A “Spike-Based” Grammar Underlies Directional Modification in Network Connectivity: Effect on Bursting Activity and Implications for Bio-Hybrids Systems

PubMed Central

Zullo, Letizia; Chiappalone, Michela; Martinoia, Sergio; Benfenati, Fabio

2012-01-01

Developed biological systems are endowed with the ability of interacting with the environment; they sense the external state and react to it by changing their own internal state. Many attempts have been made to build ‘hybrids’ with the ability of perceiving, modifying and reacting to external modifications. Investigation of the rules that govern network changes in a hybrid system may lead to finding effective methods for ‘programming’ the neural tissue toward a desired task. Here we show a new perspective in the use of cortical neuronal cultures from embryonic mouse as a working platform to study targeted synaptic modifications. Differently from the common timing-based methods applied in bio-hybrids robotics, here we evaluated the importance of endogenous spike timing in the information processing. We characterized the influence of a spike-patterned stimulus in determining changes in neuronal synchronization (connectivity strength and precision) of the evoked spiking and bursting activity in the network. We show that tailoring the stimulation pattern upon a neuronal spike timing induces the network to respond stronger and more precisely to the stimulation. Interestingly, the induced modifications are conveyed more consistently in the burst timing. This increase in strength and precision may be a key in the interaction of the network with the external world and may be used to induce directional changes in bio-hybrid systems. PMID:23145147

Hybrid Percolation Transition in Cluster Merging Processes: Continuously Varying Exponents

NASA Astrophysics Data System (ADS)

Cho, Y. S.; Lee, J. S.; Herrmann, H. J.; Kahng, B.

2016-01-01

Consider growing a network, in which every new connection is made between two disconnected nodes. At least one node is chosen randomly from a subset consisting of g fraction of the entire population in the smallest clusters. Here we show that this simple strategy for improving connection exhibits a more unusual phase transition, namely a hybrid percolation transition exhibiting the properties of both first-order and second-order phase transitions. The cluster size distribution of finite clusters at a transition point exhibits power-law behavior with a continuously varying exponent τ in the range 2 <τ (g )≤2.5 . This pattern reveals a necessary condition for a hybrid transition in cluster aggregation processes, which is comparable to the power-law behavior of the avalanche size distribution arising in models with link-deleting processes in interdependent networks.

Hybrid Analog/Digital Receiver

NASA Technical Reports Server (NTRS)

Brown, D. H.; Hurd, W. J.

1989-01-01

Advanced hybrid analog/digital receiver processes intermediate-frequency (IF) signals carrying digital data in form of phase modulation. Uses IF sampling and digital phase-locked loops to track carrier and subcarrier signals and to synchronize data symbols. Consists of three modules: IF assembly, signal-processing assembly, and test-signal assembly. Intended for use in Deep Space Network, but presumably basic design modified for such terrestrial uses as communications or laboratory instrumentation where signals weak and/or noise strong.

An energy-efficient data gathering protocol in large wireless sensor network

NASA Astrophysics Data System (ADS)

Wang, Yamin; Zhang, Ruihua; Tao, Shizhong

2006-11-01

Wireless sensor network consisting of a large number of small sensors with low-power transceiver can be an effective tool for gathering data in a variety of environment. The collected data must be transmitted to the base station for further processing. Since a network consists of sensors with limited battery energy, the method for data gathering and routing must be energy efficient in order to prolong the lifetime of the network. In this paper, we presented an energy-efficient data gathering protocol in wireless sensor network. The new protocol used data fusion technology clusters nodes into groups and builds a chain among the cluster heads according to a hybrid of the residual energy and distance to the base station. Results in stochastic geometry are used to derive the optimum parameter of our algorithm that minimizes the total energy spent in the network. Simulation results show performance superiority of the new protocol.

Copercolating Networks: An Approach for Realizing High-Performance Transparent Conductors using Multicomponent Nanostructured Networks

NASA Astrophysics Data System (ADS)

Das, Suprem R.; Sadeque, Sajia; Jeong, Changwook; Chen, Ruiyi; Alam, Muhammad A.; Janes, David B.

2016-06-01

Although transparent conductive oxides such as indium tin oxide (ITO) are widely employed as transparent conducting electrodes (TCEs) for applications such as touch screens and displays, new nanostructured TCEs are of interest for future applications, including emerging transparent and flexible electronics. A number of twodimensional networks of nanostructured elements have been reported, including metallic nanowire networks consisting of silver nanowires, metallic carbon nanotubes (m-CNTs), copper nanowires or gold nanowires, and metallic mesh structures. In these single-component systems, it has generally been difficult to achieve sheet resistances that are comparable to ITO at a given broadband optical transparency. A relatively new third category of TCEs consisting of networks of 1D-1D and 1D-2D nanocomposites (such as silver nanowires and CNTs, silver nanowires and polycrystalline graphene, silver nanowires and reduced graphene oxide) have demonstrated TCE performance comparable to, or better than, ITO. In such hybrid networks, copercolation between the two components can lead to relatively low sheet resistances at nanowire densities corresponding to high optical transmittance. This review provides an overview of reported hybrid networks, including a comparison of the performance regimes achievable with those of ITO and single-component nanostructured networks. The performance is compared to that expected from bulk thin films and analyzed in terms of the copercolation model. In addition, performance characteristics relevant for flexible and transparent applications are discussed. The new TCEs are promising, but significant work must be done to ensure earth abundance, stability, and reliability so that they can eventually replace traditional ITO-based transparent conductors.

Effectiveness evaluation of double-layered satellite network with laser and microwave hybrid links based on fuzzy analytic hierarchy process

NASA Astrophysics Data System (ADS)

Zhang, Wei; Rao, Qiaomeng

2018-01-01

In order to solve the problem of high speed, large capacity and limited spectrum resources of satellite communication network, a double-layered satellite network with global seamless coverage based on laser and microwave hybrid links is proposed in this paper. By analyzing the characteristics of the double-layered satellite network with laser and microwave hybrid links, an effectiveness evaluation index system for the network is established. And then, the fuzzy analytic hierarchy process, which combines the analytic hierarchy process and the fuzzy comprehensive evaluation theory, is used to evaluate the effectiveness of the double-layered satellite network with laser and microwave hybrid links. Furthermore, the evaluation result of the proposed hybrid link network is obtained by simulation. The effectiveness evaluation process of the proposed double-layered satellite network with laser and microwave hybrid links can help to optimize the design of hybrid link double-layered satellite network and improve the operating efficiency of the satellite system.

A framework using cluster-based hybrid network architecture for collaborative virtual surgery.

PubMed

Qin, Jing; Choi, Kup-Sze; Poon, Wai-Sang; Heng, Pheng-Ann

2009-12-01

Research on collaborative virtual environments (CVEs) opens the opportunity for simulating the cooperative work in surgical operations. It is however a challenging task to implement a high performance collaborative surgical simulation system because of the difficulty in maintaining state consistency with minimum network latencies, especially when sophisticated deformable models and haptics are involved. In this paper, an integrated framework using cluster-based hybrid network architecture is proposed to support collaborative virtual surgery. Multicast transmission is employed to transmit updated information among participants in order to reduce network latencies, while system consistency is maintained by an administrative server. Reliable multicast is implemented using distributed message acknowledgment based on cluster cooperation and sliding window technique. The robustness of the framework is guaranteed by the failure detection chain which enables smooth transition when participants join and leave the collaboration, including normal and involuntary leaving. Communication overhead is further reduced by implementing a number of management approaches such as computational policies and collaborative mechanisms. The feasibility of the proposed framework is demonstrated by successfully extending an existing standalone orthopedic surgery trainer into a collaborative simulation system. A series of experiments have been conducted to evaluate the system performance. The results demonstrate that the proposed framework is capable of supporting collaborative surgical simulation.

PV-Diesel Hybrid SCADA Experiment Network Design

NASA Technical Reports Server (NTRS)

Kalu, Alex; Durand, S.; Emrich, Carol; Ventre, G.; Wilson, W.; Acosta, R.

1999-01-01

The essential features of an experimental network for renewable power system satellite based supervisory, control and data acquisition (SCADA) are communication links, controllers, diagnostic equipment and a hybrid power system. Required components for implementing the network consist of two satellite ground stations, to satellite modems, two 486 PCs, two telephone receivers, two telephone modems, two analog telephone lines, one digital telephone line, a hybrid-power system equipped with controller and a satellite spacecraft. In the technology verification experiment (TVE) conducted by Savannah State University and Florida Solar Energy Center, the renewable energy hybrid system is the Apex-1000 Mini-Hybrid which is equipped with NGC3188 for user interface and remote control and the NGC2010 for monitoring and basic control tasks. This power system is connected to a satellite modem via a smart interface, RS232. Commands are sent to the power system control unit through a control PC designed as PC1. PC1 is thus connected to a satellite model through RS232. A second PC, designated PC2, the diagnostic PC is connected to both satellite modems via separate analog telephone lines for checking modems'health. PC2 is also connected to PC1 via a telephone line. Due to the unavailability of a second ground station for the ACTS, one ground station is used to serve both the sending and receiving functions in this experiment. Signal is sent from the control PC to the Hybrid system at a frequency f(sub 1), different from f(sub 2), the signal from the hybrid system to the control PC. f(sub l) and f(sub 2) are sufficiently separated to avoid interference.

Modeling of synchronization behavior of bursting neurons at nonlinearly coupled dynamical networks.

PubMed

Çakir, Yüksel

2016-01-01

Synchronization behaviors of bursting neurons coupled through electrical and dynamic chemical synapses are investigated. The Izhikevich model is used with random and small world network of bursting neurons. Various currents which consist of diffusive electrical and time-delayed dynamic chemical synapses are used in the simulations to investigate the influences of synaptic currents and couplings on synchronization behavior of bursting neurons. The effects of parameters, such as time delay, inhibitory synaptic strengths, and decay time on synchronization behavior are investigated. It is observed that in random networks with no delay, bursting synchrony is established with the electrical synapse alone, single spiking synchrony is observed with hybrid coupling. In small world network with no delay, periodic bursting behavior with multiple spikes is observed when only chemical and only electrical synapse exist. Single-spike and multiple-spike bursting are established with hybrid couplings. A decrease in the synchronization measure is observed with zero time delay, as the decay time is increased in random network. For synaptic delays which are above active phase period, synchronization measure increases with an increase in synaptic strength and time delay in small world network. However, in random network, it increases with only an increase in synaptic strength.

Enhancing cycling durability of Li-ion batteries with hierarchical structured silicon-graphene hybrid anodes.

PubMed

Loveridge, Melanie J; Lain, Michael J; Huang, Qianye; Wan, Chaoying; Roberts, Alexander J; Pappas, George S; Bhagat, Rohit

2016-11-09

Hybrid anode materials consisting of micro-sized silicon (Si) particles interconnected with few-layer graphene (FLG) nanoplatelets and sodium-neutralized poly(acrylic acid) as a binder were evaluated for Li-ion batteries. The hybrid film has demonstrated a reversible discharge capacity of ∼1800 mA h g -1 with a capacity retention of 97% after 200 cycles. The superior electrochemical properties of the hybrid anodes are attributed to a durable, hierarchical conductive network formed between Si particles and the multi-scale carbon additives, with enhanced cohesion by the functional polymer binder. Furthermore, improved solid electrolyte interphase (SEI) stability is achieved from the electrolyte additives, due to the formation of a kinetically stable film on the surface of the Si.

The genomic mosaicism of hybrid speciation

PubMed Central

Elgvin, Tore O.; Trier, Cassandra N.; Tørresen, Ole K.; Hagen, Ingerid J.; Lien, Sigbjørn; Nederbragt, Alexander J.; Ravinet, Mark; Jensen, Henrik; Sætre, Glenn-Peter

2017-01-01

Hybridization is widespread in nature and, in some instances, can result in the formation of a new hybrid species. We investigate the genetic foundation of this poorly understood process through whole-genome analysis of the hybrid Italian sparrow and its progenitors. We find overall balanced yet heterogeneous levels of contribution from each parent species throughout the hybrid genome and identify areas of novel divergence in the hybrid species exhibiting signals consistent with balancing selection. High-divergence areas are disproportionately located on the Z chromosome and overrepresented in gene networks relating to key traits separating the focal species, which are likely involved in reproductive barriers and/or species-specific adaptations. Of special interest are genes and functional groups known to affect body patterning, beak morphology, and the immune system, which are important features of diversification and fitness. We show that a combination of mosaic parental inheritance and novel divergence within the hybrid lineage has facilitated the origin and maintenance of an avian hybrid species. PMID:28630911

Fabrication of Self-Healable and Patternable Polypyrrole/Agarose Hybrid Hydrogels for Smart Bioelectrodes.

PubMed

Park, Nokyoung; Chae, Seung Chul; Kim, Il Tae; Hur, Jaehyun

2016-02-01

We present a new class of electrically conductive, mechanically moldable, and thermally self-healable hybrid hydrogels. The hybrid gels consist of polypyrrole and agarose as the conductive component and self-healable matrix, respectively. By using the appropriate oxidizing agent under conditions of mild temperature, the polymerization of pyrrole occurred along the three-dimensional network of the agarose hydrogel matrix. In contrast to most commercially available hydrogels, the physical crosslinking of agarose gel allows for reversible gelation in the case of our hybrid gel, which could be manipulated by temperature variation, which controls the electrical on/off behavior of the hybrid gel electrode. Exploiting this property, we fabricated a hybrid conductive hydrogel electrode which also self-heals thermally. The novel composite material we report here will be useful for many technological and biological applications, especially in reactive biomimetic functions and devices, artificial muscles, smart membranes, smart full organic batteries, and artificial chemical synapses.

Design and construction of a VHGT-attached WDM-type triplex transceiver module using polymer PLC hybrid integration technology

NASA Astrophysics Data System (ADS)

Jerábek, Vitezslav; Hüttel, Ivan; Prajzler, Václav; Busek, K.; Seliger, P.

2008-11-01

We report about design and construction of the bidirectional transceiver TRx module for subscriber part of the passive optical network PON for a fiber to the home FTTH topology. The TRx module consists of a epoxy novolak resin polymer planar lightwave circuit (PLC) hybrid integration technology with volume holographic grating triplex filter VHGT, surface-illuminated photodetectors and spot-size converted Fabry-Pérot laser diode in SMD package. The hybrid PLC has composed from a two parts-polymer optical waveguide including VHGT filter section and a optoelectronic microwave section. The both parts are placed on the composite substrate.

Numerical Modeling of Saturated Boiling in a Heated Tube

NASA Technical Reports Server (NTRS)

Majumdar, Alok; LeClair, Andre; Hartwig, Jason

2017-01-01

This paper describes a mathematical formulation and numerical solution of boiling in a heated tube. The mathematical formulation involves a discretization of the tube into a flow network consisting of fluid nodes and branches and a thermal network consisting of solid nodes and conductors. In the fluid network, the mass, momentum and energy conservation equations are solved and in the thermal network, the energy conservation equation of solids is solved. A pressure-based, finite-volume formulation has been used to solve the equations in the fluid network. The system of equations is solved by a hybrid numerical scheme which solves the mass and momentum conservation equations by a simultaneous Newton-Raphson method and the energy conservation equation by a successive substitution method. The fluid network and thermal network are coupled through heat transfer between the solid and fluid nodes which is computed by Chen's correlation of saturated boiling heat transfer. The computer model is developed using the Generalized Fluid System Simulation Program and the numerical predictions are compared with test data.

Sensing performances of pure and hybridized carbon nanotubes-ZnO nanowire networks: A detailed study.

PubMed

Lupan, Oleg; Schütt, Fabian; Postica, Vasile; Smazna, Daria; Mishra, Yogendra Kumar; Adelung, Rainer

2017-11-07

In this work, the influence of carbon nanotube (CNT) hybridization on ultraviolet (UV) and gas sensing properties of individual and networked ZnO nanowires (NWs) is investigated in detail. The CNT concentration was varied to achieve optimal conditions for the hybrid with improved sensing properties. In case of CNT decorated ZnO nanonetworks, the influence of relative humidity (RH) and applied bias voltage on the UV sensing properties was thoroughly studied. By rising the CNT content to about 2.0 wt% (with respect to the entire ZnO network) the UV sensing response is considerably increased from 150 to 7300 (about 50 times). With respect to gas sensing, the ZnO-CNT networks demonstrate an excellent selectivity as well as a high gas response to NH 3 vapor. A response of 430 to 50 ppm at room temperature was obtained, with an estimated detection limit of about 0.4 ppm. Based on those results, several devices consisting of individual ZnO NWs covered with CNTs were fabricated using a FIB/SEM system. The highest sensing performance was obtained for the finest NW with diameter (D) of 100 nm,  with a response of about 4 to 10 ppm NH 3 vapor at room temperature.

Integrated fast assembly of free-standing lithium titanate/carbon nanotube/cellulose nanofiber hybrid network film as flexible paper-electrode for lithium-ion batteries.

PubMed

Cao, Shaomei; Feng, Xin; Song, Yuanyuan; Xue, Xin; Liu, Hongjiang; Miao, Miao; Fang, Jianhui; Shi, Liyi

2015-05-27

A free-standing lithium titanate (Li4Ti5O12)/carbon nanotube/cellulose nanofiber hybrid network film is successfully assembled by using a pressure-controlled aqueous extrusion process, which is highly efficient and easily to scale up from the perspective of disposable and recyclable device production. This hybrid network film used as a lithium-ion battery (LIB) electrode has a dual-layer structure consisting of Li4Ti5O12/carbon nanotube/cellulose nanofiber composites (hereinafter referred to as LTO/CNT/CNF), and carbon nanotube/cellulose nanofiber composites (hereinafter referred to as CNT/CNF). In the heterogeneous fibrous network of the hybrid film, CNF serves simultaneously as building skeleton and a biosourced binder, which substitutes traditional toxic solvents and synthetic polymer binders. Of importance here is that the CNT/CNF layer is used as a lightweight current collector to replace traditional heavy metal foils, which therefore reduces the total mass of the electrode while keeping the same areal loading of active materials. The free-standing network film with high flexibility is easy to handle, and has extremely good conductivity, up to 15.0 S cm(-1). The flexible paper-electrode for LIBs shows very good high rate cycling performance, and the specific charge/discharge capacity values are up to 142 mAh g(-1) even at a current rate of 10 C. On the basis of the mild condition and fast assembly process, a CNF template fulfills multiple functions in the fabrication of paper-electrode for LIBs, which would offer an ever increasing potential for high energy density, low cost, and environmentally friendly flexible electronics.

Hybrid function projective synchronization in complex dynamical networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Qiang; Wang, Xing-yuan, E-mail: wangxy@dlut.edu.cn; Hu, Xiao-peng

2014-02-15

This paper investigates hybrid function projective synchronization in complex dynamical networks. When the complex dynamical networks could be synchronized up to an equilibrium or periodic orbit, a hybrid feedback controller is designed to realize the different component of vector of node could be synchronized up to different desired scaling function in complex dynamical networks with time delay. Hybrid function projective synchronization (HFPS) in complex dynamical networks with constant delay and HFPS in complex dynamical networks with time-varying coupling delay are researched, respectively. Finally, the numerical simulations show the effectiveness of theoretical analysis.

