundrum, that full-blooded engineering does not generally happen on campuses, while many in the professional engineering and computer science community do not believe in quantum physics!

  11. 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

  12. Fuzzy and neural control

    Science.gov (United States)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  13. What Is Neural Plasticity?

    Science.gov (United States)

    von Bernhardi, Rommy; Bernhardi, Laura Eugenín-von; Eugenín, Jaime

    2017-01-01

    "Neural plasticity" refers to the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. As the various chapters in this volume show, plasticity is a key component of neural development and normal functioning of the nervous system, as well as a response to the changing environment, aging, or pathological insult. This chapter discusses how plasticity is necessary not only for neural networks to acquire new functional properties, but also for them to remain robust and stable. The article also reviews the seminal proposals developed over the years that have driven experiments and strongly influenced concepts of neural plasticity.

  14. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    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......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...

  15. Predicting the parameters of energy installations with laser ignition: Neural network models

    Directory of Open Access Journals (Sweden)

    Alexey A. Pastukhov

    2015-06-01

    Full Text Available This article considers the possibility of using artificial neural networks for predicting the parameters of the model energy installation with laser ignition. The main stages of creating a prognostic model based on an artificial neural network have been presented. Input data were analyzed by principal component method. The synthesized neural network was designed to predict the parameter value of the model in question. The artificial neural network was trained by a back-propagation algorithm. The efficiency of the artificial neural networks and their applicability to predicting parameter values of various rocket engine elements were demonstrated.

  16. 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…

  17. 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

  18. Statistical Physics, Neural Networks, Brain Studies

    OpenAIRE

    TOULOUSE, Gérard

    2014-01-01

    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 interdis...

  19. 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.

  20. An Engineering Context for Software Engineering

    Science.gov (United States)

    2008-09-01

    his recent work on software practice with, Some ... object that commercial software is too dependent on changing market conditions to permit...Building codes also derive from community standards regarding esthetics , bigotry, and economics. The chemical engineer may be concerned with human... esthetics , and consistency are essential. The architecture, once established, should remain 69 a stable model throughout the entire process. The

  1. 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…

  2. Kunstige neurale net

    DEFF Research Database (Denmark)

    Hørning, Annette

    1994-01-01

    Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse.......Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse....

  3. Engineer Ethics

    Energy Technology Data Exchange (ETDEWEB)

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

    2003-03-15

    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.

  4. 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.

  5. Classification-based Financial Markets Prediction using Deep Neural Networks

    OpenAIRE

    Dixon, Matthew; Klabjan, Diego; Bang, Jin Hoon

    2016-01-01

    Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the applicat...

  6. 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.

  7. A Survey of Neural Front End Amplifiers and Their Requirements toward Practical Neural Interfaces

    Directory of Open Access Journals (Sweden)

    Eric Bharucha

    2014-11-01

    Full Text Available When designing an analog front-end for neural interfacing, it is hard to evaluate the interplay of priority features that one must upkeep. Given the competing nature of design requirements for such systems a good understanding of these trade-offs is necessary. Low power, chip size, noise control, gain, temporal resolution and safety are the salient ones. There is a need to expose theses critical features for high performance neural amplifiers as the density and performance needs of these systems increases. This review revisits the basic science behind the engineering problem of extracting neural signal from living tissue. A summary of architectures and topologies is then presented and illustrated through a rich set of examples based on the literature. A survey of existing systems is presented for comparison based on prevailing performance metrics.

  8. Communication as Part of the Engineering Skills Set

    Science.gov (United States)

    Lappalainen, Pia

    2009-01-01

    Engineering graduates are facing changing requirements regarding their competencies, as interdisciplinarity and globalization have transformed engineering communities into collaboration arenas extending beyond uniform national, cultural, contextual and disciplinary settings and structures. Engineers no longer manage their daily tasks with plain…

  9. 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.

  10. 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...

  11. Building a Community of Scholars: One University's Comparison of "Typical" vs. Open Ended Ethics Case Studies in First-Year Engineering

    Science.gov (United States)

    Reid, Kenneth J.

    2012-01-01

    Ethics is among the professional skills embedded in the first year engineering curriculum in many institutions. The general format of the study of ethics is similar to many other institutions: student teams review case studies and develop written and oral presentations on the ethical issues encountered. This report investigates whether the use of…

  12. 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...

  13. [Neural codes for perception].

    Science.gov (United States)

    Romo, R; Salinas, E; Hernández, A; Zainos, A; Lemus, L; de Lafuente, V; Luna, R

    This article describes experiments designed to show the neural codes associated with the perception and processing of tactile information. The results of these experiments have shown the neural activity correlated with tactile perception. The neurones of the primary somatosensory cortex (S1) represent the physical attributes of tactile perception. We found that these representations correlated with tactile perception. By means of intracortical microstimulation we demonstrated the causal relationship between S1 activity and tactile perception. In the motor areas of the frontal lobe is to be found the connection between sensorial and motor representation whilst decisions are being taken. S1 generates neural representations of the somatosensory stimuli which seen to be sufficient for tactile perception. These neural representations are subsequently processed by central areas to S1 and seem useful in perception, memory and decision making.

  14. Internal combustion piston engines

    Energy Technology Data Exchange (ETDEWEB)

    Segaser, C.L.

    1977-07-01

    Current worldwide production of internal combustion piston engines includes many diversified types of designs and a very broad range of sizes. Engine sizes range from a few horsepower in small mobile units to over 40,000 brake horsepower in large stationary and marine units. The key characteristics of internal combustion piston engines considered appropriate for use as prime movers in Integrated Community Energy Systems (ICES) are evaluated. The categories of engines considered include spark-ignition gas engines, compression-ignition oil (diesel) engines, and dual-fuel engines. The engines are evaluated with respect to full-load and part-load performance characteristics, reliability, environmental concerns, estimated 1976 cost data, and current and future status of development. The largest internal combustion piston engines manufactured in the United States range up to 13,540 rated brake horsepower. Future development efforts are anticipated to result in a 20 to 25% increase in brake horsepower without increase in or loss of weight, economy, reliability, or life expectancy, predicated on a simple extension of current development trends.

  15. 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.

  16. 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.

  17. Neural network applications

    Science.gov (United States)

    Padgett, Mary L.; Desai, Utpal; Roppel, T.A.; White, Charles R.

    1993-01-01

    A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.

  18. Building Neural Net Software

    OpenAIRE

    Neto, João Pedro; Costa, José Félix

    1999-01-01

    In a recent paper [Neto et al. 97] we showed that programming languages can be translated on recurrent (analog, rational weighted) neural nets. The goal was not efficiency but simplicity. Indeed we used a number-theoretic approach to machine programming, where (integer) numbers were coded in a unary fashion, introducing a exponential slow down in the computations, with respect to a two-symbol tape Turing machine. Implementation of programming languages in neural nets turns to be not only theo...

  19. NEMEFO: NEural MEteorological FOrecast

    Energy Technology Data Exchange (ETDEWEB)

    Pasero, E.; Moniaci, W.; Meindl, T.; Montuori, A. [Polytechnic of Turin (Italy). Dept. of Electronics

    2004-07-01

    Artificial Neural Systems are a well-known technique used to classify and recognize objects. Introducing the time dimension they can be used to forecast numerical series. NEMEFO is a ''nowcasting'' tool, which uses both statistical and neural systems to forecast meteorological data in a restricted area close to a meteorological weather station in a short time range (3 hours). Ice, fog, rain are typical events which can be anticipated by NEMEFO. (orig.)

  20. Adaptive Neurotechnology for Making Neural Circuits Functional .

    Science.gov (United States)

    Jung, Ranu

    2008-03-01

    Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions.

  1. 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.

  2. Business Process Re-Engineering (BPR) of the Navy’s Information Professional (IP) Community’s Continuing Education Unit (CEU) Tracking Process

    Science.gov (United States)

    2006-03-01

    Microsoft Access and JET, applications with incompatible features, or large applications without database abstraction. ( Hillyer , 2005) With any...sure to see results of increased performance, cross-platform capability and open source. ( Hillyer , 2005) e. Oracle Oracle is one of the most widely...M. and Champy, J. (1993). Re-engineering the Corporation: A Manifesto for Business Revolution, New York: Harper Business Press, 1993. Hillyer

  3. Engineering ceramics

    CERN Document Server

    Bengisu, Murat

    2001-01-01

    This is a comprehensive book applying especially to junior and senior engineering students pursuing Materials Science/ Engineering, Ceramic Engineering and Mechanical Engineering degrees. It is also a reference book for other disciplines such as Chemical Engineering, Biomedical Engineering, Nuclear Engineering and Environmental Engineering. Important properties of most engineering ceramics are given in detailed tables. Many current and possible applications of engineering ceramics are described, which can be used as a guide for materials selection and for potential future research. While covering all relevant information regarding raw materials, processing properties, characterization and applications of engineering ceramics, the book also summarizes most recent innovations and developments in this field as a result of extensive literature search.

  4. 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...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear 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...

  5. 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.

  6. Artificial neural networks for prediction of percentage of water ...

    Indian Academy of Sciences (India)

    Mater. Sci., Vol. 35, No. 6, November 2012, pp. 1019–1029. c Indian Academy of Sciences. Artificial neural networks for prediction of percentage of water absorption of geopolymers produced by waste ashes. ALI NAZARI. Department of Materials Science and Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran.

  7. 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…

  8. The Application of Radial Basis Function (RBF) Neural Network for Mechanical Fault Diagnosis of Gearbox

    Science.gov (United States)

    Wang, Pengbo

    2017-11-01

    In this paper, the radial basis function (RBF) neural network is used for the mechanical fault diagnosis of a gearbox. We introduce the basic principles of the RBF neural network which is used for pattern classification and features a fast learning pace and strong nonlinear mapping capability; thus, it can be employed for fault diagnosis. The gearbox is a widely-used piece of equipment in engineering, and diagnosing mechanical faults is of great significance for engineers. A numerical example is presented to demonstrate the capability of the proposed method. The results indicate that the mechanical faults of a gearbox can be correctly diagnosed with a trained RBF neural network.

  9. At the interface: convergence of neural regeneration and neural prostheses for restoration of function.

    Science.gov (United States)

    Grill, W M; McDonald, J W; Peckham, P H; Heetderks, W; Kocsis, J; Weinrich, M

    2001-01-01

    The rapid pace of recent advances in development and application of electrical stimulation of the nervous system and in neural regeneration has created opportunities to combine these two approaches to restoration of function. This paper relates the discussion on this topic from a workshop at the International Functional Electrical Stimulation Society. The goals of this workshop were to discuss the current state of interaction between the fields of neural regeneration and neural prostheses and to identify potential areas of future research that would have the greatest impact on achieving the common goal of restoring function after neurological damage. Identified areas include enhancement of axonal regeneration with applied electric fields, development of hybrid neural interfaces combining synthetic silicon and biologically derived elements, and investigation of the role of patterned neural activity in regulating various neuronal processes and neurorehabilitation. Increased communication and cooperation between the two communities and recognition by each field that the other has something to contribute to their efforts are needed to take advantage of these opportunities. In addition, creative grants combining the two approaches and more flexible funding mechanisms to support the convergence of their perspectives are necessary to achieve common objectives.

  10. 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...

  11. Conducting Polymers for Neural Prosthetic and Neural Interface Applications

    Science.gov (United States)

    2015-01-01

    Neural interfacing devices are an artificial mechanism for restoring or supplementing the function of the nervous system lost as a result of injury or disease. Conducting polymers (CPs) are gaining significant attention due to their capacity to meet the performance criteria of a number of neuronal therapies including recording and stimulating neural activity, the regeneration of neural tissue and the delivery of bioactive molecules for mediating device-tissue interactions. CPs form a flexible platform technology that enables the development of tailored materials for a range of neuronal diagnostic and treatment therapies. In this review the application of CPs for neural prostheses and other neural interfacing devices are discussed, with a specific focus on neural recording, neural stimulation, neural regeneration, and therapeutic drug delivery. PMID:26414302

  12. 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.

  13. 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

  14. Hyperbolic Hopfield neural networks.

    Science.gov (United States)

    Kobayashi, M

    2013-02-01

    In recent years, several neural networks using Clifford algebra have been studied. Clifford algebra is also called geometric algebra. Complex-valued Hopfield neural networks (CHNNs) are the most popular neural networks using Clifford algebra. The aim of this brief is to construct hyperbolic HNNs (HHNNs) as an analog of CHNNs. Hyperbolic algebra is a Clifford algebra based on Lorentzian geometry. In this brief, a hyperbolic neuron is defined in a manner analogous to a phasor neuron, which is a typical complex-valued neuron model. HHNNs share common concepts with CHNNs, such as the angle and energy. However, HHNNs and CHNNs are different in several aspects. The states of hyperbolic neurons do not form a circle, and, therefore, the start and end states are not identical. In the quantized version, unlike complex-valued neurons, hyperbolic neurons have an infinite number of states.

  15. Neural Semantic Encoders.

    Science.gov (United States)

    Munkhdalai, Tsendsuren; Yu, Hong

    2017-04-01

    We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

  16. The neural crest and neural crest cells: discovery and significance ...

    Indian Academy of Sciences (India)

    In this paper I provide a brief overview of the major phases of investigation into the neural crest and the major players involved, discuss how the origin of the neural crest relates to the origin of the nervous system in vertebrate embryos, discuss the impact on the germ-layer theory of the discovery of the neural crest and of ...

  17. 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....

  18. 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.

  19. Polymeric Nanofibers in Tissue Engineering

    Science.gov (United States)

    Dahlin, Rebecca L.; Kasper, F. Kurtis

    2011-01-01

    Polymeric nanofibers can be produced using methods such as electrospinning, phase separation, and self-assembly, and the fiber composition, diameter, alignment, degradation, and mechanical properties can be tailored to the intended application. Nanofibers possess unique advantages for tissue engineering. The small diameter closely matches that of extracellular matrix fibers, and the relatively large surface area is beneficial for cell attachment and bioactive factor loading. This review will update the reader on the aspects of nanofiber fabrication and characterization important to tissue engineering, including control of porous structure, cell infiltration, and fiber degradation. Bioactive factor loading will be discussed with specific relevance to tissue engineering. Finally, applications of polymeric nanofibers in the fields of bone, cartilage, ligament and tendon, cardiovascular, and neural tissue engineering will be reviewed. PMID:21699434

  20. Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

    Directory of Open Access Journals (Sweden)

    Jonathan C Tapson

    2013-08-01

    Full Text Available The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify – arbitrarily - neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.

  1. 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…

  2. 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....

  3. 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...

  4. Neural Tube Defects

    Science.gov (United States)

    ... pregnancies each year in the United States. A baby’s neural tube normally develops into the brain and spinal cord. ... fluid in the brain. This is called hydrocephalus. Babies with this condition are treated with surgery to insert a tube (called a shunt) into the brain. The shunt ...

  5. 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,

  6. Artificial Neural Networks in Policy Research: A Current Assessment.

    Science.gov (United States)

    Woelfel, Joseph

    1993-01-01

    Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

  7. 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

  8. 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

  9. Biochemical Engineering.

    Science.gov (United States)

    Dunnill, P.

    1979-01-01

    Biochemical engineering as a scientific discipline is becoming accepted in England and is drawing many young men and women to its ranks. This article focuses on how engineering came to embrace the biological sciences. (Author/SA)

  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. 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 ...

  12. 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)....

  13. A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.

    Science.gov (United States)

    Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L; Nicolau, Alex; Veidenbaum, Alexander V

    2009-01-01

    Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for various neural engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Compute Unified Device Architecture (CUDA) Graphics Processing Units (GPUs) can provide a low-cost, programmable, and high-performance computing platform for simulation of SNNs. In this paper we demonstrate an efficient, biologically realistic, large-scale SNN simulator that runs on a single GPU. The SNN model includes Izhikevich spiking neurons, detailed models of synaptic plasticity and variable axonal delay. We allow user-defined configuration of the GPU-SNN model by means of a high-level programming interface written in C++ but similar to the PyNN programming interface specification. PyNN is a common programming interface developed by the neuronal simulation community to allow a single script to run on various simulators. The GPU implementation (on NVIDIA GTX-280 with 1 GB of memory) is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7 Hz. For simulation of 10 Million synaptic connections and 100K neurons, the GPU SNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results was validated on CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. Our simulator is publicly available to the modeling community so that researchers will have easy access to large-scale SNN simulations.

  14. Neural prostheses in clinical practice: biomedical microsystems in neurological rehabilitation.

    Science.gov (United States)

    Stieglitz, T

    2007-01-01

    Technical devices have supported physicians in diagnosis, therapy, and rehabilitation since ancient times. Neural prostheses interface parts of the nervous system with technical (micro-) systems to partially restore sensory and motor functions that have been lost due to trauma or diseases. Electrodes act as transducers to record neural signals or to excite neural cells by means of electrical stimulation. The field of neural prostheses has grown over the last decades. An overview of neural prostheses illustrates the opportunities and limitations of the implants and performance in their current size and complexity. The implementation of microsystem technology with integrated microelectronics in neural implants 20 years ago opened new fields of application, but also new design paradigms and approaches with respect to the biostability of passivation and housing concepts and electrode interfaces. Microsystem specific applications in the peripheral nervous system, vision prostheses and brain-machine interfaces show the variety of applications and the challenges in biomedical microsystems for chronic nerve interfaces in new and emerging research fields that bridge neuroscientific disciplines with material science and engineering. Different scenarios are discussed where system complexity strongly depends on the rehabilitation objective and the amount of information that is necessary for the chosen neuro-technical interface.

  15. 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.

  16. 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.

  17. 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.

  18. Engineering Motion

    Science.gov (United States)

    Tuttle, Nicole; Stanley, Wendy; Bieniek, Tracy

    2016-01-01

    For many teachers, engineering can be intimidating; teachers receive little training in engineering, particularly those teaching early elementary students. In addition, the necessity of differentiating for students with special needs can make engineering more challenging to teach. This article describes a professional development program…

  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. 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...

  1. 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.

  2. Neural network technologies

    Science.gov (United States)

    Villarreal, James A.