Transparent Conducting Graphene Hybrid Films To Improve Electromagnetic Interference (EMI) Shielding Performance of Graphene.

PubMed

Ma, Limin; Lu, Zhengang; Tan, Jiubin; Liu, Jian; Ding, Xuemei; Black, Nicola; Li, Tianyi; Gallop, John; Hao, Ling

2017-10-04

Conducting graphene-based hybrids have attracted considerable attention in recent years for their scientific and technological significance in many applications. In this work, conductive graphene hybrid films, consisting of a metallic network fully encapsulated between monolayer graphene and quartz-glass substrate, were fabricated and characterized for their electromagnetic interference shielding capabilities. Experimental results show that by integration with a metallic network the sheet resistance of graphene was significantly suppressed from 813.27 to 5.53 Ω/sq with an optical transmittance at 91%. Consequently, the microwave shielding effectiveness (SE) exceeded 23.60 dB at the K u -band and 13.48 dB at the K a -band. The maximum SE value was 28.91 dB at 12 GHz. Compared with the SE of pristine monolayer graphene (3.46 dB), the SE of graphene hybrid film was enhanced by 25.45 dB (99.7% energy attenuation). At 94% optical transmittance, the sheet resistance was 20.67 Ω/sq and the maximum SE value was 20.86 dB at 12 GHz. Our results show that hybrid graphene films incorporate both high conductivity and superior electromagnetic shielding comparable to existing ITO shielding modalities. The combination of high conductivity and shielding along with the materials' earth-abundant nature, and facile large-scale fabrication, make these graphene hybrid films highly attractive for transparent EMI shielding.

A research using hybrid RBF/Elman neural networks for intrusion detection system secure model

NASA Astrophysics Data System (ADS)

Tong, Xiaojun; Wang, Zhu; Yu, Haining

2009-10-01

A hybrid RBF/Elman neural network model that can be employed for both anomaly detection and misuse detection is presented in this paper. The IDSs using the hybrid neural network can detect temporally dispersed and collaborative attacks effectively because of its memory of past events. The RBF network is employed as a real-time pattern classification and the Elman network is employed to restore the memory of past events. The IDSs using the hybrid neural network are evaluated against the intrusion detection evaluation data sponsored by U.S. Defense Advanced Research Projects Agency (DARPA). Experimental results are presented in ROC curves. Experiments show that the IDSs using this hybrid neural network improve the detection rate and decrease the false positive rate effectively.

Hybridized electromagnetic-triboelectric nanogenerator for scavenging air-flow energy to sustainably power temperature sensors.

PubMed

Wang, Xue; Wang, Shuhua; Yang, Ya; Wang, Zhong Lin

2015-04-28

We report a hybridized nanogenerator with dimensions of 6.7 cm × 4.5 cm × 2 cm and a weight of 42.3 g that consists of two triboelectric nanogenerators (TENGs) and two electromagnetic generators (EMGs) for scavenging air-flow energy. Under an air-flow speed of about 18 m/s, the hybridized nanogenerator can deliver largest output powers of 3.5 mW for one TENG (in correspondence of power per unit mass/volume: 8.8 mW/g and 14.6 kW/m(3)) at a loading resistance of 3 MΩ and 1.8 mW for one EMG (in correspondence of power per unit mass/volume: 0.3 mW/g and 0.4 kW/m(3)) at a loading resistance of 2 kΩ, respectively. The hybridized nanogenerator can be utilized to charge a capacitor of 3300 μF to sustainably power four temperature sensors for realizing self-powered temperature sensor networks. Moreover, a wireless temperature sensor driven by a hybridized nanogenerator charged Li-ion battery can work well to send the temperature data to a receiver/computer at a distance of 1.5 m. This work takes a significant step toward air-flow energy harvesting and its potential applications in self-powered wireless sensor networks.

Quantum key distribution network for multiple applications

NASA Astrophysics Data System (ADS)

Tajima, A.; Kondoh, T.; Ochi, T.; Fujiwara, M.; Yoshino, K.; Iizuka, H.; Sakamoto, T.; Tomita, A.; Shimamura, E.; Asami, S.; Sasaki, M.

2017-09-01

The fundamental architecture and functions of secure key management in a quantum key distribution (QKD) network with enhanced universal interfaces for smooth key sharing between arbitrary two nodes and enabling multiple secure communication applications are proposed. The proposed architecture consists of three layers: a quantum layer, key management layer and key supply layer. We explain the functions of each layer, the key formats in each layer and the key lifecycle for enabling a practical QKD network. A quantum key distribution-advanced encryption standard (QKD-AES) hybrid system and an encrypted smartphone system were developed as secure communication applications on our QKD network. The validity and usefulness of these systems were demonstrated on the Tokyo QKD Network testbed.

A network architecture supporting consistent rich behavior in collaborative interactive applications.

PubMed

Marsh, James; Glencross, Mashhuda; Pettifer, Steve; Hubbold, Roger

2006-01-01

Network architectures for collaborative virtual reality have traditionally been dominated by client-server and peer-to-peer approaches, with peer-to-peer strategies typically being favored where minimizing latency is a priority, and client-server where consistency is key. With increasingly sophisticated behavior models and the demand for better support for haptics, we argue that neither approach provides sufficient support for these scenarios and, thus, a hybrid architecture is required. We discuss the relative performance of different distribution strategies in the face of real network conditions and illustrate the problems they face. Finally, we present an architecture that successfully meets many of these challenges and demonstrate its use in a distributed virtual prototyping application which supports simultaneous collaboration for assembly, maintenance, and training applications utilizing haptics.

Hermes: Seamless delivery of containerized bioinformatics workflows in hybrid cloud (HTC) environments

NASA Astrophysics Data System (ADS)

Kintsakis, Athanassios M.; Psomopoulos, Fotis E.; Symeonidis, Andreas L.; Mitkas, Pericles A.

Hermes introduces a new "describe once, run anywhere" paradigm for the execution of bioinformatics workflows in hybrid cloud environments. It combines the traditional features of parallelization-enabled workflow management systems and of distributed computing platforms in a container-based approach. It offers seamless deployment, overcoming the burden of setting up and configuring the software and network requirements. Most importantly, Hermes fosters the reproducibility of scientific workflows by supporting standardization of the software execution environment, thus leading to consistent scientific workflow results and accelerating scientific output.

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network

PubMed Central

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins. PMID:27418910

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

PubMed

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

Optical back propagation for fiber optic networks with hybrid EDFA Raman amplification.

PubMed

Liang, Xiaojun; Kumar, Shiva

2017-03-06

We have investigated an optical back propagation (OBP) method to compensate for propagation impairments in fiber optic networks with lumped Erbium doped fiber amplifier (EDFA) and/or distributed Raman amplification. An OBP module consists of an optical phase conjugator (OPC), optical amplifiers and dispersion varying fibers (DVFs). We derived a semi-analytical expression that calculates the dispersion profile of DVF. The OBP module acts as a nonlinear filter that fully compensates for the nonlinear distortions due to signal propagation in a transmission fiber, and is applicable for fiber optic networks with reconfigurable optical add-drop multiplexers (ROADMs). We studied a wavelength division multiplexing (WDM) network with 3000 km transmission distance and 64-quadrature amplitude modulation (QAM) modulation. OBP brings 5.8 dB, 5.9 dB and 6.1 dB Q-factor gains over linear compensation for systems with full EDFA amplification, hybrid EDFA/Raman amplification, and full Raman amplification, respectively. In contrast, digital back propagation (DBP) or OPC-only systems provide only 0.8 ~ 1.5 dB Q-factor gains.

Electron beam energy stabilization using a neural network hybrid controller at the Australian Synchrotron Linac.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meier, E.; Morgan, M. J.; Biedron, S. G.

2009-01-01

This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by themore » PI controller to attenuate the remaining perturbations. With a view to control the energy and bunch length at the FERMI{at}Elettra Free Electron Laser (FEL), the present study considers a neural network hybrid feed forward-feedback type of control to rectify limitations related to feedback systems, such as poor response for high jitter frequencies or limited bandwidth, while ensuring robustness of control. The Australian Synchrotron Linac is equipped with a beam position monitor (BPM), that was provided by Sincrotrone Trieste from a former transport line thus allowing energy measurements and energy control experiments. The present study will consequently focus on correcting energy jitter induced by variations in klystron phase and voltage.« less

Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients. A Hybrid Method of Decision Tree and Neural Network.

PubMed

Pourahmad, Saeedeh; Hafizi-Rastani, Iman; Khalili, Hosseinali; Paydar, Shahram

2016-10-17

Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps caregivers with treatment-decision options, or prepares the relatives for the most-likely outcome. This study attempted to determine and order the attributes in prognostic prediction in TBI patients, based on early clinical findings. A hybrid method was employed, which combines a decision tree (DT) and an artificial neural network (ANN) in order to improve the modeling process. The DT approach was applied as the initial analysis of the network architecture to increase accuracy in prediction. Afterwards, the ANN structure was mapped from the initial DT based on a part of the data. Subsequently, the designed network was trained and validated by the remaining data. 5-fold cross-validation method was applied to train the network. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy rate were utilized as performance measures. The important attributes were then determined from the trained network using two methods: change of mean squared error (MSE), and sensitivity analysis (SA). The hybrid method offered better results compared to the DT method. The accuracy rate of 86.3 % vs. 82.2 %, sensitivity value of 55.1 % vs. 47.6 %, specificity value of 93.6 % vs. 91.1 %, and the area under the ROC curve of 0.705 vs. 0.695 were achieved for the hybrid method and DT, respectively. However, the attributes' order by DT method was more consistent with the clinical literature. The combination of different modeling methods can enhance their performance. However, it may create some complexities in computations and interpretations. The outcome of the present study could deliver some useful hints in prognostic prediction on the basis of early clinical findings for TBI patients.

Hybrid adaptive ascent flight control for a flexible launch vehicle

NASA Astrophysics Data System (ADS)

Lefevre, Brian D.

For the purpose of maintaining dynamic stability and improving guidance command tracking performance under off-nominal flight conditions, a hybrid adaptive control scheme is selected and modified for use as a launch vehicle flight controller. This architecture merges a model reference adaptive approach, which utilizes both direct and indirect adaptive elements, with a classical dynamic inversion controller. This structure is chosen for a number of reasons: the properties of the reference model can be easily adjusted to tune the desired handling qualities of the spacecraft, the indirect adaptive element (which consists of an online parameter identification algorithm) continually refines the estimates of the evolving characteristic parameters utilized in the dynamic inversion, and the direct adaptive element (which consists of a neural network) augments the linear feedback signal to compensate for any nonlinearities in the vehicle dynamics. The combination of these elements enables the control system to retain the nonlinear capabilities of an adaptive network while relying heavily on the linear portion of the feedback signal to dictate the dynamic response under most operating conditions. To begin the analysis, the ascent dynamics of a launch vehicle with a single 1st stage rocket motor (typical of the Ares 1 spacecraft) are characterized. The dynamics are then linearized with assumptions that are appropriate for a launch vehicle, so that the resulting equations may be inverted by the flight controller in order to compute the control signals necessary to generate the desired response from the vehicle. Next, the development of the hybrid adaptive launch vehicle ascent flight control architecture is discussed in detail. Alterations of the generic hybrid adaptive control architecture include the incorporation of a command conversion operation which transforms guidance input from quaternion form (as provided by NASA) to the body-fixed angular rate commands needed by the hybrid adaptive flight controller, development of a Newton's method based online parameter update that is modified to include a step size which regulates the rate of change in the parameter estimates, comparison of the modified Newton's method and recursive least squares online parameter update algorithms, modification of the neural network's input structure to accommodate for the nature of the nonlinearities present in a launch vehicle's ascent flight, examination of both tracking error based and modeling error based neural network weight update laws, and integration of feedback filters for the purpose of preventing harmful interaction between the flight control system and flexible structural modes. To validate the hybrid adaptive controller, a high-fidelity Ares I ascent flight simulator and a classical gain-scheduled proportional-integral-derivative (PID) ascent flight controller were obtained from the NASA Marshall Space Flight Center. The classical PID flight controller is used as a benchmark when analyzing the performance of the hybrid adaptive flight controller. Simulations are conducted which model both nominal and off-nominal flight conditions with structural flexibility of the vehicle either enabled or disabled. First, rigid body ascent simulations are performed with the hybrid adaptive controller under nominal flight conditions for the purpose of selecting the update laws which drive the indirect and direct adaptive components. With the neural network disabled, the results revealed that the recursive least squares online parameter update caused high frequency oscillations to appear in the engine gimbal commands. This is highly undesirable for long and slender launch vehicles, such as the Ares I, because such oscillation of the rocket nozzle could excite unstable structural flex modes. In contrast, the modified Newton's method online parameter update produced smooth control signals and was thus selected for use in the hybrid adaptive launch vehicle flight controller. In the simulations where the online parameter identification algorithm was disabled, the tracking error based neural network weight update law forced the network's output to diverge despite repeated reductions of the adaptive learning rate. As a result, the modeling error based neural network weight update law (which generated bounded signals) is utilized by the hybrid adaptive controller in all subsequent simulations. Comparing the PID and hybrid adaptive flight controllers under nominal flight conditions in rigid body ascent simulations showed that their tracking error magnitudes are similar for a period of time during the middle of the ascent phase. Though the PID controller performs better for a short interval around the 20 second mark, the hybrid adaptive controller performs far better from roughly 70 to 120 seconds. Elevating the aerodynamic loads by increasing the force and moment coefficients produced results very similar to the nominal case. However, applying a 5% or 10% thrust reduction to the first stage rocket motor causes the tracking error magnitude observed by the PID controller to be significantly elevated and diverge rapidly as the simulation concludes. In contrast, the hybrid adaptive controller steadily maintains smaller errors (often less than 50% of the corresponding PID value). Under the same sets of flight conditions with flexibility enabled, the results exhibit similar trends with the hybrid adaptive controller performing even better in each case. Again, the reduction of the first stage rocket motor's thrust clearly illustrated the superior robustness of the hybrid adaptive flight controller.

Information transmission on hybrid networks

NASA Astrophysics Data System (ADS)

Chen, Rongbin; Cui, Wei; Pu, Cunlai; Li, Jie; Ji, Bo; Gakis, Konstantinos; Pardalos, Panos M.

2018-01-01

Many real-world communication networks often have hybrid nature with both fixed nodes and moving modes, such as the mobile phone networks mainly composed of fixed base stations and mobile phones. In this paper, we discuss the information transmission process on the hybrid networks with both fixed and mobile nodes. The fixed nodes (base stations) are connected as a spatial lattice on the plane forming the information-carrying backbone, while the mobile nodes (users), which are the sources and destinations of information packets, connect to their current nearest fixed nodes respectively to deliver and receive information packets. We observe the phase transition of traffic load in the hybrid network when the packet generation rate goes from below and then above a critical value, which measures the network capacity of packets delivery. We obtain the optimal speed of moving nodes leading to the maximum network capacity. We further improve the network capacity by rewiring the fixed nodes and by considering the current load of fixed nodes during packets transmission. Our purpose is to optimize the network capacity of hybrid networks from the perspective of network science, and provide some insights for the construction of future communication infrastructures.

Development of module for neural network identification of attacks on applications and services in multi-cloud platforms

NASA Astrophysics Data System (ADS)

Parfenov, D. I.; Bolodurina, I. P.

2018-05-01

The article presents the results of developing an approach to detecting and protecting against network attacks on the corporate infrastructure deployed on the multi-cloud platform. The proposed approach is based on the combination of two technologies: a softwareconfigurable network and virtualization of network functions. The approach for searching for anomalous traffic is to use a hybrid neural network consisting of a self-organizing Kohonen network and a multilayer perceptron. The study of the work of the prototype of the system for detecting attacks, the method of forming a learning sample, and the course of experiments are described. The study showed that using the proposed approach makes it possible to increase the effectiveness of the obfuscation of various types of attacks and at the same time does not reduce the performance of the network

An Empirical Investigation of Entrepreneurship Intensity in Iranian State Universities

ERIC Educational Resources Information Center

Mazdeh, Mohammad Mahdavi; Razavi, Seyed-Mostafa; Hesamamiri, Roozbeh; Zahedi, Mohammad-Reza; Elahi, Behin

2013-01-01

The purpose of this study is to propose a framework to evaluate the entrepreneurship intensity (EI) of Iranian state universities. In order to determine EI, a hybrid multi-method framework consisting of Delphi, Analytic Network Process (ANP), and VIKOR is proposed. The Delphi method is used to localize and reduce the number of criteria extracted…

Identification of hybrid node and link communities in complex networks

PubMed Central

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-01-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

Identification of hybrid node and link communities in complex networks.

PubMed

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-02

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Identification of hybrid node and link communities in complex networks

NASA Astrophysics Data System (ADS)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Concept and Design of the Hybrid Sensor Bus System for Telecommunication Satellites

NASA Astrophysics Data System (ADS)

Hurni, Andreas; Tiefenbeck, Christoph; Manhart, Markus; Heyer, Heinz-Volker; Plattner, Markus; Putzer, Philipp; Roßner, Max; Koch, Alexander W.; Furano, Gianluca; McKenzie, Iain; Lam, King

2012-08-01

The Hybrid Sensor Bus (HSB) is a system for sensor interrogation in telecommunication satellites, which will be developed in the frame of the ESA ARTES program. The main target of the HSB system is the replacement of classical point-to-point wired sensors by sensors connected on bus networks. This will save mass and reduces efforts in assembly, integration and testing (AIT). The HSB system is able to manage an electrical I2C and a fiber-optical sensor network. The system consists of an intelligent power module, an electrical and a fiber-optical interrogator module in cold redundancy. Additional features of the HSB system are its modularity and the adaptability to different satellite platforms. The implementation of a HSB system allows platform manufacturers to build a more cost efficient satellite.This paper presents the concept and the design status of the HSB system.

SoilNet - A hybrid underground wireless sensor network for near real-time monitoring of hydrological processes

NASA Astrophysics Data System (ADS)

Bogena, H. R.; Huisman, S.; Rosenbaum, U.; Wuethen, A.; Vereecken, H.

2009-04-01

Wireless sensor network technology allows near real-time monitoring of soil properties with a high spatial and temporal resolution for observing hydrological processes in small watersheds. The novel wireless sensor network SoilNet uses the low-cost ZigBee radio network for communication and a hybrid topology with a mixture of underground end devices each wired to several soil sensors and aboveground router devices. The SoilNet sensor network consists of soil water content, salinity and temperature sensors attached to end devices by cables, router devices and a coordinator device. The end devices are buried in the soil and linked wirelessly with nearby aboveground router devices. This ZigBee network design considers channel errors, delays, packet losses, and power and topology constraints. In order to conserve battery power, a reactive routing protocol is used that determines a new route only when it is required. The sensor network is also able to react to external influences, e.g. the occurrence of precipitation. The SoilNet communicator, routing and end devices have been developed by the Forschungszentrum Juelich and will be marketed through external companies. Simultaneously, we have also developed a data management and visualisation system. Recently, a small forest catchment Wüstebach (27 ha) was instrumented with 50 end devices and more than 400 soil sensors in the frame of the TERENO-RUR hydrological observatory. We will present first results of this large sensor network both in terms of spatial-temporal variations in soil water content and the performance of the sensor network (e.g. network stability and power use).

Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees.

PubMed

Mirzaei, Sajad; Wu, Yufeng

2016-01-01

Hybridization networks represent plausible evolutionary histories of species that are affected by reticulate evolutionary processes. An established computational problem on hybridization networks is constructing the most parsimonious hybridization network such that each of the given phylogenetic trees (called gene trees) is "displayed" in the network. There have been several previous approaches, including an exact method and several heuristics, for this NP-hard problem. However, the exact method is only applicable to a limited range of data, and heuristic methods can be less accurate and also slow sometimes. In this paper, we develop a new algorithm for constructing near parsimonious networks for multiple binary gene trees. This method is more efficient for large numbers of gene trees than previous heuristics. This new method also produces more parsimonious results on many simulated datasets as well as a real biological dataset than a previous method. We also show that our method produces topologically more accurate networks for many datasets.

Research on key technology of planning and design for AC/DC hybrid distribution network

NASA Astrophysics Data System (ADS)

Shen, Yu; Wu, Guilian; Zheng, Huan; Deng, Junpeng; Shi, Pengjia

2018-04-01

With the increasing demand of DC generation and DC load, the development of DC technology, AC and DC distribution network integrating will become an important form of future distribution network. In this paper, the key technology of planning and design for AC/DC hybrid distribution network is proposed, including the selection of AC and DC voltage series, the design of typical grid structure and the comprehensive evaluation method of planning scheme. The research results provide some ideas and directions for the future development of AC/DC hybrid distribution network.

Computing all hybridization networks for multiple binary phylogenetic input trees.

PubMed

Albrecht, Benjamin

2015-07-30

The computation of phylogenetic trees on the same set of species that are based on different orthologous genes can lead to incongruent trees. One possible explanation for this behavior are interspecific hybridization events recombining genes of different species. An important approach to analyze such events is the computation of hybridization networks. This work presents the first algorithm computing the hybridization number as well as a set of representative hybridization networks for multiple binary phylogenetic input trees on the same set of taxa. To improve its practical runtime, we show how this algorithm can be parallelized. Moreover, we demonstrate the efficiency of the software Hybroscale, containing an implementation of our algorithm, by comparing it to PIRNv2.0, which is so far the best available software computing the exact hybridization number for multiple binary phylogenetic trees on the same set of taxa. The algorithm is part of the software Hybroscale, which was developed specifically for the investigation of hybridization networks including their computation and visualization. Hybroscale is freely available(1) and runs on all three major operating systems. Our simulation study indicates that our approach is on average 100 times faster than PIRNv2.0. Moreover, we show how Hybroscale improves the interpretation of the reported hybridization networks by adding certain features to its graphical representation.

Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks

PubMed Central

Wei, Yunkai; Ma, Xiaohui; Yang, Ning; Chen, Yijin

2017-01-01

Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller’s direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20–40% while ensuring feasible data delay. PMID:28914816

Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks.

PubMed

Wei, Yunkai; Ma, Xiaohui; Yang, Ning; Chen, Yijin

2017-09-15

Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller's direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20-40% while ensuring feasible data delay.

Simulation-Based Evaluation of Hybridization Network Reconstruction Methods in the Presence of Incomplete Lineage Sorting

PubMed Central

Kamneva, Olga K; Rosenberg, Noah A

2017-01-01

Hybridization events generate reticulate species relationships, giving rise to species networks rather than species trees. We report a comparative study of consensus, maximum parsimony, and maximum likelihood methods of species network reconstruction using gene trees simulated assuming a known species history. We evaluate the role of the divergence time between species involved in a hybridization event, the relative contributions of the hybridizing species, and the error in gene tree estimation. When gene tree discordance is mostly due to hybridization and not due to incomplete lineage sorting (ILS), most of the methods can detect even highly skewed hybridization events between highly divergent species. For recent divergences between hybridizing species, when the influence of ILS is sufficiently high, likelihood methods outperform parsimony and consensus methods, which erroneously identify extra hybridizations. The more sophisticated likelihood methods, however, are affected by gene tree errors to a greater extent than are consensus and parsimony. PMID:28469378

Finite-time hybrid projective synchronization of the drive-response complex networks with distributed-delay via adaptive intermittent control

NASA Astrophysics Data System (ADS)

Cheng, Lin; Yang, Yongqing; Li, Li; Sui, Xin

2018-06-01

This paper studies the finite-time hybrid projective synchronization of the drive-response complex networks. In the model, general transmission delays and distributed delays are also considered. By designing the adaptive intermittent controllers, the response network can achieve hybrid projective synchronization with the drive system in finite time. Based on finite-time stability theory and several differential inequalities, some simple finite-time hybrid projective synchronization criteria are derived. Two numerical examples are given to illustrate the effectiveness of the proposed method.

Nonlinear mechanics of hybrid polymer networks that mimic the complex mechanical environment of cells

NASA Astrophysics Data System (ADS)

Jaspers, Maarten; Vaessen, Sarah L.; van Schayik, Pim; Voerman, Dion; Rowan, Alan E.; Kouwer, Paul H. J.

2017-05-01

The mechanical properties of cells and the extracellular environment they reside in are governed by a complex interplay of biopolymers. These biopolymers, which possess a wide range of stiffnesses, self-assemble into fibrous composite networks such as the cytoskeleton and extracellular matrix. They interact with each other both physically and chemically to create a highly responsive and adaptive mechanical environment that stiffens when stressed or strained. Here we show that hybrid networks of a synthetic mimic of biological networks and either stiff, flexible and semi-flexible components, even very low concentrations of these added components, strongly affect the network stiffness and/or its strain-responsive character. The stiffness (persistence length) of the second network, its concentration and the interaction between the components are all parameters that can be used to tune the mechanics of the hybrids. The equivalence of these hybrids with biological composites is striking.

Hybrid services efficient provisioning over the network coding-enabled elastic optical networks

NASA Astrophysics Data System (ADS)

Wang, Xin; Gu, Rentao; Ji, Yuefeng; Kavehrad, Mohsen

2017-03-01

As a variety of services have emerged, hybrid services have become more common in real optical networks. Although the elastic spectrum resource optimizations over the elastic optical networks (EONs) have been widely investigated, little research has been carried out on the hybrid services of the routing and spectrum allocation (RSA), especially over the network coding-enabled EON. We investigated the RSA for the unicast service and network coding-based multicast service over the network coding-enabled EON with the constraints of time delay and transmission distance. To address this issue, a mathematical model was built to minimize the total spectrum consumption for the hybrid services over the network coding-enabled EON under the constraints of time delay and transmission distance. The model guarantees different routing constraints for different types of services. The immediate nodes over the network coding-enabled EON are assumed to be capable of encoding the flows for different kinds of information. We proposed an efficient heuristic algorithm of the network coding-based adaptive routing and layered graph-based spectrum allocation algorithm (NCAR-LGSA). From the simulation results, NCAR-LGSA shows highly efficient performances in terms of the spectrum resources utilization under different network scenarios compared with the benchmark algorithms.

Multiple imputation of rainfall missing data in the Iberian Mediterranean context

NASA Astrophysics Data System (ADS)

Miró, Juan Javier; Caselles, Vicente; Estrela, María José

2017-11-01

Given the increasing need for complete rainfall data networks, in recent years have been proposed diverse methods for filling gaps in observed precipitation series, progressively more advanced that traditional approaches to overcome the problem. The present study has consisted in validate 10 methods (6 linear, 2 non-linear and 2 hybrid) that allow multiple imputation, i.e., fill at the same time missing data of multiple incomplete series in a dense network of neighboring stations. These were applied for daily and monthly rainfall in two sectors in the Júcar River Basin Authority (east Iberian Peninsula), which is characterized by a high spatial irregularity and difficulty of rainfall estimation. A classification of precipitation according to their genetic origin was applied as pre-processing, and a quantile-mapping adjusting as post-processing technique. The results showed in general a better performance for the non-linear and hybrid methods, highlighting that the non-linear PCA (NLPCA) method outperforms considerably the Self Organizing Maps (SOM) method within non-linear approaches. On linear methods, the Regularized Expectation Maximization method (RegEM) was the best, but far from NLPCA. Applying EOF filtering as post-processing of NLPCA (hybrid approach) yielded the best results.

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

Simulator of Space Communication Networks

NASA Technical Reports Server (NTRS)

Clare, Loren; Jennings, Esther; Gao, Jay; Segui, John; Kwong, Winston

2005-01-01

Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) is a suite of software tools that simulates the behaviors of communication networks to be used in space exploration, and predict the performance of established and emerging space communication protocols and services. MACHETE consists of four general software systems: (1) a system for kinematic modeling of planetary and spacecraft motions; (2) a system for characterizing the engineering impact on the bandwidth and reliability of deep-space and in-situ communication links; (3) a system for generating traffic loads and modeling of protocol behaviors and state machines; and (4) a system of user-interface for performance metric visualizations. The kinematic-modeling system makes it possible to characterize space link connectivity effects, including occultations and signal losses arising from dynamic slant-range changes and antenna radiation patterns. The link-engineering system also accounts for antenna radiation patterns and other phenomena, including modulations, data rates, coding, noise, and multipath fading. The protocol system utilizes information from the kinematic-modeling and link-engineering systems to simulate operational scenarios of space missions and evaluate overall network performance. In addition, a Communications Effect Server (CES) interface for MACHETE has been developed to facilitate hybrid simulation of space communication networks with actual flight/ground software/hardware embedded in the overall system.

Integration of hybrid wireless networks in cloud services oriented enterprise information systems

NASA Astrophysics Data System (ADS)

Li, Shancang; Xu, Lida; Wang, Xinheng; Wang, Jue

2012-05-01

This article presents a hybrid wireless network integration scheme in cloud services-based enterprise information systems (EISs). With the emerging hybrid wireless networks and cloud computing technologies, it is necessary to develop a scheme that can seamlessly integrate these new technologies into existing EISs. By combining the hybrid wireless networks and computing in EIS, a new framework is proposed, which includes frontend layer, middle layer and backend layers connected to IP EISs. Based on a collaborative architecture, cloud services management framework and process diagram are presented. As a key feature, the proposed approach integrates access control functionalities within the hybrid framework that provide users with filtered views on available cloud services based on cloud service access requirements and user security credentials. In future work, we will implement the proposed framework over SwanMesh platform by integrating the UPnP standard into an enterprise information system.

An efficient hybrid approach for multiobjective optimization of water distribution systems

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2014-05-01

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

Exponential Synchronization of Networked Chaotic Delayed Neural Network by a Hybrid Event Trigger Scheme.

PubMed

Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun; Zhongyang Fei; Chaoxu Guan; Huijun Gao; Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun

2018-06-01

This paper is concerned with the exponential synchronization for master-slave chaotic delayed neural network with event trigger control scheme. The model is established on a network control framework, where both external disturbance and network-induced delay are taken into consideration. The desired aim is to synchronize the master and slave systems with limited communication capacity and network bandwidth. In order to save the network resource, we adopt a hybrid event trigger approach, which not only reduces the data package sending out, but also gets rid of the Zeno phenomenon. By using an appropriate Lyapunov functional, a sufficient criterion for the stability is proposed for the error system with extended ( , , )-dissipativity performance index. Moreover, hybrid event trigger scheme and controller are codesigned for network-based delayed neural network to guarantee the exponential synchronization between the master and slave systems. The effectiveness and potential of the proposed results are demonstrated through a numerical example.

Joint Optimization of Receiver Placement and Illuminator Selection for a Multiband Passive Radar Network.

PubMed

Xie, Rui; Wan, Xianrong; Hong, Sheng; Yi, Jianxin

2017-06-14

The performance of a passive radar network can be greatly improved by an optimal radar network structure. Generally, radar network structure optimization consists of two aspects, namely the placement of receivers in suitable places and selection of appropriate illuminators. The present study investigates issues concerning the joint optimization of receiver placement and illuminator selection for a passive radar network. Firstly, the required radar cross section (RCS) for target detection is chosen as the performance metric, and the joint optimization model boils down to the partition p -center problem (PPCP). The PPCP is then solved by a proposed bisection algorithm. The key of the bisection algorithm lies in solving the partition set covering problem (PSCP), which can be solved by a hybrid algorithm developed by coupling the convex optimization with the greedy dropping algorithm. In the end, the performance of the proposed algorithm is validated via numerical simulations.

Things fall apart: biological species form unconnected parsimony networks.

PubMed

Hart, Michael W; Sunday, Jennifer

2007-10-22

The generality of operational species definitions is limited by problematic definitions of between-species divergence. A recent phylogenetic species concept based on a simple objective measure of statistically significant genetic differentiation uses between-species application of statistical parsimony networks that are typically used for population genetic analysis within species. Here we review recent phylogeographic studies and reanalyse several mtDNA barcoding studies using this method. We found that (i) alignments of DNA sequences typically fall apart into a separate subnetwork for each Linnean species (but with a higher rate of true positives for mtDNA data) and (ii) DNA sequences from single species typically stick together in a single haplotype network. Departures from these patterns are usually consistent with hybridization or cryptic species diversity.

Modeling Music Emotion Judgments Using Machine Learning Methods

PubMed Central

Vempala, Naresh N.; Russo, Frank A.

2018-01-01

Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML) methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion. PMID:29354080

Modeling Music Emotion Judgments Using Machine Learning Methods.

PubMed

Vempala, Naresh N; Russo, Frank A

2017-01-01

Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML) methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

Three-dimensional Aerographite-GaN hybrid networks: Single step fabrication of porous and mechanically flexible materials for multifunctional applications

PubMed Central

Schuchardt, Arnim; Braniste, Tudor; Mishra, Yogendra K.; Deng, Mao; Mecklenburg, Matthias; Stevens-Kalceff, Marion A.; Raevschi, Simion; Schulte, Karl; Kienle, Lorenz; Adelung, Rainer; Tiginyanu, Ion

2015-01-01

Three dimensional (3D) elastic hybrid networks built from interconnected nano- and microstructure building units, in the form of semiconducting-carbonaceous materials, are potential candidates for advanced technological applications. However, fabrication of these 3D hybrid networks by simple and versatile methods is a challenging task due to the involvement of complex and multiple synthesis processes. In this paper, we demonstrate the growth of Aerographite-GaN 3D hybrid networks using ultralight and extremely porous carbon based Aerographite material as templates by a single step hydride vapor phase epitaxy process. The GaN nano- and microstructures grow on the surface of Aerographite tubes and follow the network architecture of the Aerographite template without agglomeration. The synthesized 3D networks are integrated with the properties from both, i.e., nanoscale GaN structures and Aerographite in the form of flexible and semiconducting composites which could be exploited as next generation materials for electronic, photonic, and sensors applications. PMID:25744694

A Hybrid Authentication and Authorization Process for Control System Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Manz, David O.; Edgar, Thomas W.; Fink, Glenn A.

2010-08-25

Convergence of control system and IT networks require that security, privacy, and trust be addressed. Trust management continues to plague traditional IT managers and is even more complex when extended into control system networks, with potentially millions of entities, a mission that requires 100% availability. Yet these very networks necessitate a trusted secure environment where controllers and managers can be assured that the systems are secure and functioning properly. We propose a hybrid authentication management protocol that addresses the unique issues inherent within control system networks, while leveraging the considerable research and momentum in existing IT authentication schemes. Our hybridmore » authentication protocol for control systems provides end device to end device authentication within a remote station and between remote stations and control centers. Additionally, the hybrid protocol is failsafe and will not interrupt communication or control of vital systems in a network partition or device failure. Finally, the hybrid protocol is resilient to transitory link loss and can operate in an island mode until connectivity is reestablished.« less

Three-dimensional carbon allotropes comprising phenyl rings and acetylenic chains in sp+ sp 2 hybrid networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jian -Tao; Chen, Changfeng; Li, Han -Dong

Here, we here identify by ab initio calculations a new type of three-dimensional (3D) carbon allotropes that consist of phenyl rings connected by linear acetylenic chains in sp+ sp 2 bonding networks. These structures are constructed by inserting acetylenic or diacetylenic bonds into an all sp 2-hybridized rhombohedral polybenzene lattice, and the resulting 3D phenylacetylene and phenyldiacetylene nets comprise a 12-atom and 18-atom rhombohedral primitive unit cells R - 3m symmetry, which are characterized as the 3D chiral crystalline modification of 2D graphyne and graphdiyne, respectively. Simulated phonon spectra reveal that these structures are dynamically stable. Electronic band calculations indicatemore » that phenylacetylene is metallic, while phenyldiacetylene is a semiconductor with an indirect band gap of 0.58 eV. The present results establish a new type of carbon phases and offer insights into their outstanding structural and electronic properties.« less

Three-dimensional carbon allotropes comprising phenyl rings and acetylenic chains in sp+ sp 2 hybrid networks

DOE PAGES

Wang, Jian -Tao; Chen, Changfeng; Li, Han -Dong; ...

2016-04-18

Here, we here identify by ab initio calculations a new type of three-dimensional (3D) carbon allotropes that consist of phenyl rings connected by linear acetylenic chains in sp+ sp 2 bonding networks. These structures are constructed by inserting acetylenic or diacetylenic bonds into an all sp 2-hybridized rhombohedral polybenzene lattice, and the resulting 3D phenylacetylene and phenyldiacetylene nets comprise a 12-atom and 18-atom rhombohedral primitive unit cells R - 3m symmetry, which are characterized as the 3D chiral crystalline modification of 2D graphyne and graphdiyne, respectively. Simulated phonon spectra reveal that these structures are dynamically stable. Electronic band calculations indicatemore » that phenylacetylene is metallic, while phenyldiacetylene is a semiconductor with an indirect band gap of 0.58 eV. The present results establish a new type of carbon phases and offer insights into their outstanding structural and electronic properties.« less

Fabrication of Uniform Hydrogel Microparticles with Alternate Polyelectrolyte/Silica Shell Layers for Applications of Controlled Loading and Releasing

NASA Astrophysics Data System (ADS)

Jeong, Eun Sook; Kim, Jin Woong

2015-03-01

Hydrogel particles, also known as microgels, consist of cross-linked three-dimensional water-soluble polymer networks. They play an essential role in loading and delivering active ingredients in medicine, cosmetics, and foods. Despite their excellent biocompatibility as well as structural diversity, much wider applications are limited due mainly to their intrinsically loose network nature. This study introduces a practical and straightforward method that enables fabrication of hydrogel microparticles layered with a mechanically robust hybrid thin shell. Basically highly monodisperse hydrogel microparticles were produced in microcapillary devices. Then, their surface was coated with alternate polyelectrolyte layers through the layer-by-layer deposition. Finally a thin silica layer was again formed by reduction of silicate on the amino-functionalized polyelectrolyte layer. We have figured out that these hybrid hydrogel microparticles showed controlled loading and releasing behaviors for water-soluble probe molecules. Moreover, we have demonstrated that they can be applied for immobilization of biomacromolecules, such as bacteria and living cells, and even for targeted releasing.

Exploring the evolution of London's street network in the information space: A dual approach

NASA Astrophysics Data System (ADS)

Masucci, A. Paolo; Stanilov, Kiril; Batty, Michael

2014-01-01

We study the growth of London's street network in its dual representation, as the city has evolved over the past 224 years. The dual representation of a planar graph is a content-based network, where each node is a set of edges of the planar graph and represents a transportation unit in the so-called information space, i.e., the space where information is handled in order to navigate through the city. First, we discuss a novel hybrid technique to extract dual graphs from planar graphs, called the hierarchical intersection continuity negotiation principle. Then we show that the growth of the network can be analytically described by logistic laws and that the topological properties of the network are governed by robust log-normal distributions characterizing the network's connectivity and small-world properties that are consistent over time. Moreover, we find that the double-Pareto-like distributions for the connectivity emerge for major roads and can be modeled via a stochastic content-based network model using simple space-filling principles.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Transition Features from Simplicity-Universality to Complexity-Diversification Under UHNTF

NASA Astrophysics Data System (ADS)

Fang, Jin-Qing; Li, Yong

2010-02-01

A large unified hybrid network model with a variable speed growth (LUHNM-VSG) is proposed as third model of the unified hybrid network theoretical framework (UHNTF). A hybrid growth ratio vg of deterministic linking number to random linking number and variable speed growth index α are introduced in it. The main effects of vg and α on topological transition features of the LUHNM-VSG are revealed. For comparison with the other models, we construct a type of the network complexity pyramid with seven levels, in which from the bottom level-1 to the top level-7 of the pyramid simplicity-universality is increasing but complexity-diversity is decreasing. The transition relations between them depend on matching of four hybrid ratios (dr, fd, gr, vg). Thus the most of network models can be investigated in the unification way via four hybrid ratios (dr, fd, gr, vg). The LUHNM-VSG as the level-1 of the pyramid is much better and closer to description of real-world networks as well as has potential application.

Transformation of metal-organic framework to polymer gel by cross-linking the organic ligands preorganized in metal-organic framework.

PubMed

Ishiwata, Takumi; Furukawa, Yuki; Sugikawa, Kouta; Kokado, Kenta; Sada, Kazuki

2013-04-10

Until now, seamless fusion of metal-organic frameworks (MOFs) and covalently cross-linked polymer gels (PG) at molecular level has been extremely rare, since these two matters have been regarded as opposite, that is, hard versus soft. In this report, we demonstrate transformation of cubic MOF crystals to PG via inner cross-linking of the organic linkers in the void space of MOF, followed by decomposition of the metal coordination. The obtained PG behaved as a polyelectrolyte gel, indicating the high content of ionic groups inside. Metal ions were well adsorbed in the PG due to its densely packed carboxylate groups. A chimera-type hybrid material consisting of MOF and PG was obtained by partial hydrolysis of resulting cross-linked MOF. The shape of resulting PG network well reflected the crystal structure of MOF employed as a template. Our results will connect the two different network materials that have been ever studied in the two different fields to provide new soft and hard hybrid materials, and the unique copolymerization in the large void space of the MOF will open a new horizon toward "ideal network polymers" never prepared before now.

Ultrathin single-crystalline TiO2 nanosheets anchored on graphene to be hybrid network for high-rate and long cycle-life sodium battery electrode application

NASA Astrophysics Data System (ADS)

Shoaib, Anwer; Huang, Yongxin; Liu, Jia; Liu, Jiajia; Xu, Meng; Wang, Ziheng; Chen, Renjie; Zhang, Jiatao; Wu, Feng

2017-02-01

In view of the growing concern about energy management issues, sodium ion batteries (SIBs) as cheap and environmentally friendly devices have increasingly received wide research attentions. The high current rate and long cycle-life of SIBs are considered as two key parameters determining its potential for practical applications. In this work, the rigid single-crystalline anatase TiO2 nanosheets (NSs) with a thickness of ∼4 nm has been firstly prepared, based on which a stable nanostructured network consisting of ultrathin anatase TiO2 NSs homogeneously anchored on graphene through chemical bonding (TiO2 NSs-G) has fabricated by hydrothermal process and subsequent calcination treatment. The morphology, crystallization, chemical compositions and the intimate maximum contact between TiO2 NSs and graphene are confirmed by TEM, SEM, XRD, XPS and Raman characterizations. The results of electrochemical performance tests indicated that the TiO2 NSs-G hybrid network could be consider as a promising anode material for SIBs, in assessment of its remarkably high current rate and long cycle-life aside from the improved specific capacity, rate capability and cycle stability.

Hybrid Communication Architectures for Distributed Smart Grid Applications

DOE PAGES

Zhang, Jianhua; Hasandka, Adarsh; Wei, Jin; ...

2018-04-09

Wired and wireless communications both play an important role in the blend of communications technologies necessary to enable future smart grid communications. Hybrid networks exploit independent mediums to extend network coverage and improve performance. However, whereas individual technologies have been applied in simulation networks, as far as we know there is only limited attention that has been paid to the development of a suite of hybrid communication simulation models for the communications system design. Hybrid simulation models are needed to capture the mixed communication technologies and IP address mechanisms in one simulation. To close this gap, we have developed amore » suite of hybrid communication system simulation models to validate the critical system design criteria for a distributed solar Photovoltaic (PV) communications system, including a single trip latency of 300 ms, throughput of 9.6 Kbps, and packet loss rate of 1%. In conclusion, the results show that three low-power wireless personal area network (LoWPAN)-based hybrid architectures can satisfy three performance metrics that are critical for distributed energy resource communications.« less

Hybrid Communication Architectures for Distributed Smart Grid Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jianhua; Hasandka, Adarsh; Wei, Jin

Wired and wireless communications both play an important role in the blend of communications technologies necessary to enable future smart grid communications. Hybrid networks exploit independent mediums to extend network coverage and improve performance. However, whereas individual technologies have been applied in simulation networks, as far as we know there is only limited attention that has been paid to the development of a suite of hybrid communication simulation models for the communications system design. Hybrid simulation models are needed to capture the mixed communication technologies and IP address mechanisms in one simulation. To close this gap, we have developed amore » suite of hybrid communication system simulation models to validate the critical system design criteria for a distributed solar Photovoltaic (PV) communications system, including a single trip latency of 300 ms, throughput of 9.6 Kbps, and packet loss rate of 1%. In conclusion, the results show that three low-power wireless personal area network (LoWPAN)-based hybrid architectures can satisfy three performance metrics that are critical for distributed energy resource communications.« less

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

NASA Technical Reports Server (NTRS)

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.

Effects of Modulation Techniques (Manchester Code, NRZ or RZ) on the Operation of Hybrid WDM/TDM Passive Optical Networks

PubMed Central

Nyachionjeka, Kumbirayi

2014-01-01

In this paper, the performance and feasibility of a hybrid wavelength division multiplexing/time division multiplexing passive optical network (WDM/TDM PON) system with 128 optical network units (ONUs) is analysed. In this system, triple play services (video, voice and data) are successfully communicated through a distance of up to 28 km. Moreover, we analysed and compared the performance of various modulation formats for different distances in the proposed hybrid WDM/TDM PON. NRZ rectangular emerged as the most appropriate modulation format for triple play transmission in the proposed hybrid PON. PMID:27382633

Feedback Error Learning Controller for Functional Electrical Stimulation Assistance in a Hybrid Robotic System for Reaching Rehabilitation

PubMed Central

Resquín, Francisco; Gonzalez-Vargas, Jose; Ibáñez, Jaime; Brunetti, Fernando; Pons, José Luis

2016-01-01

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model. PMID:27990245

Hybrid stochastic simplifications for multiscale gene networks.

PubMed

Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

2009-09-07

Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

NASA Astrophysics Data System (ADS)

Mora, A.; Han, F.; Lubineau, G.

2018-04-01

One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in the remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite’s conductivity based on these parameters.

Internal and External Pressures: How Does an Organic Cotton Production Network Learn to Keep Its Hybrid Nature?

ERIC Educational Resources Information Center

Turcato, Carolina; Barin-Cruz, Luciano; Pedrozo, Eugenio Avila

2012-01-01

Purpose: This study aims to investigate how an organic cotton production network learns to maintain its hybrid network and its sustainability in the face of internal and external pressures. Design/methodology/approach: A qualitative case study was conducted in Justa Trama, a Brazilian-based organic cotton production network formed by six members…

Optimizing performance of hybrid FSO/RF networks in realistic dynamic scenarios

NASA Astrophysics Data System (ADS)

Llorca, Jaime; Desai, Aniket; Baskaran, Eswaran; Milner, Stuart; Davis, Christopher

2005-08-01

Hybrid Free Space Optical (FSO) and Radio Frequency (RF) networks promise highly available wireless broadband connectivity and quality of service (QoS), particularly suitable for emerging network applications involving extremely high data rate transmissions such as high quality video-on-demand and real-time surveillance. FSO links are prone to atmospheric obscuration (fog, clouds, snow, etc) and are difficult to align over long distances due the use of narrow laser beams and the effect of atmospheric turbulence. These problems can be mitigated by using adjunct directional RF links, which provide backup connectivity. In this paper, methodologies for modeling and simulation of hybrid FSO/RF networks are described. Individual link propagation models are derived using scattering theory, as well as experimental measurements. MATLAB is used to generate realistic atmospheric obscuration scenarios, including moving cloud layers at different altitudes. These scenarios are then imported into a network simulator (OPNET) to emulate mobile hybrid FSO/RF networks. This framework allows accurate analysis of the effects of node mobility, atmospheric obscuration and traffic demands on network performance, and precise evaluation of topology reconfiguration algorithms as they react to dynamic changes in the network. Results show how topology reconfiguration algorithms, together with enhancements to TCP/IP protocols which reduce the network response time, enable the network to rapidly detect and act upon link state changes in highly dynamic environments, ensuring optimized network performance and availability.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

PubMed

Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

2016-12-01

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

High capacity fiber optic sensor networks using hybrid multiplexing techniques and their applications

NASA Astrophysics Data System (ADS)

Sun, Qizhen; Li, Xiaolei; Zhang, Manliang; Liu, Qi; Liu, Hai; Liu, Deming

2013-12-01

Fiber optic sensor network is the development trend of fiber senor technologies and industries. In this paper, I will discuss recent research progress on high capacity fiber sensor networks with hybrid multiplexing techniques and their applications in the fields of security monitoring, environment monitoring, Smart eHome, etc. Firstly, I will present the architecture of hybrid multiplexing sensor passive optical network (HSPON), and the key technologies for integrated access and intelligent management of massive fiber sensor units. Two typical hybrid WDM/TDM fiber sensor networks for perimeter intrusion monitor and cultural relics security are introduced. Secondly, we propose the concept of "Microstructure－Optical X Domin Refecltor (M-OXDR)" for fiber sensor network expansion. By fabricating smart micro-structures with the ability of multidimensional encoded and low insertion loss along the fiber, the fiber sensor network of simple structure and huge capacity more than one thousand could be achieved. Assisted by the WDM/TDM and WDM/FDM decoding methods respectively, we built the verification systems for long-haul and real-time temperature sensing. Finally, I will show the high capacity and flexible fiber sensor network with IPv6 protocol based hybrid fiber/wireless access. By developing the fiber optic sensor with embedded IPv6 protocol conversion module and IPv6 router, huge amounts of fiber optic sensor nodes can be uniquely addressed. Meanwhile, various sensing information could be integrated and accessed to the Next Generation Internet.

Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network

DOE PAGES

Zhu, Zhenshan; Liu, Dichen; Liao, Qingfen; ...

2018-01-26

With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); softmore » open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment.« less

Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Zhenshan; Liu, Dichen; Liao, Qingfen

With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); softmore » open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment.« less

Augmented Quantum Yield of a 2D Monolayer Photodetector by Surface Plasmon Coupling.

PubMed

Bang, Seungho; Duong, Ngoc Thanh; Lee, Jubok; Cho, Yoo Hyun; Oh, Hye Min; Kim, Hyun; Yun, Seok Joon; Park, Chulho; Kwon, Min-Ki; Kim, Ja-Yeon; Kim, Jeongyong; Jeong, Mun Seok

2018-04-11

Monolayer (1L) transition metal dichalcogenides (TMDCs) are promising materials for nanoscale optoelectronic devices because of their direct band gap and wide absorption range (ultraviolet to infrared). However, 1L-TMDCs cannot be easily utilized for practical optoelectronic device applications (e.g., photodetectors, solar cells, and light-emitting diodes) because of their extremely low optical quantum yields (QYs). In this investigation, a high-gain 1L-MoS 2 photodetector was successfully realized, based on the surface plasmon (SP) of the Ag nanowire (NW) network. Through systematic optical characterization of the hybrid structure consisting of a 1L-MoS 2 and the Ag NW network, it was determined that a strong SP and strain relaxation effect influenced a greatly enhanced optical QY. The photoluminescence (PL) emission was drastically increased by a factor of 560, and the main peak was shifted to the neutral exciton of 1L-MoS 2 . Consequently, the overall photocurrent of the hybrid 1L-MoS 2 photodetector was observed to be 250 times better than that of the pristine 1L-MoS 2 photodetector. In addition, the photoresponsivity and photodetectivity of the hybrid photodetector were effectively improved by a factor of ∼1000. This study provides a new approach for realizing highly efficient optoelectronic devices based on TMDCs.

Hybrid wireless sensor network for rescue site monitoring after earthquake

NASA Astrophysics Data System (ADS)

Wang, Rui; Wang, Shuo; Tang, Chong; Zhao, Xiaoguang; Hu, Weijian; Tan, Min; Gao, Bowei

2016-07-01

This paper addresses the design of a low-cost, low-complexity, and rapidly deployable wireless sensor network (WSN) for rescue site monitoring after earthquakes. The system structure of the hybrid WSN is described. Specifically, the proposed hybrid WSN consists of two kinds of wireless nodes, i.e., the monitor node and the sensor node. Then the mechanism and the system configuration of the wireless nodes are detailed. A transmission control protocol (TCP)-based request-response scheme is proposed to allow several monitor nodes to communicate with the monitoring center. UDP-based image transmission algorithms with fast recovery have been developed to meet the requirements of in-time delivery of on-site monitor images. In addition, the monitor node contains a ZigBee module that used to communicate with the sensor nodes, which are designed with small dimensions to monitor the environment by sensing different physical properties in narrow spaces. By building a WSN using these wireless nodes, the monitoring center can display real-time monitor images of the monitoring area and visualize all collected sensor data on geographic information systems. In the end, field experiments were performed at the Training Base of Emergency Seismic Rescue Troops of China and the experimental results demonstrate the feasibility and effectiveness of the monitor system.

A hybrid ARIMA and neural network model applied to forecast catch volumes of Selar crumenophthalmus

NASA Astrophysics Data System (ADS)

Aquino, Ronald L.; Alcantara, Nialle Loui Mar T.; Addawe, Rizavel C.

2017-11-01

The Selar crumenophthalmus with the English name big-eyed scad fish, locally known as matang-baka, is one of the fishes commonly caught along the waters of La Union, Philippines. The study deals with the forecasting of catch volumes of big-eyed scad fish for commercial consumption. The data used are quarterly caught volumes of big-eyed scad fish from 2002 to first quarter of 2017. This actual data is available from the open stat database published by the Philippine Statistics Authority (PSA)whose task is to collect, compiles, analyzes and publish information concerning different aspects of the Philippine setting. Autoregressive Integrated Moving Average (ARIMA) models, Artificial Neural Network (ANN) model and the Hybrid model consisting of ARIMA and ANN were developed to forecast catch volumes of big-eyed scad fish. Statistical errors such as Mean Absolute Errors (MAE) and Root Mean Square Errors (RMSE) were computed and compared to choose the most suitable model for forecasting the catch volume for the next few quarters. A comparison of the results of each model and corresponding statistical errors reveals that the hybrid model, ARIMA-ANN (2,1,2)(6:3:1), is the most suitable model to forecast the catch volumes of the big-eyed scad fish for the next few quarters.

Hybrid stochastic simplifications for multiscale gene networks

PubMed Central

Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

2009-01-01

Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach. PMID:19735554

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

PubMed Central

Shao, Yuehjen E.

2014-01-01

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models. PMID:24723804

A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks.

PubMed

Zhang, Qingguo; Fok, Mable P

2017-01-09

Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate's target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate's target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage-distance rate and the number of moved mobile sensors, when compare with other approaches.

A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks

PubMed Central

Zhang, Qingguo; Fok, Mable P.

2017-01-01

Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate’s target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate’s target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage–distance rate and the number of moved mobile sensors, when compare with other approaches. PMID:28075365

Hybrid methods for simulating hydrodynamics and heat transfer in multiscale (1D-3D) models

NASA Astrophysics Data System (ADS)

Filimonov, S. A.; Mikhienkova, E. I.; Dekterev, A. A.; Boykov, D. V.

2017-09-01

The work is devoted to application of different-scale models in the simulation of hydrodynamics and heat transfer of large and/or complex systems, which can be considered as a combination of extended and “compact” elements. The model consisting of simultaneously existing three-dimensional and network (one-dimensional) elements is called multiscale. The paper examines the relevance of building such models and considers three main options for their implementation: the spatial and the network parts of the model are calculated separately; spatial and network parts are calculated simultaneously (hydraulically unified model); network elements “penetrate” the spatial part and are connected through the integral characteristics at the tube/channel walls (hydraulically disconnected model). Each proposed method is analyzed in terms of advantages and disadvantages. The paper presents a number of practical examples demonstrating the application of multiscale models.

Characterization and Curing Kinetics of Epoxy/Silica Nano-Hybrids

PubMed Central

Yang, Cheng-Fu; Wang, Li-Fen; Wu, Song-Mao; Su, Chean-Cheng

2015-01-01

The sol-gel technique was used to prepare epoxy/silica nano-hybrids. The thermal characteristics, curing kinetics and structure of epoxy/silica nano-hybrids were studied using differential scanning calorimetry (DSC), 29Si nuclear magnetic resonance (NMR) and transmission electron microscopy (TEM). To improve the compatibility between the organic and inorganic phases, a coupling agent was used to modify the diglycidyl ether of bisphenol A (DGEBA) epoxy. The sol-gel technique enables the silica to be successfully incorporated into the network of the hybrids, increasing the thermal stability and improving the mechanical properties of the prepared epoxy/silica nano-hybrids. An autocatalytic mechanism of the epoxy/SiO2 nanocomposites was observed. The low reaction rate of epoxy in the nanocomposites is caused by the steric hindrance in the network of hybrids that arises from the consuming of epoxide group in the network of hybrids by the silica. In the nanocomposites, the nano-scale silica particles had an average size of approximately 35 nm, and the particles were well dispersed in the epoxy matrix, according to the TEM images. PMID:28793616

A hybrid linear/nonlinear training algorithm for feedforward neural networks.