    1991-01-01

    A whole new arena of computer technologies is now beginning to form. Still in its infancy, neural network technology is a biologically inspired methodology which draws on nature's own cognitive processes. The Software Technology Branch has provided a software tool, Neural Execution and Training System (NETS), to industry, government, and academia to facilitate and expedite the use of this technology. NETS is written in the C programming language and can be executed on a variety of machines. Once a network has been debugged, NETS can produce a C source code which implements the network. This code can then be incorporated into other software systems. Described here are various software projects currently under development with NETS and the anticipated future enhancements to NETS and the technology.

  3. 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.

  4. Neural tube defects

    Directory of Open Access Journals (Sweden)

    M.E. Marshall

    1981-09-01

    Full Text Available Neural tube defects refer to any defect in the morphogenesis of the neural tube, the most common types being spina bifida and anencephaly. Spina bifida has been recognised in skeletons found in north-eastern Morocco and estimated to have an age of almost 12 000 years. It was also known to the ancient Greek and Arabian physicians who thought that the bony defect was due to the tumour. The term spina bifida was first used by Professor Nicolai Tulp of Amsterdam in 1652. Many other terms have been used to describe this defect, but spina bifida remains the most useful general term, as it describes the separation of the vertebral elements in the midline.

  5. Neural networks for triggering

    Energy Technology Data Exchange (ETDEWEB)

    Denby, B. (Fermi National Accelerator Lab., Batavia, IL (USA)); Campbell, M. (Michigan Univ., Ann Arbor, MI (USA)); Bedeschi, F. (Istituto Nazionale di Fisica Nucleare, Pisa (Italy)); Chriss, N.; Bowers, C. (Chicago Univ., IL (USA)); Nesti, F. (Scuola Normale Superiore, Pisa (Italy))

    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.

  6. 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. .

  7. Neurally-mediated sincope.

    Science.gov (United States)

    Can, I; Cytron, J; Jhanjee, R; Nguyen, J; Benditt, D G

    2009-08-01

    Syncope is a syndrome characterized by a relatively sudden, temporary and self-terminating loss of consciousness; the causes may vary, but they have in common a temporary inadequacy of cerebral nutrient flow, usually due to a fall in systemic arterial pressure. However, while syncope is a common problem, it is only one explanation for episodic transient loss of consciousness (TLOC). Consequently, diagnostic evaluation should start with a broad consideration of real or seemingly real TLOC. Among those patients in whom TLOC is deemed to be due to ''true syncope'', the focus may then reasonably turn to assessing the various possible causes; in this regard, the neurally-mediated syncope syndromes are among the most frequently encountered. There are three common variations: vasovagal syncope (often termed the ''common'' faint), carotid sinus syndrome, and the so-called ''situational faints''. Defining whether the cause is due to a neurally-mediated reflex relies heavily on careful history taking and selected testing (e.g., tilt-test, carotid massage). These steps are important. Despite the fact that neurally-mediated faints are usually relatively benign from a mortality perspective, they are nevertheless only infrequently an isolated event; neurally-mediated syncope tends to recur, and physical injury resulting from falls or accidents, diminished quality-of-life, and possible restriction from employment or avocation are real concerns. Consequently, defining the specific form and developing an effective treatment strategy are crucial. In every case the goal should be to determine the cause of syncope with sufficient confidence to provide patients and family members with a reliable assessment of prognosis, recurrence risk, and treatment options.

  8. The Neural Noisy Channel

    OpenAIRE

    Yu, Lei; Blunsom, Phil; Dyer, Chris; Grefenstette, Edward; Kocisky, Tomas

    2016-01-01

    We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control ...

  9. Getting symbols out of a neural architecture

    Science.gov (United States)

    Hummel, John E.

    2011-06-01

    Traditional connectionist networks are sharply limited as general accounts of human perception and cognition because they are unable to represent relational ideas such as loves (John, Mary) or bigger-than (Volkswagen, breadbox) in a way that allows them to be manipulated as explicitly relational structures. This paper reviews and critiques the four major responses to this problem in the modelling community: (1) reject connectionism (in any form) in favour of traditional symbolic approaches to modelling the mind; (2) reject the idea that mental representations are symbolic (i.e. reject the idea that we can represent relations); and (3) attempt to represent symbolic structures in a connectionist/neural architecture by finding a way to represent role-filler bindings. Approach (3) is further subdivided into (3a) approaches based on varieties of conjunctive coding and (3b) approaches based on dynamic role-filler binding. I will argue that (3b) is necessary to get symbolic processing out of a neural computing architecture. Specifically, I will argue that vector addition is both the best way to accomplish dynamic binding and an essential part of the proper treatment of symbols in a neural architecture.

  10. Bayesian auxiliary particle filters for estimating neural tuning parameters.

    Science.gov (United States)

    Mountney, John; Sobel, Marc; Obeid, Iyad

    2009-01-01

    A common challenge in neural engineering is to track the dynamic parameters of neural tuning functions. This work introduces the application of Bayesian auxiliary particle filters for this purpose. Based on Monte-Carlo filtering, Bayesian auxiliary particle filters use adaptive methods to model the prior densities of the state parameters being tracked. The observations used are the neural firing times, modeled here as a Poisson process, and the biological driving signal. The Bayesian auxiliary particle filter was evaluated by simultaneously tracking the three parameters of a hippocampal place cell and compared to a stochastic state point process filter. It is shown that Bayesian auxiliary particle filters are substantially more accurate and robust than alternative methods of state parameter estimation. The effects of time-averaging on parameter estimation are also evaluated.

  11. 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

  12. 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.

  13. 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.

  14. 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...

  15. Developing Leadership Skills of Undergraduate Engineering Students: Perspectives from Engineering Faculty

    Science.gov (United States)

    Cox, Monica F.; Cekic, Osman; Adams, Stephanie G.

    2010-01-01

    The engineering education community (motivated by internal and external factors) has begun to focus on leadership abilities of college students in engineering fields via reports from ABET, the National Academy of Engineering, and the National Research Council. These reports have directed criticism toward higher education institutions for their…

  16. 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.

  17. 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.

  18. Engineering knowledge

    OpenAIRE

    Nathan Rosenberg; W. Edward Steinmueller

    2013-01-01

    In historical perspective, both the nature of and arrangements for the generation of engineering knowledge have evolved over the past 150 years. We examine the historical development of the search for ‘useful knowledge’ in agriculture, aeronautics and chemical engineering during the first half of this period and the evolving balance between public and private initiative in supporting this search. During this period, the US was engaged in the engineering knowledge was often empirical, practice...

  19. Cognitive Engineering

    OpenAIRE

    Wilson, Kyle M.; Helton, William S.; Wiggins, Mark W.

    2013-01-01

    Cognitive engineering is the application of cognitive psychology and related disciplines to the design and operation of human–machine systems. Cognitive engineering combines both detailed and close study of the human worker in the actual work context and the study of the worker in more controlled environments. Cognitive engineering combines multiple methods and perspectives to achieve the goal of improved system performance. Given the origins of experimental psychology itself in issues regard...

  20. Predator effects on the feeding and bioirrigation activity of ecosystem-engineered

    NARCIS (Netherlands)

    De Smet, B.; Braeckman, U.; Soetaert, K.; Vincx, M.; Vanaverbeke, J.

    2016-01-01

    Ecosystem engineers can considerably affect the community composition, abundance and species richness of their environment. This study investigates the existence of positive or negative feedbacks of species that compose the community in intertidal biogenic reefs constructed by the ecosystem engineer

  1. 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

  2. Neural Correlates of Stimulus Reportability

    OpenAIRE

    Hulme, Oliver J.; Friston, Karl F.; Zeki, Semir

    2009-01-01

    Most experiments on the “neural correlates of consciousness” employ stimulus reportability as an operational definition of what is consciously perceived. The interpretation of such experiments therefore depends critically on understanding the neural basis of stimulus reportability. Using a high volume of fMRI data, we investigated the neural correlates of stimulus reportability using a partial report object detection paradigm. Subjects were presented with a random array of circularly arranged...

  3. Symbolic processing in neural networks

    OpenAIRE

    Neto, João Pedro; Hava T Siegelmann; Costa,J.Félix

    2003-01-01

    In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and sub symbolic computation. To be of some use, it should be carried in a context of bounded resources. Herein, we show how to use resource bounds to speed up computations over neural nets, thro...

  4. 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.

  5. Population red blood cell folate concentrations for prevention of neural tube defects: Bayesian model.

    Science.gov (United States)

    Crider, Krista S; Devine, Owen; Hao, Ling; Dowling, Nicole F; Li, Song; Molloy, Anne M; Li, Zhu; Zhu, Jianghui; Berry, Robert J

    2014-07-29

    To determine an optimal population red blood cell (RBC) folate concentration for the prevention of neural tube birth defects. Bayesian model. Data from two population based studies in China. 247,831 participants in a prospective community intervention project in China (1993-95) to prevent neural tube defects with 400 μg/day folic acid supplementation and 1194 participants in a population based randomized trial (2003-05) to evaluate the effect of folic acid supplementation on blood folate concentration among Chinese women of reproductive age. Folic acid supplementation (400 μg/day). Estimated RBC folate concentration at time of neural tube closure (day 28 of gestation) and risk of neural tube defects. Risk of neural tube defects was high at the lowest estimated RBC folate concentrations (for example, 25.4 (95% uncertainty interval 20.8 to 31.2) neural tube defects per 10,000 births at 500 nmol/L) and decreased as estimated RBC folate concentration increased. Risk of neural tube defects was substantially attenuated at estimated RBC folate concentrations above about 1000 nmol/L (for example, 6 neural tube defects per 10,000 births at 1180 (1050 to 1340) nmol/L). The modeled dose-response relation was consistent with the existing literature. In addition, neural tube defect risk estimates developed using the proposed model and population level RBC information were consistent with the prevalence of neural tube defects in the US population before and after food fortification with folic acid. A threshold for "optimal" population RBC folate concentration for the prevention of neural tube defects could be defined (for example, approximately 1000 nmol/L). Population based RBC folate concentrations, as a biomarker for risk of neural tube defects, can be used to facilitate evaluation of prevention programs as well as to identify subpopulations at elevated risk for a neural tube defect affected pregnancy due to folate insufficiency. © Crider et al 2014.