PubMed

McLoone, S; Brown, M D; Irwin, G; Lightbody, A

1998-01-01

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large.

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

NASA Astrophysics Data System (ADS)

Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine

2017-06-01

The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to "learn" the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model.

Hybrid Radio Frequency/Free-Space Optics (RF/FSO) Wireless Sensor Network: Security Concerns and Protective Measures

NASA Astrophysics Data System (ADS)

Banerjee, Koushik; Sharma, Hemant; Sengupta, Anasuya

Wireless sensor networks (WSNs) are ad hoc wireless networks that are written off as spread out structure and ad hoc deployment. Sensor networks have all the rudimentary features of ad hoc networks but to altered points—for instance, considerably lesser movement and far more energy necessities. Commonly used technology for communication is radio frequency (RF) communications. Free-space optics (FSO) is relatively new technology which has the prospective to deliver remarkable increases in network lifetime of WSN. Hybrid RF/FSO communications has been suggested to decrease power consumption by a single sensor node. It is observed that security plays a very important role for either RF WSN or hybrid RF/FSO WSN as those are vulnerable to numerous threats. In this paper, various possible attacks in RF/FSO WSN are discussed and aimed to propose some way out from those attacks.

Multi-Layer Coating of Ultrathin Polymer Films on Nanoparticles of Alumina by a Plasma Treatment

DTIC Science & Technology

2001-01-01

Proc. Vol. 635 © 2001 Materials Research Society Multi-Layer Coating of Ultrathin Polymer Films on Nanoparticles of Alumina by a Plasma Treatment Donglu...interconnected organic and inorganic networks results in coatings with a very low permeability for gases and liquids. Hybrid materials are very suitable for... materials consist of a clear alcoholic solution that can easily be processed by classical application techniques such as dipping, spraying, or spin coating

Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models

PubMed Central

2016-01-01

The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis. PMID:27248692

Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models.

PubMed

Chan Phooi M'ng, Jacinta; Mehralizadeh, Mohammadali

2016-01-01

The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today's increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong's Hang Seng futures, Japan's NIKKEI 225 futures, Singapore's MSCI futures, South Korea's KOSPI 200 futures, and Taiwan's TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis.

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Inferring Phylogenetic Networks Using PhyloNet.

PubMed

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

Application of Approximate Pattern Matching in Two Dimensional Spaces to Grid Layout for Biochemical Network Maps

PubMed Central

Inoue, Kentaro; Shimozono, Shinichi; Yoshida, Hideaki; Kurata, Hiroyuki

2012-01-01

Background For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. Results We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor) algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. Conclusions Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html. PMID:22679486

Application of approximate pattern matching in two dimensional spaces to grid layout for biochemical network maps.

PubMed

Inoue, Kentaro; Shimozono, Shinichi; Yoshida, Hideaki; Kurata, Hiroyuki

2012-01-01

For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor) algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html.

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

NASA Astrophysics Data System (ADS)

Wan, Li; Zhou, Qinghua

2007-10-01

The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem.

Structural health monitoring using a hybrid network of self-powered accelerometer and strain sensors

NASA Astrophysics Data System (ADS)

Alavi, Amir H.; Hasni, Hassene; Jiao, Pengcheng; Lajnef, Nizar

2017-04-01

This paper presents a structural damage identification approach based on the analysis of the data from a hybrid network of self-powered accelerometer and strain sensors. Numerical and experimental studies are conducted on a plate with bolted connections to verify the method. Piezoelectric ceramic Lead Zirconate Titanate (PZT)-5A ceramic discs and PZT-5H bimorph accelerometers are placed on the surface of the plate to measure the voltage changes due to damage progression. Damage is defined by loosening or removing one bolt at a time from the plate. The results show that the PZT accelerometers provide a fairly more consistent behavior than the PZT strain sensors. While some of the PZT strain sensors are not sensitive to the changes of the boundary condition, the bimorph accelerometers capture the mode changes from undamaged to missing bolt conditions. The results corresponding to the strain sensors are better indicator to the location of damage compared to the accelerometers. The characteristics of the overall structure can be monitored with even one accelerometer. On the other hand, several PZT strain sensors might be needed to localize the damage.

Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

NASA Astrophysics Data System (ADS)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

2009-04-01

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Kim, Chan Moon; Parnichkun, Manukid

2017-11-01

Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments

PubMed Central

Ambroise, Matthieu; Levi, Timothée; Joucla, Sébastien; Yvert, Blaise; Saïghi, Sylvain

2013-01-01

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin–Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development. PMID:24319408

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

USGS Publications Warehouse

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

2000-01-01

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

[CH(3)(CH(2))(11)NH(3)]SnI(3): a hybrid semiconductor with MoO(3)-type tin(II) iodide layers.

PubMed

Xu, Zhengtao; Mitzi, David B

2003-10-20

The organic-inorganic hybrid [CH(3)(CH(2))(11)NH(3)]SnI(3) presents a lamellar structure with a Sn-I framework isotypic to that of MoO(3). The SnI(3)(-) layer consists of edge and corner-sharing SnI(6) octahedra in which one of the six Sn-I bonds is distinctly elongated (e.g., 3.62 A), indicating lone-pair stereoactivity for the Sn(II) atom. The overall electronic character remains comparable with that of the well-studied SnI(4)(2)(-)-based perovskite semiconductors, such as [CH(3)(CH(2))(11)NH(3)](2)SnI(4), with a red-shifted and broadened exciton peak associated with the band gap, apparently due to the increased dimensionality of the Sn-I framework. The title compound offers, aside from the hybrid perovskites, a new type of solution-processable Sn-I network for potential applications in semiconductive devices.

Highly Electrically Conducting Glass-Graphene Nanoplatelets Hybrid Coatings.

PubMed

Garcia, E; Nistal, A; Khalifa, A; Essa, Y; Martín de la Escalera, F; Osendi, M I; Miranzo, P

2015-08-19

Hybrid coatings consisting of a heat resistant Y2O3-Al2O3-SiO2 (YAS) glass containing 2.3 wt % of graphene nanoplatelets (GNPs) were developed by flame spraying homogeneous ceramic powders-GNP granules. Around 40% of the GNPs survived the high spraying temperatures and were distributed along the splat-interfaces, forming a percolated network. These YAS-GNP coatings are potentially interesting in thermal protection systems and electromagnetic interference shields for aerospace applications; therefore silicon carbide (SiC) materials at the forefront of those applications were employed as substrates. Whereas the YAS coatings are nonconductive, the YAS-GNP coatings showed in-plane electrical conductivity (∼10(2) S·m(-1)) for which a low percolation limit (below 3.6 vol %) is inferred. Indentation tests revealed the formation of a highly damaged indentation zone showing multiple shear displacements between adjacent splats probably favored by the graphene sheets location. The indentation radial cracks typically found in brittle glass coatings are not detected in the hybrid coatings that are also more compliant.

[Novel hybrid inhibitors of the phage T7 RNA polymerase: synthesis, docking and screening in vitro].

PubMed

Kostina, V H; Pal'chykovs'ka, L H; Platonov, M O; Vasyl'chenko, O V; Lysenko, N A; Alekseeva, I V

2012-01-01

A number of new hybrid heteroaromatic compounds, consisting of tricyclic fragments (acridone, thioxanthone and phenazine) and bicyclic fragments (benzimidazole, benzothiazole and benzoxazole) were synthesized using the method, developed by the authors. As a result of screening against the transcription model system of the phage T7 DNA-dependent RNA polymerase three effective inhibitors of the RNA syntheses with the IC50 value of 8.9, 5.7 and 19.8 microM were detected. To cast light on the mode of interaction between the synthesized compounds and the target, the molecular docking was applied to the model pocket of the phage T7 RNA polymerase transcription complex. It was established that these ligands form networks of H-bonds with residues of the pocket conservative amino acids and pi-interaction with the Mg2+ ion. A planar geometry of the hybrid molecules, realized due to the intramolecular H-bonds, proved to be an important structural feature, which correlates with an efficacious inhibitory activity.

Facile solid-state synthesis of Ni@C nanosheet-assembled hierarchical network for high-performance lithium storage

NASA Astrophysics Data System (ADS)

Gu, Jinghe; Li, Qiyun; Zeng, Pan; Meng, Yulin; Zhang, Xiukui; Wu, Ping; Zhou, Yiming

2017-08-01

Micro/nano-architectured transition-metal@C hybrids possess unique structural and compositional features toward lithium storage, and are thus expected to manifest ideal anodic performances in advanced lithium-ion batteries (LIBs). Herein, we propose a facile and scalable solid-state coordination and subsequent pyrolysis route for the formation of a novel type of micro/nano-architectured transition-metal@C hybrid (i.e., Ni@C nanosheet-assembled hierarchical network, Ni@C network). Moreover, this coordination-pyrolysis route has also been applied for the construction of bare carbon network using zinc salts instead of nickel salts as precursors. When applied as potential anodic materials in LIBs, the Ni@C network exhibits Ni-content-dependent electrochemical performances, and the partially-etched Ni@C network manifests markedly enhanced Li-storage performances in terms of specific capacities, cycle life, and rate capability than the pristine Ni@C network and carbon network. The proposed solid-state coordination and pyrolysis strategy would open up new opportunities for constructing micro/nano-architectured transition-metal@C hybrids as advanced anode materials for LIBs.

Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

NASA Astrophysics Data System (ADS)

Prakash, S.; Sinha, S. K.

2015-09-01

In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

Combined effect of external damper and cross-tie on the modal response of hybrid two-cable networks

NASA Astrophysics Data System (ADS)

Ahmad, Javaid; Cheng, Shaohong; Ghrib, Faouzi

2018-03-01

Combining external dampers and cross-ties into a hybrid system to control bridge stay cable vibrations can address deficiencies associated with these two commonly used vibration control solutions while retaining their respective merits. Despite successful implementation of this strategy on a few cable-stayed bridges, behavior of such a structural system is still not fully understood. In the current study, an analytical model of a hybrid system consisting of two parallel taut cables interconnected by a transverse linear flexible cross-tie, with one cable also equipped with a transverse linear viscous damper close to one end support, is developed. The proposed model is validated by an experimental work in the literature and an independent numerical simulation. A parametric study is conducted to comprehend the impact of main design parameters on the performance of a hybrid system in terms of the in-plane frequency, the damping and the degree of mode localization of the system's fundamental mode. In addition, the concept of isoquant curve is applied not only to appreciate the effect of simultaneous variation in main design parameters on the modal behavior of a hybrid system, but also to identify the optimal ranges of these parameters to achieve the required cable vibration control effect.

Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier

PubMed Central

Akram, M. Usman; Khan, Shoab A.; Javed, Muhammad Younus

2014-01-01

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network. PMID:25136674

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

NASA Astrophysics Data System (ADS)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

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

PubMed

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

2015-01-01

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

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

PubMed

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

2015-02-01

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

Hybrid-Lambda: simulation of multiple merger and Kingman gene genealogies in species networks and species trees.

PubMed

Zhu, Sha; Degnan, James H; Goldstien, Sharyn J; Eldon, Bjarki

2015-09-15

There has been increasing interest in coalescent models which admit multiple mergers of ancestral lineages; and to model hybridization and coalescence simultaneously. Hybrid-Lambda is a software package that simulates gene genealogies under multiple merger and Kingman's coalescent processes within species networks or species trees. Hybrid-Lambda allows different coalescent processes to be specified for different populations, and allows for time to be converted between generations and coalescent units, by specifying a population size for each population. In addition, Hybrid-Lambda can generate simulated datasets, assuming the infinitely many sites mutation model, and compute the F ST statistic. As an illustration, we apply Hybrid-Lambda to infer the time of subdivision of certain marine invertebrates under different coalescent processes. Hybrid-Lambda makes it possible to investigate biogeographic concordance among high fecundity species exhibiting skewed offspring distribution.

Hybrid networking sensing system for structural health monitoring of a concrete cable-stayed bridge

NASA Astrophysics Data System (ADS)

Torbol, Marco; Kim, Sehwan; Chien, Ting-Chou; Shinozuka, Masanobu

2013-04-01

The purpose of this study is the remote structural health monitoring to identify the torsional natural frequencies and mode shapes of a concrete cable-stayed bridge using a hybrid networking sensing system. The system consists of one data aggregation unit, which is daisy-chained to one or more sensing nodes. A wireless interface is used between the data aggregation units, whereas a wired interface is used between a data aggregation unit and the sensing nodes. Each sensing node is equipped with high-precision MEMS accelerometers with adjustable sampling frequency from 0.2 Hz to 1.2 kHz. The entire system was installed inside the reinforced concrete box-girder deck of Hwamyung Bridge, which is a cable stayed bridge in Busan, South Korea, to protect the system from the harsh environmental conditions. This deployment makes wireless communication a challenge due to the signal losses and the high levels of attenuation. To address these issues, the concept of hybrid networking system is introduced with the efficient local power distribution technique. The theoretical communication range of Wi-Fi is 100m. However, inside the concrete girder, the peer to peer wireless communication cannot exceed about 20m. The distance is further reduced by the line of sight between the antennas. However, the wired daisy-chained connection between sensing nodes is useful because the data aggregation unit can be placed in the optimal location for transmission. To overcome the limitation of the wireless communication range, we adopt a high-gain antenna that extends the wireless communication distance to 50m. Additional help is given by the multi-hopping data communication protocol. The 4G modem, which allows remote access to the system, is the only component exposed to the external environment.

Tree-average distances on certain phylogenetic networks have their weights uniquely determined.

PubMed

Willson, Stephen J

2012-01-01

A phylogenetic network N has vertices corresponding to species and arcs corresponding to direct genetic inheritance from the species at the tail to the species at the head. Measurements of DNA are often made on species in the leaf set, and one seeks to infer properties of the network, possibly including the graph itself. In the case of phylogenetic trees, distances between extant species are frequently used to infer the phylogenetic trees by methods such as neighbor-joining. This paper proposes a tree-average distance for networks more general than trees. The notion requires a weight on each arc measuring the genetic change along the arc. For each displayed tree the distance between two leaves is the sum of the weights along the path joining them. At a hybrid vertex, each character is inherited from one of its parents. We will assume that for each hybrid there is a probability that the inheritance of a character is from a specified parent. Assume that the inheritance events at different hybrids are independent. Then for each displayed tree there will be a probability that the inheritance of a given character follows the tree; this probability may be interpreted as the probability of the tree. The tree-average distance between the leaves is defined to be the expected value of their distance in the displayed trees. For a class of rooted networks that includes rooted trees, it is shown that the weights and the probabilities at each hybrid vertex can be calculated given the network and the tree-average distances between the leaves. Hence these weights and probabilities are uniquely determined. The hypotheses on the networks include that hybrid vertices have indegree exactly 2 and that vertices that are not leaves have a tree-child.

Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

ERIC Educational Resources Information Center

van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

2016-01-01

E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

The resilient hybrid fiber sensor network with self-healing function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Shibo, E-mail: Shibo-Xu@tju.edu.cn; Liu, Tiegen; Ge, Chunfeng

This paper presents a novel resilient fiber sensor network (FSN) with multi-ring architecture, which could interconnect various kinds of fiber sensors responsible for more than one measurands. We explain how the intelligent control system provides sensors with self-healing function meanwhile sensors are working properly, besides each fiber in FSN is under real-time monitoring. We explain the software process and emergency mechanism to respond failures or other circumstances. To improve the efficiency in the use of limited spectrum resources in some situations, we have two different structures to distribute the light sources rationally. Then, we propose a hybrid sensor working inmore » FSN which is a combination of a distributed sensor and a FBG (Fiber Bragg Grating) array fused in a common fiber sensing temperature and vibrations simultaneously with neglectable crosstalk to each other. By making a failure to a working fiber in experiment, the feasibility and effectiveness of the network with a hybrid sensor has been demonstrated, hybrid sensors could not only work as designed but also survive from destructive failures with the help of resilient network and smart and quick self-healing actions. The network has improved the viability of the fiber sensors and diversity of measurands.« less

Hybrid optical CDMA-FSO communications network under spatially correlated gamma-gamma scintillation.

PubMed

Jurado-Navas, Antonio; Raddo, Thiago R; Garrido-Balsells, José María; Borges, Ben-Hur V; Olmos, Juan José Vegas; Monroy, Idelfonso Tafur

2016-07-25

In this paper, we propose a new hybrid network solution based on asynchronous optical code-division multiple-access (OCDMA) and free-space optical (FSO) technologies for last-mile access networks, where fiber deployment is impractical. The architecture of the proposed hybrid OCDMA-FSO network is thoroughly described. The users access the network in a fully asynchronous manner by means of assigned fast frequency hopping (FFH)-based codes. In the FSO receiver, an equal gain-combining technique is employed along with intensity modulation and direct detection. New analytical formalisms for evaluating the average bit error rate (ABER) performance are also proposed. These formalisms, based on the spatially correlated gamma-gamma statistical model, are derived considering three distinct scenarios, namely, uncorrelated, totally correlated, and partially correlated channels. Numerical results show that users can successfully achieve error-free ABER levels for the three scenarios considered as long as forward error correction (FEC) algorithms are employed. Therefore, OCDMA-FSO networks can be a prospective alternative to deliver high-speed communication services to access networks with deficient fiber infrastructure.

Hybrid Mobile Communication Networks for Planetary Exploration

NASA Technical Reports Server (NTRS)

Alena, Richard; Lee, Charles; Walker, Edward; Osenfort, John; Stone, Thom

2007-01-01

A paper discusses the continuing work of the Mobile Exploration System Project, which has been performing studies toward the design of hybrid communication networks for future exploratory missions to remote planets. A typical network could include stationary radio transceivers on a remote planet, mobile radio transceivers carried by humans and robots on the planet, terrestrial units connected via the Internet to an interplanetary communication system, and radio relay transceivers aboard spacecraft in orbit about the planet. Prior studies have included tests on prototypes of these networks deployed in Arctic and desert regions chosen to approximate environmental conditions on Mars. Starting from the findings of the prior studies, the paper discusses methods of analysis, design, and testing of the hybrid communication networks. It identifies key radio-frequency (RF) and network engineering issues. Notable among these issues is the study of wireless LAN throughput loss due to repeater use, RF signal strength, and network latency variations. Another major issue is that of using RF-link analysis to ensure adequate link margin in the face of statistical variations in signal strengths.

Hybrid modeling of microbial exopolysaccharide (EPS) production: The case of Enterobacter A47.

PubMed

Marques, Rodolfo; von Stosch, Moritz; Portela, Rui M C; Torres, Cristiana A V; Antunes, Sílvia; Freitas, Filomena; Reis, Maria A M; Oliveira, Rui

2017-03-20

Enterobacter A47 is a bacterium that produces high amounts of a fucose-rich exopolysaccharide (EPS) from glycerol residue of the biodiesel industry. The fed-batch process is characterized by complex non-linear dynamics with highly viscous pseudo-plastic rheology due to the accumulation of EPS in the culture medium. In this paper, we study hybrid modeling as a methodology to increase the predictive power of models for EPS production optimization. We compare six hybrid structures that explore different levels of knowledge-based and machine-learning model components. Knowledge-based components consist of macroscopic material balances, Monod type kinetics, cardinal temperature and pH (CTP) dependency and power-law viscosity models. Unknown dependencies are set to be identified by a feedforward artificial neural network (ANN). A semiparametric identification schema is applied resorting to a data set of 13 independent fed-batch experiments. A parsimonious hybrid model was identified that describes the dynamics of the 13 experiments with the same parameterization. The final model is specific to Enterobacter A47 but can be easily extended to other microbial EPS processes. Copyright © 2017 Elsevier B.V. All rights reserved.

Multifunctional three-dimensional macroporous nanoelectronic networks for smart materials.

PubMed

Liu, Jia; Xie, Chong; Dai, Xiaochuan; Jin, Lihua; Zhou, Wei; Lieber, Charles M

2013-04-23

Seamless and minimally invasive integration of 3D electronic circuitry within host materials could enable the development of materials systems that are self-monitoring and allow for communication with external environments. Here, we report a general strategy for preparing ordered 3D interconnected and addressable macroporous nanoelectronic networks from ordered 2D nanowire nanoelectronic precursors, which are fabricated by conventional lithography. The 3D networks have porosities larger than 99%, contain approximately hundreds of addressable nanowire devices, and have feature sizes from the 10-μm scale (for electrical and structural interconnections) to the 10-nm scale (for device elements). The macroporous nanoelectronic networks were merged with organic gels and polymers to form hybrid materials in which the basic physical and chemical properties of the host were not substantially altered, and electrical measurements further showed a >90% yield of active devices in the hybrid materials. The positions of the nanowire devices were located within 3D hybrid materials with ∼14-nm resolution through simultaneous nanowire device photocurrent/confocal microscopy imaging measurements. In addition, we explored functional properties of these hybrid materials, including (i) mapping time-dependent pH changes throughout a nanowire network/agarose gel sample during external solution pH changes, and (ii) characterizing the strain field in a hybrid nanoelectronic elastomer structures subject to uniaxial and bending forces. The seamless incorporation of active nanoelectronic networks within 3D materials reveals a powerful approach to smart materials in which the capabilities of multifunctional nanoelectronics allow for active monitoring and control of host systems.

Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices

NASA Astrophysics Data System (ADS)

Tseng, Chih-Hsiung; Cheng, Sheng-Tzong; Wang, Yi-Hsien; Peng, Jin-Tang

2008-05-01

This investigation integrates a novel hybrid asymmetric volatility approach into an Artificial Neural Networks option-pricing model to upgrade the forecasting ability of the price of derivative securities. The use of the new hybrid asymmetric volatility method can simultaneously decrease the stochastic and nonlinearity of the error term sequence, and capture the asymmetric volatility. Therefore, analytical results of the ANNS option-pricing model reveal that Grey-EGARCH volatility provides greater predictability than other volatility approaches.

A New Hybrid Scheme for Preventing Channel Interference and Collision in Mobile Networks

NASA Astrophysics Data System (ADS)

Kim, Kyungjun; Han, Kijun

This paper proposes a new hybrid scheme based on a given set of channels for preventing channel interference and collision in mobile networks. The proposed scheme is designed for improving system performance, focusing on enhancement of performance related to path breakage and channel interference. The objective of this scheme is to improve the performance of inter-node communication. Simulation results from this paper show that the new hybrid scheme can reduce a more control message overhead than a conventional random scheme.

An adaptive supramolecular hydrogel comprising self-sorting double nanofibre networks

NASA Astrophysics Data System (ADS)

Shigemitsu, Hajime; Fujisaku, Takahiro; Tanaka, Wataru; Kubota, Ryou; Minami, Saori; Urayama, Kenji; Hamachi, Itaru

2018-02-01

Novel soft materials should comprise multiple supramolecular nanostructures whose responses (for example, assembly and disassembly) to external stimuli can be controlled independently. Such multicomponent systems are present in living cells and control the formation and break-up of a variety of supramolecular assemblies made of proteins, lipids, DNA and RNA in response to external stimuli; however, artificial counterparts are challenging to make. Here, we present a hybrid hydrogel consisting of a self-sorting double network of nanofibres in which each network responds to an applied external stimulus independent of the other. The hydrogel can be made to change its mechanical properties and rates of release of encapsulated proteins by adding Na2S2O4 or bacterial alkaline phosphatase. Notably, the properties of the gel depend on the order in which the external stimuli are applied. Multicomponent hydrogels comprising orthogonal stimulus-responsive supramolecular assemblies would be suitable for designing novel adaptive materials.

A remote instruction system empowered by tightly shared haptic sensation

NASA Astrophysics Data System (ADS)

Nishino, Hiroaki; Yamaguchi, Akira; Kagawa, Tsuneo; Utsumiya, Kouichi

2007-09-01

We present a system to realize an on-line instruction environment among physically separated participants based on a multi-modal communication strategy. In addition to visual and acoustic information, commonly used communication modalities in network environments, our system provides a haptic channel to intuitively conveying partners' sense of touch. The human touch sensation, however, is very sensitive for delays and jitters in the networked virtual reality (NVR) systems. Therefore, a method to compensate for such negative factors needs to be provided. We show an NVR architecture to implement a basic framework that can be shared by various applications and effectively deals with the problems. We take a hybrid approach to implement both data consistency by client-server and scalability by peer-to-peer models. As an application system built on the proposed architecture, a remote instruction system targeted at teaching handwritten characters and line patterns on a Korea-Japan high-speed research network also is mentioned.

Experimental study of thin film sensor networks for wind turbine blade damage detection

NASA Astrophysics Data System (ADS)

Downey, A.; Laflamme, S.; Ubertini, F.; Sauder, H.; Sarkar, P.

2017-02-01

Damage detection of wind turbine blades is difficult due to their complex geometry and large size, for which large deployment of sensing systems is typically not economical. A solution is to develop and deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel skin-type strain gauge for measuring strain over very large surfaces. The skin, a type of large-area electronics, is constituted from a network of soft elastomeric capacitors. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a dense network of soft elastomeric capacitors to detect, localize, and quantify damage on wind turbine blades. We also leverage mature off-the-shelf technologies, in particular resistive strain gauges, to augment such dense sensor network with high accuracy data at key locations, therefore constituting a hybrid dense sensor network. The proposed hybrid dense sensor network is installed inside a wind turbine blade model, and tested in a wind tunnel to simulate an operational environment. Results demonstrate the ability of the hybrid dense sensor network to detect, localize, and quantify damage.

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

PubMed

Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C

2017-04-01

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

SINET3: advanced optical and IP hybrid network

NASA Astrophysics Data System (ADS)

Urushidani, Shigeo

2007-11-01

This paper introduces the new Japanese academic backbone network called SINET3, which has been in full-scale operation since June 2007. SINET3 provides a wide variety of network services, such as multi-layer transfer, enriched VPN, enhanced QoS, and layer-1 bandwidth on demand (BoD) services to create an innovative and prolific science infrastructure for more than 700 universities and research institutions. The network applies an advanced hybrid network architecture composed of 75 layer-1 switches and 12 high-performance IP routers to accommodate such diversified services in a single network platform, and provides sufficient bandwidth using Japan's first STM256 (40 Gbps) lines. The network adopts lots of the latest networking technologies, such as next-generation SDH (VCAT/GFP/LCAS), GMPLS, advanced MPLS, and logical-router technologies, for high network convergence, flexible resource assignment, and high service availability. This paper covers the network services, network design, and networking technologies of SINET3.

Cyber-physical networking for wireless mesh infrastructures

NASA Astrophysics Data System (ADS)

Mannweiler, C.; Lottermann, C.; Klein, A.; Schneider, J.; Schotten, H. D.

2012-09-01

This paper presents a novel approach for cyber-physical network control. "Cyber-physical" refers to the inclusion of different parameters and information sources, ranging from physical sensors (e.g. energy, temperature, light) to conventional network information (bandwidth, delay, jitter, etc.) to logical data providers (inference systems, user profiles, spectrum usage databases). For a consistent processing, collected data is represented in a uniform way, analyzed, and provided to dedicated network management functions and network services, both internally and, through an according API, to third party services. Specifically, in this work, we outline the design of sophisticated energy management functionalities for a hybrid wireless mesh network (WLAN for both backhaul traffic and access, GSM for access only), disposing of autonomous energy supply, in this case solar power. Energy consumption is optimized under the presumption of fluctuating power availability and considerable storage constraints, thus influencing, among others, handover and routing decisions. Moreover, advanced situation-aware auto-configuration and self-adaptation mechanisms are introduced for an autonomous operation of the network. The overall objective is to deploy a robust wireless access and backbone infrastructure with minimal operational cost and effective, cyber-physical control mechanisms, especially dedicated for rural or developing regions.

Silicon Integrated Optics: Fabrication and Characterization

NASA Astrophysics Data System (ADS)

Shearn, Michael Joseph, II

For decades, the microelectronics industry has sought integration and miniaturization as canonized in Moore's Law, and has continued doubling transistor density about every two years. However, further miniaturization of circuit elements is creating a bandwidth problem as chip interconnect wires shrink as well. A potential solution is the creation of an on-chip optical network with low delays that would be impossible to achieve using metal buses. However, this technology requires integrating optics with silicon microelectronics. The lack of efficient silicon optical sources has stymied efforts of an all-Si optical platform. Instead, the integration of efficient emitter materials, such as III-V semiconductors, with Si photonic structures is a low-cost, CMOS-compatible alternative platform. This thesis focuses on making and measuring on-chip photonic structures suitable for on-chip optical networking. The first part of the thesis assesses processing techniques of silicon and other semiconductor materials. Plasmas for etching and surface modification are described and used to make bonded, hybrid Si/III-V structures. Additionally, a novel masking method using gallium implantation into silicon for pattern definition is characterized. The second part of the thesis focuses on demonstrations of fabricated optical structures. A dense array of silicon devices is measured, consisting of fully-etched grating couplers, low-loss waveguides and ring resonators. Finally, recent progress in the Si/III-V hybrid system is discussed. Supermode control of devices is described, which uses changing Si waveguide width to control modal overlap with the gain material. Hybrid Si/III-V, Fabry-Perot evanescent lasers are demonstrated, utilizing a CMOS-compatible process suitable for integration on in electronics platforms. Future prospects and ultimate limits of Si devices and the hybrid Si/III-V system are also considered.

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

PubMed

Choi, D J; Park, H

2001-11-01

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

Autonomic and Coevolutionary Sensor Networking

NASA Astrophysics Data System (ADS)

Boonma, Pruet; Suzuki, Junichi

(WSNs) applications are often required to balance the tradeoffs among conflicting operational objectives (e.g., latency and power consumption) and operate at an optimal tradeoff. This chapter proposes and evaluates a architecture, called BiSNET/e, which allows WSN applications to overcome this issue. BiSNET/e is designed to support three major types of WSN applications: , and hybrid applications. Each application is implemented as a decentralized group of, which is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data or detect an event (a significant change in sensor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding network conditions and adaptively invoking behaviors such as pheromone emission, reproduction, migration, swarming and death. Each agent has its own behavior policy, as a set of genes, which defines how to invoke its behaviors. BiSNET/e allows agents to evolve their behavior policies (genes) across generations and autonomously adapt their performance to given objectives. Simulation results demonstrate that, in all three types of applications, agents evolve to find optimal tradeoffs among conflicting objectives and adapt to dynamic network conditions such as traffic fluctuations and node failures/additions. Simulation results also illustrate that, in hybrid applications, data collection agents and event detection agents coevolve to augment their adaptability and performance.

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

PubMed

Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

2015-07-01

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Phylogenetic comparative methods on phylogenetic networks with reticulations.

PubMed

Bastide, Paul; Solís-Lemus, Claudia; Kriebel, Ricardo; Sparks, K William; Ané, Cécile

2018-04-25

The goal of Phylogenetic Comparative Methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species' traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. One natural extension of the BM is to use a weighted average model for the trait of a hybrid, at a reticulation point. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagel's λ test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts, and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios, and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a dataset of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.

Hybrid discrete-time neural networks.

PubMed

Cao, Hongjun; Ibarz, Borja

2010-11-13

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

Cost- and reliability-oriented aggregation point association in long-term evolution and passive optical network hybrid access infrastructure for smart grid neighborhood area network

NASA Astrophysics Data System (ADS)

Cheng, Xiao; Feng, Lei; Zhou, Fanqin; Wei, Lei; Yu, Peng; Li, Wenjing

2018-02-01

With the rapid development of the smart grid, the data aggregation point (AP) in the neighborhood area network (NAN) is becoming increasingly important for forwarding the information between the home area network and wide area network. Due to limited budget, it is unable to use one-single access technology to meet the ongoing requirements on AP coverage. This paper first introduces the wired and wireless hybrid access network with the integration of long-term evolution (LTE) and passive optical network (PON) system for NAN, which allows a good trade-off among cost, flexibility, and reliability. Then, based on the already existing wireless LTE network, an AP association optimization model is proposed to make the PON serve as many APs as possible, considering both the economic efficiency and network reliability. Moreover, since the features of the constraints and variables of this NP-hard problem, a hybrid intelligent optimization algorithm is proposed, which is achieved by the mixture of the genetic, ant colony and dynamic greedy algorithm. By comparing with other published methods, simulation results verify the performance of the proposed method in improving the AP coverage and the performance of the proposed algorithm in terms of convergence.

Displayed Trees Do Not Determine Distinguishability Under the Network Multispecies Coalescent

PubMed Central

Zhu, Sha; Degnan, James H.

2017-01-01

Abstract Recent work in estimating species relationships from gene trees has included inferring networks assuming that past hybridization has occurred between species. Probabilistic models using the multispecies coalescent can be used in this framework for likelihood-based inference of both network topologies and parameters, including branch lengths and hybridization parameters. A difficulty for such methods is that it is not always clear whether, or to what extent, networks are identifiable—that is whether there could be two distinct networks that lead to the same distribution of gene trees. For cases in which incomplete lineage sorting occurs in addition to hybridization, we demonstrate a new representation of the species network likelihood that expresses the probability distribution of the gene tree topologies as a linear combination of gene tree distributions given a set of species trees. This representation makes it clear that in some cases in which two distinct networks give the same distribution of gene trees when sampling one allele per species, the two networks can be distinguished theoretically when multiple individuals are sampled per species. This result means that network identifiability is not only a function of the trees displayed by the networks but also depends on allele sampling within species. We additionally give an example in which two networks that display exactly the same trees can be distinguished from their gene trees even when there is only one lineage sampled per species. PMID:27780899

Devices and circuits for nanoelectronic implementation of artificial neural networks

NASA Astrophysics Data System (ADS)

Turel, Ozgur

Biological neural networks perform complicated information processing tasks at speeds better than conventional computers based on conventional algorithms. This has inspired researchers to look into the way these networks function, and propose artificial networks that mimic their behavior. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed or the complexity of a human brain. Nanoelectronics, with high density and low power dissipation that it provides, may be used in developing more efficient artificial neural networks. This work consists of two major contributions in this direction. First is the proposal of the CMOL concept, hybrid CMOS-molecular hardware [1-8]. CMOL may circumvent most of the problems in posed by molecular devices, such as low yield, vet provide high active device density, ˜1012/cm 2. The second contribution is CrossNets, artificial neural networks that are based on CMOL. We showed that CrossNets, with their fault tolerance, exceptional speed (˜ 4 to 6 orders of magnitude faster than biological neural networks) can perform any task any artificial neural network can perform. Moreover, there is a hope that if their integration scale is increased to that of human cerebral cortex (˜ 1010 neurons and ˜ 1014 synapses), they may be capable of performing more advanced tasks.

Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

2013-09-01

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

Unified synchronization criteria in an array of coupled neural networks with hybrid impulses.

PubMed

Wang, Nan; Li, Xuechen; Lu, Jianquan; Alsaadi, Fuad E

2018-05-01

This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming from hybrid impulses. By employing the Lyapunov method combined with some mathematical analysis, some efficient unified criteria are obtained to guarantee the globally exponential synchronization of impulsive networks. Our method and criteria are proved to be effective for impulsively coupled neural networks simultaneously with synchronizing impulses and desynchronizing impulses, and we do not need to discuss these two kinds of impulses separately. Moreover, by using our average impulsive interval method, we can obtain an interesting and valuable result for the case of average impulsive interval T a =∞. For some sparse impulsive sequences with T a =∞, the impulses can happen for infinite number of times, but they do not have essential influence on the synchronization property of networks. Finally, numerical examples including scale-free networks are exploited to illustrate our theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Neighborhood Auditing Tool: a hybrid interface for auditing the UMLS.

PubMed

Morrey, C Paul; Geller, James; Halper, Michael; Perl, Yehoshua

2009-06-01

The UMLS's integration of more than 100 source vocabularies, not necessarily consistent with one another, causes some inconsistencies. The purpose of auditing the UMLS is to detect such inconsistencies and to suggest how to resolve them while observing the requirement of fully representing the content of each source in the UMLS. A software tool, called the Neighborhood Auditing Tool (NAT), that facilitates UMLS auditing is presented. The NAT supports "neighborhood-based" auditing, where, at any given time, an auditor concentrates on a single-focus concept and one of a variety of neighborhoods of its closely related concepts. Typical diagrammatic displays of concept networks have a number of shortcomings, so the NAT utilizes a hybrid diagram/text interface that features stylized neighborhood views which retain some of the best features of both the diagrammatic layouts and text windows while avoiding the shortcomings. The NAT allows an auditor to display knowledge from both the Metathesaurus (concept) level and the Semantic Network (semantic type) level. Various additional features of the NAT that support the auditing process are described. The usefulness of the NAT is demonstrated through a group of case studies. Its impact is tested with a study involving a select group of auditors.

A network of networks.

PubMed

Iedema, Rick; Verma, Raj; Wutzke, Sonia; Lyons, Nigel; McCaughan, Brian

2017-04-10

Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author's participation in network activities and discussions with the agency's staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency's implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of "centralised decentralisation", co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid - the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a "network of networks" through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture "strategic hybrids", and enhance the impact of network activities on health system reform. Social implications Network governance requires more from network members than participation in a single network, as it involves health service professionals and consumers in a multi-network dynamic. This dynamic requires deliberations and collaborations to be flexible, and it increasingly positions members as "strategic hybrids" - people who have moved on from singular taken-as-given stances and identities, towards hybrid positionings and flexible perspectives. Originality/value This paper is novel in that it identifies a critical feature of health service reform and large system transformation: network governance is empowered through the dynamic co-location of and collaboration among healthcare networks, particularly when complemented with "enabler" teams of people specialising in programme implementation and evaluation.