  6. 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.

  7. Bridging the Divide between Neuroprosthetic Design, Tissue Engineering and Neurobiology.

    Science.gov (United States)

    Leach, Jennie B; Achyuta, Anil Kumar H; Murthy, Shashi K

    2010-01-01

    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 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 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 toward novel technologies to address the complexities associated with long-term neuroprosthetic device performance.

  8. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  9. Neural Correlates of Face Detection

    National Research Council Canada - National Science Library

    Xu, Xiaokun; Biederman, Irving

    2014-01-01

    Although face detection likely played an essential adaptive role in our evolutionary past and in contemporary social interactions, there have been few rigorous studies investigating its neural correlates...

  10. Intelligent systems II complete approximation by neural network operators

    CERN Document Server

    Anastassiou, George A

    2016-01-01

    This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximation by Artificial Neural Networks” written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book’s results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.  .

  11. 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......-cise is to determine the degree to which the concepts of discourse commu-nity and community of practice are suitable for investigating the social and organizational context of text and knowledge production. Finally, the paper examines the explanatory value of the two concepts for analyzing text and knowledge...... 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....

  12. Biosystems engineering

    OpenAIRE

    P. Ulger; E. Gonulol

    2015-01-01

    Higher agricultural education system has been getting multidiscipline as a result of the level oftechnology, recently. Biosystems Engineering has become popular in developed countries particularly afterelectronic and information technologies has been getting to be a part of agriculture and accompanied ofbiology. In this study definition of Biosystems Engineering discipline, working areas, research, publicationand job opportunities were discussed meticulously. Academic organization of Biosyste...

  13. Engineering Administration.

    Science.gov (United States)

    Naval Personnel Program Support Activity, Washington, DC.

    This book is intended to acquaint naval engineering officers with their duties in the engineering department. Standard shipboard organizations are analyzed in connection with personnel assignments, division operations, and watch systems. Detailed descriptions are included for the administration of directives, ship's bills, damage control, training…

  14. Engineering News

    OpenAIRE

    Nystrom, Lynn; Haugh, Lindsey; Simpkins, David

    2014-01-01

    Engineering News is the College of Engineering's annual newsletter sent to all alumni of the college. Virginia Tech leaders, along with counterparts in New Jersey, welcomed a late December approval by the Federal Aviation Administration to operate a test site to integrate unmanned aircraft into the national airspace.

  15. 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

  16. 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…

  17. Women Engineer.

    Science.gov (United States)

    Neustadtl, Sara Jane

    This booklet is designed to provide information to girls about the nature of and possible career opportunities in engineering. Following a brief introduction in which the characteristics of engineers are outlined (such as ability to solve problems, interest in science/mathematics, and urge to make creative use of their intelligence), answers to…

  18. 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)

  19. 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.

  20. Systems Engineering

    Science.gov (United States)

    Pellerano, Fernando

    2015-01-01

    This short course provides information on what systems engineering is and how the systems engineer guides requirements, interfaces with the discipline leads, and resolves technical issues. There are many system-wide issues that either impact or are impacted by the thermal subsystem. This course will introduce these issues and illustrate them with real life examples.

  1. Electrokinetic confinement of axonal growth for dynamically configurable neural networks

    Science.gov (United States)

    Honegger, Thibault; Scott, Mark A.; Yanik, Mehmet F.; Voldman, Joel

    2013-01-01

    Axons in the developing nervous system are directed via guidance cues, whose expression varies both spatially and temporally, to create functional neural circuits. Existing methods to create patterns of neural connectivity in vitro use only static geometries, and are unable to dynamically alter the guidance cues imparted on the cells. We introduce the use of AC electrokinetics to dynamically control axonal growth in cultured rat hippocampal neurons. We find that the application of modest voltages at frequencies on the order of 105 Hz can cause developing axons to be stopped adjacent to the electrodes while axons away from the electric fields exhibit uninhibited growth. By switching electrodes on or off, we can reversibly inhibit or permit axon passage across the electrodes. Our models suggest that dielectrophoresis is the causative AC electrokinetic effect. We make use of our dynamic control over axon elongation to create an axon-diode via an axon-lock system that consists of a pair of electrode `gates' that either permit or prevent axons from passing through. Finally, we developed a neural circuit consisting of three populations of neurons, separated by three axon-locks to demonstrate the assembly of a functional, engineered neural network. Action potential recordings demonstrate that the AC electrokinetic effect does not harm axons, and Ca2+ imaging demonstrated the unidirectional nature of the synaptic connections. AC electrokinetic confinement of axonal growth has potential for creating configurable, directional neural networks. PMID:23314575

  2. From neural-based object recognition toward microelectronic eyes

    Science.gov (United States)

    Sheu, Bing J.; Bang, Sa Hyun

    1994-01-01

    Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

  3. Open-Source Neural Machine Translation API Server

    Directory of Open Access Journals (Sweden)

    Tars Sander

    2017-10-01

    Full Text Available We introduce an open-source implementation of a machine translation API server. The aim of this software package is to enable anyone to run their own multi-engine translation server with neural machine translation engines, supporting an open API for client applications. Besides the hub with the implementation of the client API and the translation service providers running in the background we also describe an open-source demo web application that uses our software package and implements an online translation tool that supports collecting translation quality comparisons from users.

  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. Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

    Science.gov (United States)

    Decker, Arthur J.

    1999-01-01

    Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

  6. 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. .

  7. 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.

  8. 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...

  9. Artificial Neural Networks·

    Indian Academy of Sciences (India)

    differences between biological neural networks (BNNs) of the brain and ANN s. A thorough understanding of ... neurons. Artificial neural models are loosely based on biology since a complete understanding of the .... A learning scheme for updating a neuron's connections (weights) was proposed by Donald Hebb in 1949.

  10. 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....

  11. The Neural Support Vector Machine

    NARCIS (Netherlands)

    Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus

    2013-01-01

    This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a

  12. 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 ...

  13. The Neural Correlates of Race

    Science.gov (United States)

    Ito, Tiffany A.; Bartholow, Bruce D.

    2009-01-01

    Behavioral analyses are a natural choice for understanding the wide-ranging behavioral consequences of racial stereotyping and prejudice. However, neuroimaging and electrophysiological research has recently considered the neural mechanisms that underlie racial categorization and the activation and application of racial stereotypes and prejudice, revealing exciting new insights. Work reviewed here points to the importance of neural structures previously associated with face processing, semantic knowledge activation, evaluation, and self-regulatory behavioral control, allowing for the specification of a neural model of race processing. We show how research on the neural correlates of race can serve to link otherwise disparate lines of evidence on the neural underpinnings of a broad array of social-cognitive phenomena, and consider implications for effecting change in race relations. PMID:19896410

  14. 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...

  15. 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

  16. 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.

  17. Nanobiomaterials for neural regeneration

    Directory of Open Access Journals (Sweden)

    Nuan Chen

    2016-01-01

    Full Text Available Diseases and disorders associated with nervous system such as injuries by trauma and neurodegeneration are shown to be one of the most serious problems in medicine, requiring innovative strategies to trigger and enhance the nerve regeneration. Tissue engineering aims to provide a highly biomimetic environment by using a combination of cells, materials and suitable biological cues, by which the lost body part may be regenerated or even fully rebuilt. Electrospinning, being able to produce extracellular matrix (ECM-like nanostructures with great flexibility in design and choice of materials, have demonstrated their great potential for fabrication of nerve tissue engineered scaffolds. The review here begins with a brief description of the anatomy of native nervous system, which provides basic knowledge and ideas for the design of nerve tissue scaffolds, followed by five main parts in the design of electrospun nerve tissue engineered scaffolds including materials selection, structural design, in vitro bioreactor, functionalization and cellular support. Performances of biomimetic electrospun nanofibrous nerve implant devices are also reviewed. Finally, future directions for advanced electrospun nerve tissue engineered scaffolds are discussed.