Chips of Hope: Neuro-Electronic Hybrids for Brain Repair

NASA Astrophysics Data System (ADS)

Ben-Jacob, Eshel

2010-03-01

The field of Neuro-Electronic Hybrids kicked off 30 years ago when researchers in the US first tweaked the technology of recording and stimulation of networks of live neurons grown in a Petri dish and interfaced with a computer via an array of electrodes. Since then, many researchers have searched for ways to imprint in neural networks new ``memories" without erasing old ones. I will describe our new generation of Neuro-Electronic Hybrids and how we succeeded to turn them into the first learning Neurochips - memory and information processing chips made of live neurons. To imprint multiple memories in our new chip we used chemical stimulation at specific locations that were selected by analyzing the networks activity in real time according to our new information encoding principle. Currently we develop new-generation of neuro chips using special carbon nano tubes (CNT). These electrodes enable to engineer the networks topology and efficient electrical interfacing with the neurons. This advance bears the promise to pave the way for building a new experimental platform for testing new drugs and developing new methods for neural networks repair and regeneration. Looking into the future, the development brings us a step closer towards the dream of Brain Repair by implementable Neuro-Electronic hybrid chips.

Tantala-based sol-gel coating for capillary microextraction on-line coupled to high-performance liquid chromatography.

PubMed

Tran, MinhPhuong; Turner, Erica B; Segro, Scott S; Fang, Li; Seyyal, Emre; Malik, Abdul

2017-11-03

A sol-gel organic-inorganic hybrid sorbent, consisting of chemically integrated tantalum (V) ethoxide (TaEO) and polypropylene glycol methacrylate (PPGM), was developed for capillary microextraction (CME). The sol-gel sorbent was synthesized within a fused silica capillary through hydrolytic polycondensation of TaEO and chemical incorporation of PPGM into the evolving sol-gel tantala network. A part of the organic-inorganic hybrid sol-gel network evolving in the vicinity of the capillary walls had favorable conditions to get chemically bonded to the silanol groups on the capillary surface forming a surface-bonded coating. The newly developed sol-gel sorbent was employed to isolate and enrich a variety of analytes from aqueous samples for on-line analysis by high-performance liquid chromatography (HPLC) equipped with a UV detector. CME was performed on aqueous samples containing trace concentrations of analytes representing polycyclic aromatic hydrocarbons, ketones, alcohols, amines, nucleosides, and nucleotides. This sol-gel hybrid coating provided efficient extraction with CME-HPLC detection limits ranging from 4.41pM to 28.19 pM. Due to direct chemical bonding between the sol-gel sorbent coating and the fused silica capillary inner surface, this sol-gel sorbent exhibited enhanced solvent stability. The sol-gel tantala-based sorbent also exhibited excellent pH stability over a wide pH range (pH 0-pH 14). Furthermore, it displayed great performance reproducibility in CME-HPLC providing run-to-run HPLC peak area relative standard deviation (RSD) values between 0.23% and 3.83%. The capillary-to-capillary RSD (n=3), characterizing capillary preparation method reproducibility, ranged from 0.24% to 4.11%. The results show great performance consistency and application potential for the sol-gel tantala-PPGM sorbent in various fields including biomedical, pharmaceutical, and environmental areas. Copyright © 2017 Elsevier B.V. All rights reserved.

Feasibility of silica-hybridized collagen hydrogels as three-dimensional cell matrices for hard tissue engineering.

PubMed

Yu, Hye-Sun; Lee, Eun-Jung; Seo, Seog-Jin; Knowles, Jonathan C; Kim, Hae-Won

2015-09-01

Exploiting hydrogels for the cultivation of stem cells, aiming to provide them with physico-chemical cues suitable for osteogenesis, is a critical demand for bone engineering. Here, we developed hybrid compositions of collagen and silica into hydrogels via a simple sol-gel process. The physico-chemical and mechanical properties, degradation behavior, and bone-bioactivity were characterized in-depth; furthermore, the in vitro mesenchymal stem cell growth and osteogenic differentiation behaviors within the 3D hybrid gel matrices were communicated for the first time. The hydrolyzed and condensed silica phase enabled chemical links with the collagen fibrils to form networked hybrid gels. The hybrid gels showed improved chemical stability and greater resistance to enzymatic degradation. The in vitro apatite-forming ability was enhanced by the hybrid composition. The viscoelastic mechanical properties of the hybrid gels were significantly improved in terms of the deformation resistance to an applied load and the modulus values under a dynamic oscillation. Mesenchymal stem cells adhered well to the hybrid networks and proliferated actively with substantial cytoskeletal extensions within the gel matrices. Of note, the hybrid gels substantially reduced the cell-mediated gel contraction behaviors, possibly due to the stiffer networks and higher resistance to cell-mediated degradation. Furthermore, the osteogenic differentiation of cells, including the expression of bone-associated genes and protein, was significantly upregulated within the hybrid gel matrices. Together with the physico-chemical and mechanical properties, the cellular behaviors observed within 3D gel matrices, being different from the previous approaches reported on 2D substrates, provide new information on the feasibility and usefulness of the silica-collagen system for stem cell culture and tissue engineering of hard tissues. © The Author(s) 2015.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Power Budget Analysis of Colorless Hybrid WDM/TDM-PON Scheme Using Downstream DPSK and Re-modulated Upstream OOK Data Signals

NASA Astrophysics Data System (ADS)

Khan, Yousaf; Afridi, Muhammad Idrees; Khan, Ahmed Mudassir; Rehman, Waheed Ur; Khan, Jahanzeb

2014-09-01

Hybrid wavelength-division multiplexed/time-division multiplexed passive optical access networks (WDM/TDM-PONs) combine the advance features of both WDM and TDM PONs to provide a cost-effective access network solution. We demonstrate and analyze the transmission performances and power budget issues of a colorless hybrid WDM/TDM-PON scheme. A 10-Gb/s downstream differential phase shift keying (DPSK) and remodulated upstream on/off keying (OOK) data signals are transmitted over 25 km standard single mode fiber. Simulation results show error free transmission having adequate power margins in both downstream and upstream transmission, which prove the applicability of the proposed scheme to future passive optical access networks. The power budget confines both the PON splitting ratio and the distance between the Optical Line Terminal (OLT) and Optical Network Unit (ONU).

A novel protection scheme for a hybrid WDM/TDM PON

NASA Astrophysics Data System (ADS)

Chen, Jiajia; Wosinska, Lena; He, Sailing

2007-11-01

This paper proposes a novel protection scheme based on the cyclic property of an array waveguide grating (AWG) and neighboring connection pattern between two adjacent optical network units (ONUs) for the hybrid WDM/TDM passive optical networks (PONs). Our scheme uses 50% fewer wavelengths while offering one order of magnitude better connection availability than the existing scheme.

HRSSA - Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

NASA Astrophysics Data System (ADS)

Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong

2016-07-01

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

Overview of hybrid fiber-coaxial network deployment in the deregulated UK environment

NASA Astrophysics Data System (ADS)

Cox, Alan L.

1995-11-01

Cable operators in the U.K. enjoy unprecedented license to construct networks and operate cable TV and telecommunications services within their franchise areas. In general, operators have built hybrid-fiber-coax (HFC) networks for cable TV in parallel with fiber-copper-pair networks for telephony. The commonly used network architectures are reviewed, together with their present and future capacities. Despite this dual-technology approach, there is considerable interest in the integration of telephony services onto the HFC network and the development of new interactive services for which HFC may be more suitable than copper pairs. Certain technological and commercial developments may have considerable significance for HFC networks and their operators. These include the digitalization of TV distribution and the rising demand for high-rate digital access lines. Possible scenarios are discussed.

Deterministic growth of AgTCNQ and CuTCNQ nanowires on large-area reduced graphene oxide films for flexible optoelectronics.

PubMed

Zhang, Shuai; Lu, Zhufeng; Gu, Li; Cai, Liling; Cao, Xuebo

2013-11-22

We describe a synchronous reduction and assembly procedure to directly produce large-area reduced graphene oxide (rGO) films sandwiched by a high density of metal nanoparticles (silver and copper). Further, by using the sandwiched metal NPs as sources, networks consisting of AgTCNQ and CuTCNQ nanowires were deterministically grown from the rGO films, forming structurally and functionally integrated rGO/metal-TCNQ hybrid films with outstanding flexibility, bending endurance, and electrical stability. Interestingly, due to the p-type nature of the rGO film and the n-type nature of the metal-TCNQ NWs, the hybrid films are essentially thin-film p-n junctions which are useful in ubiquitous electronics and optoelectronics. Measurements of the optoelectronic properties demonstrate that the rGO/metal-TCNQ hybrid films exhibit substantial photoconductivity and highly reproducible photoswitching behaviours. The present approach may open the door to the versatile and deterministic integration of functional nanostructures into flexible conducting substrates and provide an important step towards producing low-cost and high-performance soft electronic and optoelectronic devices.

Linking 1D Transition-Metal Coordination Polymers and Different Inorganic Boron Oxides To Construct a Series of 3D Inorganic-Organic Hybrid Borates.

PubMed

Zhi, Shao-Chen; Wang, Yue-Lin; Sun, Li; Cheng, Jian-Wen; Yang, Guo-Yu

2018-02-05

Three inorganic-organic hybrid borates, M(1,4-dab)[B 5 O 7 (OH) 3 ] [M = Zn (1), Cd (2), 1,4-dab = 1,4-diaminobutane)] and Co(1,3-dap)[B 4 O 7 ] (3, 1,3-dap = 1,3-diaminopropane), which integrated characteristics of 1D coordination polymers and 1D/3D inorganic boron oxides have been obtained under solvothermal conditions. Compounds 1 and 2 are isostructural and crystallize in a centrosymmetric space group P2 1 /c; the 3D achiral structures of 1 and 2 consist of the nonhelical Zn/Cd-1,4-dap coordination polymers and 1D B-O chains. Compound 3 crystallizes in a chiral space group P4 3 2 1 2; the helical Co-1,3-dap coordination polymer chains are entrained within a 3D B-O network and finally form the chiral framework. Compounds 1-3 represent good examples of using coordination polymers to construct mixed-motif inorganic-organic hybrid borates. Compounds 1 and 2 display blue luminescence when excited with UV light.

Model-based design of RNA hybridization networks implemented in living cells

PubMed Central

Rodrigo, Guillermo; Prakash, Satya; Shen, Shensi; Majer, Eszter

2017-01-01

Abstract Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations. PMID:28934501

Carbon phosphide monolayers with superior carrier mobility

NASA Astrophysics Data System (ADS)

Wang, Gaoxue; Pandey, Ravindra; Karna, Shashi P.

2016-04-01

Two dimensional (2D) materials with a finite band gap and high carrier mobility are sought after materials from both fundamental and technological perspectives. In this paper, we present the results based on the particle swarm optimization method and density functional theory which predict three geometrically different phases of the carbon phosphide (CP) monolayer consisting of sp2 hybridized C atoms and sp3 hybridized P atoms in hexagonal networks. Two of the phases, referred to as α-CP and β-CP with puckered or buckled surfaces are semiconducting with highly anisotropic electronic and mechanical properties. More remarkably, they have the lightest electrons and holes among the known 2D semiconductors, yielding superior carrier mobility. The γ-CP has a distorted hexagonal network and exhibits a semi-metallic behavior with Dirac cones. These theoretical findings suggest that the binary CP monolayer is a yet unexplored 2D material holding great promise for applications in high-performance electronics and optoelectronics.Two dimensional (2D) materials with a finite band gap and high carrier mobility are sought after materials from both fundamental and technological perspectives. In this paper, we present the results based on the particle swarm optimization method and density functional theory which predict three geometrically different phases of the carbon phosphide (CP) monolayer consisting of sp2 hybridized C atoms and sp3 hybridized P atoms in hexagonal networks. Two of the phases, referred to as α-CP and β-CP with puckered or buckled surfaces are semiconducting with highly anisotropic electronic and mechanical properties. More remarkably, they have the lightest electrons and holes among the known 2D semiconductors, yielding superior carrier mobility. The γ-CP has a distorted hexagonal network and exhibits a semi-metallic behavior with Dirac cones. These theoretical findings suggest that the binary CP monolayer is a yet unexplored 2D material holding great promise for applications in high-performance electronics and optoelectronics. Electronic supplementary information (ESI) available: Fig. S1 cohesive energy and structure of the CP monolayer with various stoichiometric compositions obtained using CALYPSO, Fig. S2 history of CALYPSO steps and structure of the CP monolayer, Fig. S3 phonon dispersion with DFT-D2 functional, Fig. S4 band structure for β-CP using the DFT-PBE and DFT-D2 functional forms, Fig. S5 strain energy curves, Fig. S6 projected band structure for α-CP, Fig. S7 projected band structure for β-CP, Fig. S8 projected band structure for γ-CP, Fig. S9 band structures obtained with the GGA-PBE and HSE06 functional; Table S1 lattice parameters with the DFT-D2 functional form; Video S1 AIMD simulation of α-CP at 300 K, Video S2 AIMD simulation of β-CP at 300 K, Video S3 AIMD simulation of γ-CP at 300 K. See DOI: 10.1039/c6nr00498a

Solving deterministic non-linear programming problem using Hopfield artificial neural network and genetic programming techniques

NASA Astrophysics Data System (ADS)

Vasant, P.; Ganesan, T.; Elamvazuthi, I.

2012-11-01

A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata

PubMed Central

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-01-01

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. PMID:28353664

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

PubMed

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-03-29

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

Hybrid monitoring scheme for end-to-end performance enhancement of multicast-based real-time media

NASA Astrophysics Data System (ADS)

Park, Ju-Won; Kim, JongWon

2004-10-01

As real-time media applications based on IP multicast networks spread widely, end-to-end QoS (quality of service) provisioning for these applications have become very important. To guarantee the end-to-end QoS of multi-party media applications, it is essential to monitor the time-varying status of both network metrics (i.e., delay, jitter and loss) and system metrics (i.e., CPU and memory utilization). In this paper, targeting the multicast-enabled AG (Access Grid) a next-generation group collaboration tool based on multi-party media services, the applicability of hybrid monitoring scheme that combines active and passive monitoring is investigated. The active monitoring measures network-layer metrics (i.e., network condition) with probe packets while the passive monitoring checks both application-layer metrics (i.e., user traffic condition by analyzing RTCP packets) and system metrics. By comparing these hybrid results, we attempt to pinpoint the causes of performance degradation and explore corresponding reactions to improve the end-to-end performance. The experimental results show that the proposed hybrid monitoring can provide useful information to coordinate the performance improvement of multi-party real-time media applications.

Manifold absolute pressure estimation using neural network with hybrid training algorithm

PubMed Central

Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

2017-01-01

In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value. PMID:29190779

Cooperative Networked Control of Dynamical Peer-to-Peer Vehicle Systems

DTIC Science & Technology

2007-12-28

dynamic deployment and task allocation;verification and hybrid systems; and information management for cooperative control. The activity of the...32 5.3 Decidability Results on Discrete and Hybrid Systems ...... .................. 33 5.4 Switched Systems...solved. Verification and hybrid systems. The program has produced significant advances in the theory of hybrid input-output automata, (HIOA) and the

A Hybrid Method of Moment Equations and Rate Equations to Modeling Gas-Grain Chemistry

NASA Astrophysics Data System (ADS)

Pei, Y.; Herbst, E.

2011-05-01

Grain surfaces play a crucial role in catalyzing many important chemical reactions in the interstellar medium (ISM). The deterministic rate equation (RE) method has often been used to simulate the surface chemistry. But this method becomes inaccurate when the number of reacting particles per grain is typically less than one, which can occur in the ISM. In this condition, stochastic approaches such as the master equations are adopted. However, these methods have mostly been constrained to small chemical networks due to the large amounts of processor time and computer power required. In this study, we present a hybrid method consisting of the moment equation approximation to the stochastic master equation approach and deterministic rate equations to treat a gas-grain model of homogeneous cold cloud cores with time-independent physical conditions. In this model, we use the standard OSU gas phase network (version OSU2006V3) which involves 458 gas phase species and more than 4000 reactions, and treat it by deterministic rate equations. A medium-sized surface reaction network which consists of 21 species and 19 reactions accounts for the productions of stable molecules such as H_2O, CO, CO_2, H_2CO, CH_3OH, NH_3 and CH_4. These surface reactions are treated by a hybrid method of moment equations (Barzel & Biham 2007) and rate equations: when the abundance of a surface species is lower than a specific threshold, say one per grain, we use the ``stochastic" moment equations to simulate the evolution; when its abundance goes above this threshold, we use the rate equations. A continuity technique is utilized to secure a smooth transition between these two methods. We have run chemical simulations for a time up to 10^8 yr at three temperatures: 10 K, 15 K, and 20 K. The results will be compared with those generated from (1) a completely deterministic model that uses rate equations for both gas phase and grain surface chemistry, (2) the method of modified rate equations (Garrod 2008), which partially takes into account the stochastic effect for surface reactions, and (3) the master equation approach solved using a Monte Carlo technique. At 10 K and standard grain sizes, our model results agree well with the above three methods, while discrepancies appear at higher temperatures and smaller grain sizes.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marchetti, Luca, E-mail: marchetti@cosbi.eu; Priami, Corrado, E-mail: priami@cosbi.eu; University of Trento, Department of Mathematics

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance andmore » accuracy of HRSSA against other state of the art algorithms.« less

Mathematical-Artificial Neural Network Hybrid Model to Predict Roll Force during Hot Rolling of Steel

NASA Astrophysics Data System (ADS)

Rath, S.; Sengupta, P. P.; Singh, A. P.; Marik, A. K.; Talukdar, P.

2013-07-01

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

Analysis of second order harmonic distortion due to transmitter non-linearity and chromatic and modal dispersion of optical OFDM SSB modulated signals in SMF-MMF fiber links

NASA Astrophysics Data System (ADS)

Patel, Dhananjay; Singh, Vinay Kumar; Dalal, U. D.

2017-01-01

Single mode fibers (SMF) are typically used in Wide Area Networks (WAN), Metropolitan Area Networks (MAN) and also find applications in Radio over Fiber (RoF) architectures supporting data transmission in Fiber to the Home (FTTH), Remote Antenna Units (RAUs), in-building networks etc. Multi-mode fibers (MMFs) with low cost, ease of installation and low maintenance are predominantly (85-90%) deployed in-building networks providing data access in local area networks (LANs). The transmission of millimeter wave signals through the SMF in WAN and MAN, along with the reuse of MMF in-building networks will not levy fiber reinstallation cost. The transmission of the millimeter waves experiences signal impairments due to the transmitter non-linearity and modal dispersion of the MMF. The MMF exhibiting large modal dispersion limits the bandwidth-length product of the fiber. The second and higher-order harmonics present in the optical signal fall within the system bandwidth. This causes degradation in the received signal and an unwanted radiation of power at the RAU. The power of these harmonics is proportional to the non-linearity of the transmitter and the modal dispersion of the MMF and should be maintained below the standard values as per the international norms. In this paper, a mathematical model is developed for Second-order Harmonic Distortion (HD2) generated due to non-linearity of the transmitter and chromatic-modal dispersion of the SMF-MMF optic link. This is also verified using a software simulation. The model consists of a Mach Zehnder Modulator (MZM) that generates two m-QAM OFDM Single Sideband (SSB) signals based on phase shift of the hybrid coupler (90° and 120°). Our results show that the SSB signal with 120° hybrid coupler has suppresses the higher-order harmonics and makes the system more robust against the HD2 in the SMF-MMF optic link.