  18. Fault diagnosis for the Space Shuttle main engine

    Science.gov (United States)

    Duyar, Ahmet; Merrill, Walter

    1992-01-01

    A conceptual design of a model-based fault detection and diagnosis system is developed for the Space Shuttle main engine. The design approach consists of process modeling, residual generation, and fault detection and diagnosis. The engine is modeled using a discrete time, quasilinear state-space representation. Model parameters are determined by identification. Residuals generated from the model are used by a neural network to detect and diagnose engine component faults. Fault diagnosis is accomplished by training the neural network to recognize the pattern of the respective fault signatures. Preliminary results for a failed valve, generated using a full, nonlinear simulation of the engine, are presented. These results indicate that the developed approach can be used for fault detection and diagnosis. The results also show that the developed model is an accurate and reliable predictor of the highly nonlinear and very complex engine.

  19. 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.

  20. ENGINEERING PSYCHOLOGY,

    Science.gov (United States)

    MAN MACHINE SYSTEMS, APPLIED PSYCHOLOGY ), INFORMATION THEORY, ELECTROPHYSIOLOGY, HUMAN FACTORS ENGINEERING, PERCEPTION( PSYCHOLOGY ...PSYCHOPHYSIOLOGY, AUTOMATION, BRAIN, AUDITORY PERCEPTION, VISUAL PERCEPTION, MEMORY( PSYCHOLOGY ), MOTOR REACTIONS, NOISE, PERFORMANCE(HUMAN), USSR

  1. 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.

  2. 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.

  3. Coastal Engineering

    OpenAIRE

    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.

  4. Cognitive engineering.

    Science.gov (United States)

    Wilson, Kyle M; Helton, William S; Wiggins, Mark W

    2013-01-01

    Cognitive engineering is the application of cognitive psychology and related disciplines to the design and operation of human-machine systems. Cognitive engineering combines both detailed and close study of the human worker in the actual work context and the study of the worker in more controlled environments. Cognitive engineering combines multiple methods and perspectives to achieve the goal of improved system performance. Given the origins of experimental psychology itself in issues regarding the design of human-machine systems, cognitive engineering is a core, or fundamental, discipline within academic psychology. WIREs Cogn Sci 2013, 4:17-31. doi: 10.1002/wcs.1204 CONFLICT OF INTEREST: The authors declare no conflict of interest. For further resources related to this article, please visit the WIREs website. Copyright © 2012 John Wiley & Sons, Ltd.

  5. 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.

  6. A thesaurus for a neural population code.

    Science.gov (United States)

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2015-09-08

    Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. To accurately decode, the brain must overcome this noise and identify which patterns are semantically similar. We use models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry. This thesaurus reveals that the code is organized in clusters of synonymous activity patterns that are similar in meaning but may differ considerably in their structure. This organization is highly reminiscent of the design of engineered codes. We suggest that the brain may use this structure and show how it allows accurate decoding of novel stimuli from novel spiking patterns.

  7. 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

  8. Geospatial Engineering

    Science.gov (United States)

    2017-02-22

    that enable terrain visualization . Geospatial engineers manipulate the TGD to create the SSGF. The SSGF is the foundation for the Web map service that...decision aids, and visualization products that enable the commander and staff to visualize the operational environment. Geospatial engineers aid in the...data that provides a common framework for visualizing an area of interest (AOI) to enable mission command and the planning and execution of operational

  9. Web Engineering

    OpenAIRE

    Deshpande, Yogesh; Murugesan, San; Ginige, Athula; Hansen, Steve; Schwabe, Daniel; Gaedke, Martin; White, Bebo

    2003-01-01

    Web Engineering is the application of systematic, disciplined and quantifiable approaches to development, operation, and maintenance of Web-based applications. It is both a pro-active approach and a growing collection of theoretical and empirical research in Web application development. This paper gives an overview of Web Engineering by addressing the questions: a) why is it needed? b) what is its domain of operation? c) how does it help and what should it do to improve Web application develo...

  10. Climate engineering

    OpenAIRE

    Platt, Ulrich

    2014-01-01

    Es klingt wie eine Mischung aus Größenwahn und Science Fiction: Wissenschaftler wollen das Klima mit Hightechverfahren beeinflussen. "Climate engineering" heißt der Fachbegriff. Natürlich geht es dabei nicht um den verregneten Sommer, sondern um den globalen Klimawandel. Campus-Reporter Nils Birschmann hat sich bei den Umweltforschern der Uni Heidelberg umgehört, ob was dran ist am "climate engineering". Der Beitrag erschien in der Sendereihe "Campus-Report" - einer Beitragsreihe, in ...

  11. Offshore Engineering

    OpenAIRE

    Vannucci, P

    2008-01-01

    Master; This course on Offshore Engineering is a basic course serving as an introduction to the problems concerning design and construction of offshore platforms, normally used in oil industry. So, some topics will be considered in this course, namely the principal ones that concern the structural design of an offshore platform; some other topics, like for instance marine installation procedures, corrosion protection, facilities and plants engineering will not be considered here. The course i...

  12. 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...

  13. Engineering reaction-diffusion networks with properties of neural tissue.

    Science.gov (United States)

    Litschel, Thomas; Norton, Michael M; Tserunyan, Vardges; Fraden, Seth

    2018-01-03

    We present an experimental system of networks of coupled non-linear chemical reactors, which we theoretically model within a reaction-diffusion framework. The networks consist of patterned arrays of diffusively coupled nanoliter-scale reactors containing the Belousov-Zhabotinsky (BZ) reaction. Microfluidic fabrication techniques are developed that provide the ability to vary the network topology and the reactor coupling strength and offer the freedom to choose whether an arbitrary reactor is inhibitory or excitatory coupled to its neighbor. This versatile experimental and theoretical framework can be used to create a wide variety of chemical networks. Here we design, construct and characterize chemical networks that achieve the complexity of central pattern generators (CPGs), which are found in the autonomic nervous system of a variety of organisms.

  14. An Optoelectronic Neural Network

    Science.gov (United States)

    Neil, Mark A. A.; White, Ian H.; Carroll, John E.

    1990-02-01

    We describe and present results of an optoelectronic neural network processing system. The system uses an algorithm based on the Hebbian learning rule to memorise a set of associated vector pairs. Recall occurs by the processing of the input vector with these stored associations in an incoherent optical vector multiplier using optical polarisation rotating liquid crystal spatial light modulators to store the vectors and an optical polarisation shadow casting technique to perform multiplications. Results are detected on a photodiode array and thresholded electronically by a controlling microcomputer. The processor is shown to work in autoassociative and heteroassociative modes with up to 10 stored memory vectors of length 64 (equivalent to 64 neurons) and a cycle time of 50ms. We discuss the limiting factors at work in this system, how they affect its scalability and the general applicability of its principles to other systems.

  15. 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.

  16. Cortical neural prosthetics.

    Science.gov (United States)

    Schwartz, Andrew B

    2004-01-01

    Control of prostheses using cortical signals is based on three elements: chronic microelectrode arrays, extraction algorithms, and prosthetic effectors. Arrays of microelectrodes are permanently implanted in cerebral cortex. These arrays must record populations of single- and multiunit activity indefinitely. Information containing position and velocity correlates of animate movement needs to be extracted continuously in real time from the recorded activity. Prosthetic arms, the current effectors used in this work, need to have the agility and configuration of natural arms. Demonstrations using closed-loop control show that subjects change their neural activity to improve performance with these devices. Adaptive-learning algorithms that capitalize on these improvements show that this technology has the capability of restoring much of the arm movement lost with immobilizing deficits.

  17. Rehabilitation Engineering: What is Rehabilitation Engineering?

    Science.gov (United States)

    ... Parents/Teachers Resource Links for Students Glossary Rehabilitation Engineering What is rehabilitation engineering? How can future rehabilitation ... the area of rehabilitation engineering? What is rehabilitation engineering? Powered prosthetic leg. Source : M. Goldfarb, Vanderbilt U. ...

  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. A Model for Sustainable Humanitarian Engineering Projects

    Directory of Open Access Journals (Sweden)

    Evan Thomas

    2009-11-01

    Full Text Available The engineering profession should embrace a new mission statement—to contribute to the building of a more sustainable, stable, and equitable world. Recently, engineering students and professionals in the United States have shown strong interest in directly addressing the needs of developing communities worldwide. That interest has taken the form of short-and medium-term international trips through Engineers Without Borders—USA and similar organizations. There are also several instances where this kind of outreach work has been integrated into engineering education at various US institutions such as the University of Colorado at Boulder. This paper addresses the challenges and opportunities associated with balancing two goals in engineering for humanitarian development projects: (i effective sustainable community development, and (ii meaningful education of engineers. Guiding principles necessary to meet those two goals are proposed.

  20. 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.

  1. Storytelling in Engineering Education. Research Brief

    Science.gov (United States)

    Adams, Robin; Allendoerfer, Cheryl; Smith, Tori Rhoulac; Socha, David; Williams, Dawn; Yasuhara, Ken

    2007-01-01

    The Institute for Scholarship on Engineering Education (ISEE) team of the Center for the Advancement of Engineering Education, designed and implemented a 120 minute interactive session called "Communities In Practice--What Are We Learning?" for the 2006 Frontiers in Education Conference in Indianapolis. Six story posters were provided by 8…

  2. 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…

  3. 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.

  4. 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.

  5. Neural Computations in Binaural Hearing

    Science.gov (United States)

    Wagner, Hermann

    Binaural hearing helps humans and animals to localize and unmask sounds. Here, binaural computations in the barn owl's auditory system are discussed. Barn owls use the interaural time difference (ITD) for azimuthal sound localization, and they use the interaural level difference (ELD) for elevational sound localization. ITD and ILD and their precursors are processed in separate neural pathways, the time pathway and the intensity pathway, respectively. Representation of ITD involves four main computational steps, while the representation of ILD is accomplished in three steps. In the discussion neural processing in the owl's auditory system is compared with neural computations present in mammals.

  6. 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/.

  7. 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...

  8. A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation.

    Science.gov (United States)

    Vuković, Najdan; Miljković, Zoran

    2013-10-01

    Radial basis function (RBF) neural network is constructed of certain number of RBF neurons, and these networks are among the most used neural networks for modeling of various nonlinear problems in engineering. Conventional RBF neuron is usually based on Gaussian type of activation function with single width for each activation function. This feature restricts neuron performance for modeling the complex nonlinear problems. To accommodate limitation of a single scale, this paper presents neural network with similar but yet different activation function-hyper basis function (HBF). The HBF allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The HBF is based on generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. Compared to the RBF, the HBF neuron has more parameters to optimize, but HBF neural network needs less number of HBF neurons to memorize relationship between input and output sets in order to achieve good generalization property. However, recent research results of HBF neural network performance have shown that optimal way of constructing this type of neural network is needed; this paper addresses this issue and modifies sequential learning algorithm for HBF neural network that exploits the concept of neuron's significance and allows growing and pruning of HBF neuron during learning process. Extensive experimental study shows that HBF neural network, trained with developed learning algorithm, achieves lower prediction error and more compact neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Neural Manifolds for the Control of Movement.

    Science.gov (United States)

    Gallego, Juan A; Perich, Matthew G; Miller, Lee E; Solla, Sara A

    2017-06-07

    The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the "neural modes." We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Fractal engine

    Science.gov (United States)

    Fatemi, Omid; Panchanathan, Sethuraman

    1997-01-01

    Visual media processing is becoming increasingly important because of the wide variety of image and video based applications. Recently, several architectures have been reported in the literature to implement image and video processing algorithms. They range from programmable DSP processors to application specific integrated circuits (ASICs). DSPs have to be software programed to execute individual operations in image and video processing. However they are not suitable for real-time execution of highly compute intensive applications such as fractal block processing (FBP). On the other hand, dedicated architectures and ASICs are designed to implement specific functions. Since they are optimized for a specific task, they cannot be used in a wide variety of applications. In this paper, we propose a parallel and pipelined architecture called fractal engine to implement the operations in FBP. Fractal engine is simple, modular, scaleable and is optimized to execute both low level and mid level operations. We note that implementation of the basic operations by fractal engine enables efficient execution of a majority of visual computing tasks. These include spatial filtering, contrast enhancement, frequency domain operations, histogram calculation, geometric transforms, indexing, vector quantization, fractal block coding, motion estimation, etc. The individual modules of fractal engine have been implemented in VHDL (VHSIC hardware description language). We have chosen to demonstrate the real-time execution capability of fractal engine by mapping a fractal block coding (FBC) algorithm onto the proposed architecture.

  11. 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

  12. Epidemiology of neural tube defects

    National Research Council Canada - National Science Library

    Seidahmed, Mohammed Z; Abdelbasit, Omar B; Shaheed, Meeralebbae M; Alhussein, Khalid A; Miqdad, Abeer M; Khalil, Mohamed I; Al-Enazy, Naif M; Salih, Mustafa A

    2014-01-01

    To find the prevalence of neural tube defects (NTDs), and compare the findings with local and international data, and highlight the important role of folic acid supplementation and flour fortification with folic acid in preventing NTDs...

  13. Memristor-based neural networks

    Science.gov (United States)

    Thomas, Andy

    2013-03-01

    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.

  14. 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.

  15. CHARGEd with neural crest defects.

    Science.gov (United States)

    Pauli, Silke; Bajpai, Ruchi; Borchers, Annette

    2017-10-30

    Neural crest cells are highly migratory pluripotent cells that give rise to diverse derivatives including cartilage, bone, smooth muscle, pigment, and endocrine cells as well as neurons and glia. Abnormalities in neural crest-derived tissues contribute to the etiology of CHARGE syndrome, a complex malformation disorder that encompasses clinical symptoms like coloboma, heart defects, atresia of the choanae, retarded growth and development, genital hypoplasia, ear anomalies, and deafness. Mutations in the chromodomain helicase DNA-binding protein 7 (CHD7) gene are causative of CHARGE syndrome and loss-of-function data in different model systems have firmly established a role of CHD7 in neural crest development. Here, we will summarize our current understanding of the function of CHD7 in neural crest development and discuss possible links of CHARGE syndrome to other developmental disorders. © 2017 Wiley Periodicals, Inc.

  16. Neural components of altruistic punishment.

    Science.gov (United States)

    Du, Emily; Chang, Steve W C

    2015-01-01

    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.

  17. Pansharpening by Convolutional Neural Networks

    National Research Council Canada - National Science Library

    Masi, Giuseppe; Cozzolino, Davide; Verdoliva, Luisa; Scarpa, Giuseppe

    2016-01-01

    A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem...

  18. 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.

  19. 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,...

  20. 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...

  1. A Convolutional Neural Network Neutrino Event Classifier

    CERN Document Server

    Aurisano, A; Rocco, D; Himmel, A; Messier, M D; Niner, E; Pawloski, G; Psihas, F; Sousa, A; Vahle, P

    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.

  2. 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...

  3. Indices for Testing Neural Codes

    OpenAIRE

    Jonathan D. Victor; Nirenberg, Sheila

    2008-01-01

    One of the most critical challenges in systems neuroscience is determining the neural code. A principled framework for addressing this can be found in information theory. With this approach, one can determine whether a proposed code can account for the stimulus-response relationship. Specifically, one can compare the transmitted information between the stimulus and the hypothesized neural code with the transmitted information between the stimulus and the behavioral response. If the former is ...

  4. Two-photon excitation based photochemistry and neural imaging

    Science.gov (United States)

    Hatch, Kevin Andrew

    Two-photon microscopy is a fluorescence imaging technique which provides distinct advantages in three-dimensional cellular and molecular imaging. The benefits of this technology may extend beyond imaging capabilities through exploitation of the quantum processes responsible for fluorescent events. This study utilized a two-photon microscope to investigate a synthetic photoreactive collagen peptidomimetic, which may serve as a potential material for tissue engineering using the techniques of two-photon photolysis and two-photon polymerization. The combination of these techniques could potentially be used to produce a scaffold for the vascularization of engineered three-dimensional tissues in vitro to address the current limitations of tissue engineering. Additionally, two-photon microscopy was used to observe the effects of the application of the neurotransmitter dopamine to the mushroom body neural structures of Drosophila melanogaster to investigate dopamine's connection to cognitive degeneration.

  5. 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

  6. 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...

  7. Neural-like growing networks

    Science.gov (United States)

    Yashchenko, Vitaliy A.

    2000-03-01

    On the basis of the analysis of scientific ideas reflecting the law in the structure and functioning the biological structures of a brain, and analysis and synthesis of knowledge, developed by various directions in Computer Science, also there were developed the bases of the theory of a new class neural-like growing networks, not having the analogue in world practice. In a base of neural-like growing networks the synthesis of knowledge developed by classical theories - semantic and neural of networks is. The first of them enable to form sense, as objects and connections between them in accordance with construction of the network. With thus each sense gets a separate a component of a network as top, connected to other tops. In common it quite corresponds to structure reflected in a brain, where each obvious concept is presented by certain structure and has designating symbol. Secondly, this network gets increased semantic clearness at the expense owing to formation not only connections between neural by elements, but also themselves of elements as such, i.e. here has a place not simply construction of a network by accommodation sense structures in environment neural of elements, and purely creation of most this environment, as of an equivalent of environment of memory. Thus neural-like growing networks are represented by the convenient apparatus for modeling of mechanisms of teleological thinking, as a fulfillment of certain psychophysiological of functions.

  8. Flexibility of neural stem cells

    Directory of Open Access Journals (Sweden)

    Eumorphia eRemboutsika

    2011-04-01

    Full Text Available Embryonic cortical neural stem cells are self-renewing progenitors that can differentiate into neurons and glia. We generated neurospheres from the developing cerebral cortex using a mouse genetic model that allows for lineage selection and found that the self-renewing neural stem cells are restricted to Sox2 expressing cells. Under normal conditions, embryonic cortical neurospheres are heterogeneous with regard to Sox2 expression and contain astrocytes, neural stem cells and neural progenitor cells sufficiently plastic to give rise to neural crest cells when transplanted into the hindbrain of E1.5 chick and E8 mouse embryos. However, when neurospheres are maintained under lineage selection, such that all cells express Sox2, neural stem cells maintain their Pax6+ cortical radial glia identity and exhibit a more restricted fate in vitro and after transplantation. These data demonstrate that Sox2 preserves the cortical identity and regulates the plasticity of self-renewing Pax6+ radial glia cells.

  9. Enhanced growth of neural networks on conductive cellulose-derived nanofibrous scaffolds

    Energy Technology Data Exchange (ETDEWEB)

    Kuzmenko, Volodymyr [Wallenberg Wood Science Center, Chalmers University of Technology, Kemivägen 4, SE-412 96 Gothenburg (Sweden); Department of Microtechnology and Nanoscience, Chalmers University of Technology, Kemivägen 9, SE-412 96 Gothenburg (Sweden); Kalogeropoulos, Theodoros [Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Kemivägen 4, SE-412 96 Gothenburg (Sweden); Thunberg, Johannes [Wallenberg Wood Science Center, Chalmers University of Technology, Kemivägen 4, SE-412 96 Gothenburg (Sweden); Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Kemivägen 4, SE-412 96 Gothenburg (Sweden); Johannesson, Sara; Hägg, Daniel [Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Kemivägen 4, SE-412 96 Gothenburg (Sweden); Enoksson, Peter [Wallenberg Wood Science Center, Chalmers University of Technology, Kemivägen 4, SE-412 96 Gothenburg (Sweden); Department of Microtechnology and Nanoscience, Chalmers University of Technology, Kemivägen 9, SE-412 96 Gothenburg (Sweden); Gatenholm, Paul, E-mail: paul.gatenholm@chalmers.se [Wallenberg Wood Science Center, Chalmers University of Technology, Kemivägen 4, SE-412 96 Gothenburg (Sweden); Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Kemivägen 4, SE-412 96 Gothenburg (Sweden)

    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.

  10. 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.

  11. Mechanotransduction of Neural Cells Through Cell–Substrate Interactions

    Science.gov (United States)

    Stukel, Jessica M.

    2016-01-01

    Neurons and neural stem cells are sensitive to their mechanical and topographical environment, and cell–substrate binding contributes to this sensitivity to activate signaling pathways for basic cell functions. Many transmembrane proteins transmit signals into and out of the cell, including integrins, growth factor receptors, G-protein-coupled receptors, cadherins, cell adhesion molecules, and ion channels. Specifically, integrins are one of the main transmembrane proteins that transmit force across the cell membrane between a cell and its extracellular matrix, making them critical in the study of cell–material interactions. This review focuses on mechanotransduction, defined as the conversion of force a cell generates through cell–substrate bonds to a chemical signal, of neural cells. The chemical signals relay information via pathways through the cellular cytoplasm to the nucleus, where signaling events can affect gene expression. Pathways and the cellular response initiated by substrate binding are explored to better understand their effect on neural cells mechanotransduction. As the results of mechanotransduction affect cell adhesion, cell shape, and differentiation, knowledge regarding neural mechanotransduction is critical for most regenerative strategies in tissue engineering, where novel environments are developed to improve conduit design for central and peripheral nervous system repair in vivo. PMID:26669274

  12. Mechanotransduction of Neural Cells Through Cell-Substrate Interactions.

    Science.gov (United States)

    Stukel, Jessica M; Willits, Rebecca Kuntz

    2016-06-01

    Neurons and neural stem cells are sensitive to their mechanical and topographical environment, and cell-substrate binding contributes to this sensitivity to activate signaling pathways for basic cell functions. Many transmembrane proteins transmit signals into and out of the cell, including integrins, growth factor receptors, G-protein-coupled receptors, cadherins, cell adhesion molecules, and ion channels. Specifically, integrins are one of the main transmembrane proteins that transmit force across the cell membrane between a cell and its extracellular matrix, making them critical in the study of cell-material interactions. This review focuses on mechanotransduction, defined as the conversion of force a cell generates through cell-substrate bonds to a chemical signal, of neural cells. The chemical signals relay information via pathways through the cellular cytoplasm to the nucleus, where signaling events can affect gene expression. Pathways and the cellular response initiated by substrate binding are explored to better understand their effect on neural cells mechanotransduction. As the results of mechanotransduction affect cell adhesion, cell shape, and differentiation, knowledge regarding neural mechanotransduction is critical for most regenerative strategies in tissue engineering, where novel environments are developed to improve conduit design for central and peripheral nervous system repair in vivo.

  13. 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.

  14. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Ziaul Huque

    2007-08-31

    This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.

  15. Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

    Science.gov (United States)

    Al-Jumeily, Dhiya; Ghazali, Rozaida; Hussain, Abir

    2014-01-01

    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. PMID:25157950

  16. 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.

  17. Pathways to Engineering: The Validation Experiences of Transfer Students

    Science.gov (United States)

    Zhang, Yi; Ozuna, Taryn

    2015-01-01

    Community college engineering transfer students are a critical student population of engineering degree recipients and technical workforce in the United States. Focusing on this group of students, we adopted Rendón's (1994) validation theory to explore the students' experiences in community colleges prior to transferring to a four-year…

  18. Spiking modular neural networks: A neural network modeling approach for hydrological processes

    National Research Council Canada - National Science Library

    Kamban Parasuraman; Amin Elshorbagy; Sean K. Carey

    2006-01-01

    .... In this study, a novel neural network model called the spiking modular neural networks (SMNNs) is proposed. An SMNN consists of an input layer, a spiking layer, and an associator neural network layer...

  19. 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.

  20. Jay Carter Enterprises, Incorporated steam engine

    Science.gov (United States)

    1981-01-01

    The Small Community Solar Thermal Power Experiment (SCSE) selected an organic rankine cycle (ORC) engine driving a high speed permanent magnet alternator (PMA) as the baseline power conversion subsystem (PCS) design. The back-up conceptual PCS design is a steam engine driving an induction alternator delivering power directly to the grid. The development of the automotive reciprocating simple rankine cycle steam engine and how an engine of similar design might be incorporated into the SCSE is discussed. A description of the third generation automotive engine is included along with some preliminary test data. Tests were conducted with the third generation engine driving an induction alternator delivering power directly to the grid. The purpose of these tests is to further verify the effects of expander inlet temperature, input thermal power level, expansion ratio, and other parameters affecting engine performance to aid in the development of an SCSE PCS.

  1. Combustion engineering

    CERN Document Server

    Ragland, Kenneth W

    2011-01-01

    Introduction to Combustion Engineering The Nature of Combustion Combustion Emissions Global Climate Change Sustainability World Energy Production Structure of the Book   Section I: Basic Concepts Fuels Gaseous Fuels Liquid Fuels Solid Fuels Problems Thermodynamics of Combustion Review of First Law Concepts Properties of Mixtures Combustion StoichiometryChemical EnergyChemical EquilibriumAdiabatic Flame TemperatureChemical Kinetics of CombustionElementary ReactionsChain ReactionsGlobal ReactionsNitric Oxide KineticsReactions at a Solid SurfaceProblemsReferences  Section II: Combustion of Gaseous and Vaporized FuelsFlamesLaminar Premixed FlamesLaminar Flame TheoryTurbulent Premixed FlamesExplosion LimitsDiffusion FlamesGas-Fired Furnaces and BoilersEnergy Balance and EfficiencyFuel SubstitutionResidential Gas BurnersIndustrial Gas BurnersUtility Gas BurnersLow Swirl Gas BurnersPremixed-Charge Engine CombustionIntroduction to the Spark Ignition EngineEngine EfficiencyOne-Zone Model of Combustion in a Piston-...

  2. Metabolic Engineering

    Indian Academy of Sciences (India)

    IAS Admin

    processes such as generation of energy, production of fundamen- tal building blocks required for structural organization and syn- thesis of biomolecules having specialized functions. ... Symbiosis International. University, Pune. His research interests are in metabolic engineering of lactic acid bacteria for increasing their.

  3. Harmonic engine

    Science.gov (United States)

    Bennett, Charles L.; Sewall, Noel; Boroa, Carl

    2014-08-19

    An engine based on a reciprocating piston engine that extracts work from pressurized working fluid. The engine includes a harmonic oscillator inlet valve capable of oscillating at a resonant frequency for controlling the flow of working fluid into of the engine. In particular, the inlet valve includes an inlet valve head and a spring arranged together as a harmonic oscillator so that the inlet valve head is moveable from an unbiased equilibrium position to a biased closed position occluding an inlet. Upon releasing the inlet valve the inlet valve head undergoes a single oscillation past the equilibrium positio to a maximum open position and returns to a biased return position close to the closed position to choke the flow and produce a pressure drop across the inlet valve causing the inlet valve to close. Protrusions carried either by the inlet valve head or piston head are used to bump open the inlet valve from the closed position and initiate the single oscillation of the inlet valve head, and protrusions carried either by the outlet valve head or piston head are used to close the outlet valve ahead of the bump opening of the inlet valve.

  4. Exhibit Engineering

    DEFF Research Database (Denmark)

    Mortensen, Marianne Foss

    ) a synthesis of the findings from the first two studies with findings from the literature to generate two types of results: a coherent series of suggestions for a design iteration of the studied exhibit as well as a more general normative model for exhibit engineering. Finally, another perspective...

  5. Acoustic Performance of Exhaust Muffler based Genetic Algorithms and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Wang Xiao Li

    2013-07-01

    Full Text Available The noise level was one of the important indicators as a measure of the quality and performance of the diesel engine, exhaust noise in diesel engines machine noise accounted for an important proportion of installed performance exhaust mufflerwas an effective way to control exhaust noise. This article using orthogonal test program was to the muffler structure parameters as input to the sound pressure level and diesel fuel each output artificial neural network (BP network learning sample. Matlab artificial neural network toolbox to complete the training of the network, and better noise performance and fuel consumption rate performance muffler internal structure parameters combination was obtained through genetic algorithm gifted collaborative validation of artificial neural networks and genetic algorithms to optimize application exhaust muffler design is entirely feasible

  6. Reduced Synchronization Persistence in Neural Networks Derived from Atm-Deficient Mice

    Science.gov (United States)

    Levine-Small, Noah; Yekutieli, Ziv; Aljadeff, Jonathan; Boccaletti, Stefano; Ben-Jacob, Eshel; Barzilai, Ari

    2011-01-01

    Many neurodegenerative diseases are characterized by malfunction of the DNA damage response. Therefore, it is important to understand the connection between system level neural network behavior and DNA. Neural networks drawn from genetically engineered animals, interfaced with micro-electrode arrays allowed us to unveil connections between networks’ system level activity properties and such genome instability. We discovered that Atm protein deficiency, which in humans leads to progressive motor impairment, leads to a reduced synchronization persistence compared to wild type synchronization, after chemically imposed DNA damage. Not only do these results suggest a role for DNA stability in neural network activity, they also establish an experimental paradigm for empirically determining the role a gene plays on the behavior of a neural network. PMID:21519382

  7. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  8. Biomedical engineering for health research and development.

    Science.gov (United States)

    Zhang, X-Y

    2015-01-01

    Biomedical engineering is a new area of research in medicine and biology, providing new concepts and designs for the diagnosis, treatment and prevention of various diseases. There are several types of biomedical engineering, such as tissue, genetic, neural and stem cells, as well as chemical and clinical engineering for health care. Many electronic and magnetic methods and equipments are used for the biomedical engineering such as Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, Electroencephalography (EEG), Ultrasound and regenerative medicine and stem cell cultures, preparations of artificial cells and organs, such as pancreas, urinary bladders, liver cells, and fibroblasts cells of foreskin and others. The principle of tissue engineering is described with various types of cells used for tissue engineering purposes. The use of several medical devices and bionics are mentioned with scaffold, cells and tissue cultures and various materials are used for biomedical engineering. The use of biomedical engineering methods is very important for the human health, and research and development of diseases. The bioreactors and preparations of artificial cells or tissues and organs are described here.

  9. 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.

  10. Enhancing Engineering Education through Engineering Management

    Science.gov (United States)

    Pence, Kenneth R.; Rowe, Christopher J.

    2012-01-01

    Engineering Management courses are added to a traditional engineering curriculum to enhance the value of an undergraduate's engineering degree. A four-year engineering degree often leaves graduates lacking in business and management acumen. Engineering management education covers topics enhancing the value of new graduates by teaching management…

  11. A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Problems With Equality and Inequality Constraints.

    Science.gov (United States)

    Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui

    2017-10-01

    Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.

  12. 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.

  13. Engineering Review Information System

    Science.gov (United States)

    Grems, III, Edward G. (Inventor); Henze, James E. (Inventor); Bixby, Jonathan A. (Inventor); Roberts, Mark (Inventor); Mann, Thomas (Inventor)

    2015-01-01

    A disciplinal engineering review computer information system and method by defining a database of disciplinal engineering review process entities for an enterprise engineering program, opening a computer supported engineering item based upon the defined disciplinal engineering review process entities, managing a review of the opened engineering item according to the defined disciplinal engineering review process entities, and closing the opened engineering item according to the opened engineering item review.

  14. Utilizing Civil Engineering Senior Design Capstone Projects to Evaluate Students' Sustainability Education across Engineering Curriculum

    Science.gov (United States)

    Dancz, Claire L. A.; Ketchman, Kevin J.; Burke, Rebekah D.; Hottle, Troy A.; Parrish, Kristen; Bilec, Melissa M.; Landis, Amy E.

    2017-01-01

    While many institutions express interest in integrating sustainability into their civil engineering curriculum, the engineering community lacks consensus on established methods for infusing sustainability into curriculum and verified approaches to assess engineers' sustainability knowledge. This paper presents the development of a sustainability…

  15. 8th International Conference on Fuzzy Information and Engineering

    CERN Document Server

    Wang, Pei-Zhuang; Liu, Zeng-Liang; Zhong, Yu-Bin

    2016-01-01

    This proceedings book presents edited results of the eighth International Conference on Fuzzy Information and Engineering (ICFIE'2015) and on Oriental Thinking and Fuzzy Logic, in August 17-20, 2015, in Dalian, China. The book contains 65 high-quality papers and is divided into six main parts: "Fuzzy Information Processing", "Fuzzy Engineering", "Internet and Big Data Applications", "Factor Space and Factorial Neural Networks", "Information Granulation and Granular Computing" as well as "Extenics and Innovation Methods".

  16. Myelin plasticity, neural activity, and traumatic neural injury.

    Science.gov (United States)

    Kondiles, Bethany R; Horner, Philip J

    2018-02-01

    The possibility that adult organisms exhibit myelin plasticity has recently become a topic of great interest. Many researchers are exploring the role of myelin growth and adaptation in daily functions such as memory and motor learning. Here we consider evidence for three different potential categories of myelin plasticity: the myelination of previously bare axons, remodeling of existing sheaths, and the removal of a sheath with replacement by a new internode. We also review evidence that points to the importance of neural activity as a mechanism by which oligodendrocyte precursor cells (OPCs) are cued to differentiate into myelinating oligodendrocytes, which may potentially be an important component of myelin plasticity. Finally, we discuss demyelination in the context of traumatic neural injury and present an argument for altering neural activity as a potential therapeutic target for remyelination following injury. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 108-122, 2018. © 2017 Wiley Periodicals, Inc.

  17. Impacts, risks, and governance of climate engineering

    Directory of Open Access Journals (Sweden)

    Zhe Liu

    2015-09-01

    Full Text Available Climate engineering is a potential alternative method to curb global warming, and this discipline has garnered considerable attention from the international scientific community including the Chinese scientists. This manuscript provides an overview of several aspects of climate engineering, including its definition, its potential impacts and risk, and its governance status. The overall conclusion is that China is not yet ready to implement climate engineering. However, it is important for China to continue conducting research on climate engineering, particularly with respect to its feasible application within China, its potential social, economic, and environmental impacts, and possible international governance structures and governing principles, with regard to both experimentation and implementation.

  18. Multigradient for Neural Networks for Equalizers

    Directory of Open Access Journals (Sweden)

    Chulhee Lee

    2003-06-01

    Full Text Available Recently, a new training algorithm, multigradient, has been published for neural networks and it is reported that the multigradient outperforms the backpropagation when neural networks are used as a classifier. When neural networks are used as an equalizer in communications, they can be viewed as a classifier. In this paper, we apply the multigradient algorithm to train the neural networks that are used as equalizers. Experiments show that the neural networks trained using the multigradient noticeably outperforms the neural networks trained by the backpropagation.

  19. Engineering sustainable development

    Energy Technology Data Exchange (ETDEWEB)

    Deitz, D.

    1996-05-01

    This article describes how engineers are forming alliances on the job, in communities, and in international organizations to accelerate economic development while they preserve resources and the environment. Despite the end of the Cold War and the rapid economic development in Asia and Latin America, anxiety abounds as the 21st century dawns. The growth rate of the world`s population remains frighteningly high, and the Earth`s atmosphere appears endangered. Even rays of hope, such as the surge in China`s and India`s economies, cast a shadow on the future by threatening to deplete natural resources even further. In the face of such overwhelming conditions, individual effort may seem futile. There are signs, however, that people are joining forces to do what they can within the limits of what is technologically and economically possible. Although many of them are driven by idealism, a good number are participating to make business more efficient and profitable as well as to enhance their nation`s industrial competitiveness. Their model for change and growth is one that doesn`t endanger the environment--a concept that has come to be known as sustainable development. In the process, engineers are leaving the isolation of their laboratories and individual disciplines to educate, invent, inspire, and join forces with other engineers, community groups, environmentalists, business and labor leaders, and government officials. One sign that such collaborative efforts are succeeding--in addition to the tangible results--is the evolution in thinking about sustainable development, as it applies both to today`s world and to future generations.

  20. Neural correlates of stimulus reportability.

    Science.gov (United States)

    Hulme, Oliver J; Friston, Karl F; Zeki, Semir

    2009-08-01

    Most experiments on the "neural correlates of consciousness" employ stimulus reportability as an operational definition of what is consciously perceived. The interpretation of such experiments therefore depends critically on understanding the neural basis of stimulus reportability. Using a high volume of fMRI data, we investigated the neural correlates of stimulus reportability using a partial report object detection paradigm. Subjects were presented with a random array of circularly arranged disc-stimuli and were cued, after variable delays (following stimulus offset), to report the presence or absence of a disc at the cued location, using variable motor actions. By uncoupling stimulus processing, decision, and motor response, we were able to use signal detection theory to deconstruct the neural basis of stimulus reportability. We show that retinotopically specific responses in the early visual cortex correlate with stimulus processing but not decision or report; a network of parietal/temporal regions correlates with decisions but not stimulus presence, whereas classical motor regions correlate with report. These findings provide a basic framework for understanding the neural basis of stimulus reportability without the theoretical burden of presupposing a relationship between reportability and consciousness.