A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

NASA Technical Reports Server (NTRS)

Kerczewski, Robert J.; Chomos, Gerald J.; Griner, James H.; Mainger, Steven W.; Martzaklis, Konstantinos S.; Kachmar, Brian A.

2000-01-01

Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

PubMed

Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

2014-12-01

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Harmon, Frederick G.

2005-11-01

Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster-monitoring missions. The benefits, due to the hybrid and electric-only modes, include increased time-on-station and greater range as compared to electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. This dissertation contributes to the research fields of small unmanned aerial vehicles, hybrid-electric propulsion system control, and intelligent control. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system is provided. The UAV is intended for intelligence, surveillance, and reconnaissance (ISR) missions. A conceptual design reveals the trade-offs that must be considered to take advantage of the hybrid-electric propulsion system. The resulting hybrid-electric propulsion system is a two-point design that includes an engine primarily sized for cruise speed and an electric motor and battery pack that are primarily sized for a slower endurance speed. The electric motor provides additional power for take-off, climbing, and acceleration and also serves as a generator during charge-sustaining operation or regeneration. The intelligent control of the hybrid-electric propulsion system is based on an instantaneous optimization algorithm that generates a hyper-plane from the nonlinear efficiency maps for the internal combustion engine, electric motor, and lithium-ion battery pack. The hyper-plane incorporates charge-depletion and charge-sustaining strategies. The optimization algorithm is flexible and allows the operator/user to assign relative importance between the use of gasoline, electricity, and recharging depending on the intended mission. A MATLAB/Simulink model was developed to test the control algorithms. The Cerebellar Model Arithmetic Computer (CMAC) associative memory neural network is applied to the control of the UAVs parallel hybrid-electric propulsion system. The CMAC neural network approximates the hyper-plane generated from the instantaneous optimization algorithm and produces torque commands for the internal combustion engine and electric motor. The CMAC neural network controller saves on the required memory as compared to a large look-up table by two orders of magnitude. The CMAC controller also prevents the need to compute a hyper-plane or complex logic every time step.

Hybrid attacks on model-based social recommender systems

NASA Astrophysics Data System (ADS)

Yu, Junliang; Gao, Min; Rong, Wenge; Li, Wentao; Xiong, Qingyu; Wen, Junhao

2017-10-01

With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.

Designing and assessing a sustainable networked delivery (SND) system: hybrid business-to-consumer book delivery case study.

PubMed

Kim, Junbeum; Xu, Ming; Kahhat, Ramzy; Allenby, Braden; Williams, Eric

2009-01-01

We attempted to design and assess an example of a sustainable networked delivery (SND) system: a hybrid business-to-consumer book delivery system. This system is intended to reduce costs, achieve significant reductions in energy consumption, and reduce environmental emissions of critical local pollutants and greenhouse gases. The energy consumption and concomitant emissions of this delivery system compared with existing alternative delivery systems were estimated. We found that regarding energy consumption, an emerging hybrid delivery system which is a sustainable networked delivery system (SND) would consume 47 and 7 times less than the traditional networked delivery system (TND) and e-commerce networked delivery system (END). Regarding concomitant emissions, in the case of CO2, the SND system produced 32 and 7 times fewer emissions than the TND and END systems. Also the SND system offer meaningful economic benefit such as the costs of delivery and packaging, to the online retailer, grocery, and consumer. Our research results show that the SND system has a lot of possibilities to save local transportation energy consumption and delivery costs, and reduce environmental emissions in delivery system.

Design, fabrication, and operation of hybrid bionanodevices for biomedical applications

NASA Astrophysics Data System (ADS)

Tucker, Robert Matthew

Cells are the fundamental building blocks of life. Despite their simplicity, cells are extremely versatile, performing a variety of functions including detection, signaling, and repair. While current biomedical devices operate at the organ level, the next generation will operate at the cellular level, combining the nanoscale machinery of cells with the mechanical robustness of synthetic materials in the form of new hybrid devices. This thesis presents advances in four topics concerning the development of nanomedical devices: fabrication, stabilization, control, and operation. First, as feature sizes decrease from the milli- and microscale towards the nanoscale, new fabrication methods must be developed. A new rapid prototyping technique using confocal microscopy was used to produce freely-programmable high-resolution protein patterns of functional motor proteins on thermo-responsive polymer surfaces. Second, hybrid device operation should be temperature-independent, but most biological components have strong responses to temperature fluctuations. To counter operational fluctuations, the temperature-dependent enzymatic activity was characterized for two types of molecular motors with the goal of developing a bionanosystem which is stabilized against temperature fluctuations. Third, replacing electromechanical systems consisting of pumps and batteries with proteins that directly convert chemical potential into mechanical energy increases the efficiency and decreases the size of the bionanodevice, but requires new control methods. An enzymatic network was developed in which fuel was photolytically released to activate molecular shuttles, excess fuel was sequestered using an enzyme, and spatial and temporal control of the system was achieved. Finally, chemically powered bionanodevices will require high-precision nano- and microscale actuators. A two-part hybrid actuator was designed, which consists of a molecular motor-coated synthetic macroscale forcer and a microtubule-based stator. Methods to create and characterize the stator were developed, which can be used to optimize the force generation of the device.

Hybrid modeling and empirical analysis of automobile supply chain network

NASA Astrophysics Data System (ADS)

Sun, Jun-yan; Tang, Jian-ming; Fu, Wei-ping; Wu, Bing-ying

2017-05-01

Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.

Hybrid optoelectronic neural networks using a mutually pumped phase-conjugate mirror

NASA Astrophysics Data System (ADS)

Dunning, G. J.; Owechko, Y.; Soffer, B. H.

1991-06-01

A method is described for interconnecting hybrid optoelectronic neural networks by using a mutually pumped phase conjugate mirror (MP-PCM). In this method, cross talk due to Bragg degeneracies is greatly reduced by storing each weight among many spatially and angularly multiplexed gratings. The effective weight throughput is increased by the parallel updating of weights using outer-product learning. Experiments demonstrated a high degree of interconnectivity between adjacent pixels. A diagram is presented showing the architecture for the optoelectronic neural network using an MP-PCM.

The control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle using a CMAC neural network.

PubMed

Harmon, Frederick G; Frank, Andrew A; Joshi, Sanjay S

2005-01-01

A Simulink model, a propulsion energy optimization algorithm, and a CMAC controller were developed for a small parallel hybrid-electric unmanned aerial vehicle (UAV). The hybrid-electric UAV is intended for military, homeland security, and disaster-monitoring missions involving intelligence, surveillance, and reconnaissance (ISR). The Simulink model is a forward-facing simulation program used to test different control strategies. The flexible energy optimization algorithm for the propulsion system allows relative importance to be assigned between the use of gasoline, electricity, and recharging. A cerebellar model arithmetic computer (CMAC) neural network approximates the energy optimization results and is used to control the parallel hybrid-electric propulsion system. The hybrid-electric UAV with the CMAC controller uses 67.3% less energy than a two-stroke gasoline-powered UAV during a 1-h ISR mission and 37.8% less energy during a longer 3-h ISR mission.

Liposome-Cross-Linked Hybrid Hydrogels for Glutathione-Triggered Delivery of Multiple Cargo Molecules.

PubMed

Liang, Yingkai; Kiick, Kristi L

2016-02-08

Novel, liposome-cross-linked hybrid hydrogels cross-linked by the Michael-type addition of thiols with maleimides were prepared via the use of maleimide-functionalized liposome cross-linkers and thiolated polyethylene glycol (PEG) polymers. Gelation of the materials was confirmed by oscillatory rheology experiments. These hybrid hydrogels are rendered degradable upon exposure to thiol-containing molecules such as glutathione (GSH), via the incorporation of selected thioether succinimide cross-links between the PEG polymers and liposome nanoparticles. Dynamic light scattering (DLS) characterization confirmed that intact liposomes were released upon network degradation. Owing to the hierarchical structure of the network, multiple cargo molecules relevant for chemotherapies, namely doxorubicin (DOX) and cytochrome c, were encapsulated and simultaneously released from the hybrid hydrogels, with differential release profiles that were driven by degradation-mediated release and Fickian diffusion, respectively. This work introduces a facile approach for the development of advanced, hybrid drug delivery vehicles that exhibit novel chemical degradation.

Information filtering on coupled social networks.

PubMed

Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

2014-01-01

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.

Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

NASA Astrophysics Data System (ADS)

Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

2016-12-01

Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

End-to-End ASR-Free Keyword Search From Speech

NASA Astrophysics Data System (ADS)

Audhkhasi, Kartik; Rosenberg, Andrew; Sethy, Abhinav; Ramabhadran, Bhuvana; Kingsbury, Brian

2017-12-01

End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.

Model-based design of RNA hybridization networks implemented in living cells.

PubMed

Rodrigo, Guillermo; Prakash, Satya; Shen, Shensi; Majer, Eszter; Daròs, José-Antonio; Jaramillo, Alfonso

2017-09-19

Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks

PubMed Central

Lee, JongHyup; Pak, Dohyun

2016-01-01

For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743

Throughput and Energy Efficiency of a Cooperative Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

PubMed Central

Ghosh, Arindam; Lee, Jae-Won; Cho, Ho-Shin

2013-01-01

Due to its efficiency, reliability and better channel and resource utilization, cooperative transmission technologies have been attractive options in underwater as well as terrestrial sensor networks. Their performance can be further improved if merged with forward error correction (FEC) techniques. In this paper, we propose and analyze a retransmission protocol named Cooperative-Hybrid Automatic Repeat reQuest (C-HARQ) for underwater acoustic sensor networks, which exploits both the reliability of cooperative ARQ (CARQ) and the efficiency of incremental redundancy-hybrid ARQ (IR-HARQ) using rate-compatible punctured convolution (RCPC) codes. Extensive Monte Carlo simulations are performed to investigate the performance of the protocol, in terms of both throughput and energy efficiency. The results clearly reveal the enhancement in performance achieved by the C-HARQ protocol, which outperforms both CARQ and conventional stop and wait ARQ (S&W ARQ). Further, using computer simulations, optimum values of various network parameters are estimated so as to extract the best performance out of the C-HARQ protocol. PMID:24217359

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

NASA Astrophysics Data System (ADS)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network

NASA Astrophysics Data System (ADS)

Pratiwi, A. B.; Damayanti, A.; Miswanto

2017-07-01

Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.

Weather forecasting based on hybrid neural model

NASA Astrophysics Data System (ADS)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Using hybridization networks to retrace the evolution of Indo-European languages.

PubMed

Willems, Matthieu; Lord, Etienne; Laforest, Louise; Labelle, Gilbert; Lapointe, François-Joseph; Di Sciullo, Anna Maria; Makarenkov, Vladimir

2016-09-06

Curious parallels between the processes of species and language evolution have been observed by many researchers. Retracing the evolution of Indo-European (IE) languages remains one of the most intriguing intellectual challenges in historical linguistics. Most of the IE language studies use the traditional phylogenetic tree model to represent the evolution of natural languages, thus not taking into account reticulate evolutionary events, such as language hybridization and word borrowing which can be associated with species hybridization and horizontal gene transfer, respectively. More recently, implicit evolutionary networks, such as split graphs and minimal lateral networks, have been used to account for reticulate evolution in linguistics. Striking parallels existing between the evolution of species and natural languages allowed us to apply three computational biology methods for reconstruction of phylogenetic networks to model the evolution of IE languages. We show how the transfer of methods between the two disciplines can be achieved, making necessary methodological adaptations. Considering basic vocabulary data from the well-known Dyen's lexical database, which contains word forms in 84 IE languages for the meanings of a 200-meaning Swadesh list, we adapt a recently developed computational biology algorithm for building explicit hybridization networks to study the evolution of IE languages and compare our findings to the results provided by the split graph and galled network methods. We conclude that explicit phylogenetic networks can be successfully used to identify donors and recipients of lexical material as well as the degree of influence of each donor language on the corresponding recipient languages. We show that our algorithm is well suited to detect reticulate relationships among languages, and present some historical and linguistic justification for the results obtained. Our findings could be further refined if relevant syntactic, phonological and morphological data could be analyzed along with the available lexical data.

Location-Based Services in Vehicular Networks

ERIC Educational Resources Information Center

Wu, Di

2013-01-01

Location-based services have been identified as a promising communication paradigm in highly mobile and dynamic vehicular networks. However, existing mobile ad hoc networking cannot be directly applied to vehicular networking due to differences in traffic conditions, mobility models and network topologies. On the other hand, hybrid architectures…

Hybrid protection algorithms based on game theory in multi-domain optical networks

NASA Astrophysics Data System (ADS)

Guo, Lei; Wu, Jingjing; Hou, Weigang; Liu, Yejun; Zhang, Lincong; Li, Hongming

2011-12-01

With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user's utility, the network operator's utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator's utility, while IEP has better user's utility.

Super-Joule heating in graphene and silver nanowire network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maize, Kerry; Das, Suprem R.; Sadeque, Sajia

Transistors, sensors, and transparent conductors based on randomly assembled nanowire networks rely on multi-component percolation for unique and distinctive applications in flexible electronics, biochemical sensing, and solar cells. While conduction models for 1-D and 1-D/2-D networks have been developed, typically assuming linear electronic transport and self-heating, the model has not been validated by direct high-resolution characterization of coupled electronic pathways and thermal response. In this letter, we show the occurrence of nonlinear “super-Joule” self-heating at the transport bottlenecks in networks of silver nanowires and silver nanowire/single layer graphene hybrid using high resolution thermoreflectance (TR) imaging. TR images at the microscopicmore » self-heating hotspots within nanowire network and nanowire/graphene hybrid network devices with submicron spatial resolution are used to infer electrical current pathways. The results encourage a fundamental reevaluation of transport models for network-based percolating conductors.« less

Some characteristics of supernetworks based on unified hybrid network theory framework

NASA Astrophysics Data System (ADS)

Liu, Qiang; Fang, Jin-Qing; Li, Yong

Comparing with single complex networks, supernetworks are more close to the real world in some ways, and have become the newest research hot spot in the network science recently. Some progresses have been made in the research of supernetworks, but the theoretical research method and complex network characteristics of supernetwork models are still needed to further explore. In this paper, we propose three kinds of supernetwork models with three layers based on the unified hybrid network theory framework (UHNTF), and introduce preferential and random linking, respectively, between the upper and lower layers. Then we compared the topological characteristics of the single networks with the supernetwork models. In order to analyze the influence of the interlayer edges on network characteristics, the cross-degree is defined as a new important parameter. Then some interesting new phenomena are found, the results imply this supernetwork model has reference value and application potential.

Always-on Education and Hybrid Learning Spaces

ERIC Educational Resources Information Center

Trentin, Guglielmo

2016-01-01

The possibility of being always connected to the Internet and/or the mobile network (hence the term "always-on") is increasingly blurring the borderline between physical and digital spaces, introducing a new concept of space, known as "hybrid." Innovative forms of teaching have been developing in hybrid spaces for some time…

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization.

PubMed

Saeed, Nasir; Celik, Abdulkadir; Al-Naffouri, Tareq Y; Alouini, Mohamed-Slim

2017-12-26

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique.

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization

PubMed Central

Saeed, Nasir; Celik, Abdulkadir; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2017-01-01

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique. PMID:29278405

De-optical-line-terminal hybrid access-aggregation optical network for time-sensitive services based on software-defined networking orchestration

NASA Astrophysics Data System (ADS)

Bai, Wei; Yang, Hui; Xiao, Hongyun; Yu, Ao; He, Linkuan; Zhang, Jie; Li, Zhen; Du, Yi

2017-11-01

With the increase in varieties of services in network, time-sensitive services (TSSs) appear and bring forward an impending need for delay performance. Ultralow-latency communication has become one of the important development goals for many scenarios in the coming 5G era (e.g., robotics and driverless cars). However, the conventional methods, which decrease delay by promoting the available resources and the network transmission speed, have limited effect; a new breakthrough for ultralow-latency communication is necessary. We propose a de-optical-line-terminal (De-OLT) hybrid access-aggregation optical network (DAON) for TSS based on software-defined networking (SDN) orchestration. In this network, low-latency all-optical communication based on optical burst switching can be achieved by removing OLT. For supporting this network and guaranteeing the quality of service for TSSs, we design SDN-driven control method and service provision method. Numerical results demonstrate the proposed DAON promotes network service efficiency and avoids traffic congestion.

Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

NASA Astrophysics Data System (ADS)

Wei, Chengying; Xiong, Cuilian; Liu, Huanlin

2017-12-01

Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

An All-Optical Access Metro Interface for Hybrid WDM/TDM PON Based on OBS

NASA Astrophysics Data System (ADS)

Segarra, Josep; Sales, Vicent; Prat, Josep

2007-04-01

A new all-optical access metro network interface based on optical burst switching (OBS) is proposed. A hybrid wavelength-division multiplexing/time-division multiplexing (WDM/TDM) access architecture with reflective optical network units (ONUs), an arrayed-waveguide-grating outside plant, and a tunable laser stack at the optical line terminal (OLT) is presented as a solution for the passive optical network. By means of OBS and a dynamic bandwidth allocation (DBA) protocol, which polls the ONUs, the available access bandwidth is managed. All the network intelligence and costly equipment is located at the OLT, where the DBA module is centrally implemented, providing quality of service (QoS). To scale this access network, an optical cross connect (OXC) is then used to attain a large number of ONUs by the same OLT. The hybrid WDM/TDM structure is also extended toward the metropolitan area network (MAN) by introducing the concept of OBS multiplexer (OBS-M). The network element OBS-M bridges the MAN and access networks by offering all-optical cross connection, wavelength conversion, and data signaling. The proposed innovative OBS-M node yields a full optical data network, interfacing access and metro with a geographically distributed access control. The resulting novel access metro architectures are nonblocking and, with an improved signaling, provide QoS, scalability, and very low latency. Finally, numerical analysis and simulations demonstrate the traffic performance of the proposed access scheme and all-optical access metro interface and architectures.

Stochastic resonance enhancement of small-world neural networks by hybrid synapses and time delay

NASA Astrophysics Data System (ADS)

Yu, Haitao; Guo, Xinmeng; Wang, Jiang

2017-01-01

The synergistic effect of hybrid electrical-chemical synapses and information transmission delay on the stochastic response behavior in small-world neuronal networks is investigated. Numerical results show that, the stochastic response behavior can be regulated by moderate noise intensity to track the rhythm of subthreshold pacemaker, indicating the occurrence of stochastic resonance (SR) in the considered neural system. Inheriting the characteristics of two types of synapses-electrical and chemical ones, neural networks with hybrid electrical-chemical synapses are of great improvement in neuron communication. Particularly, chemical synapses are conducive to increase the network detectability by lowering the resonance noise intensity, while the information is better transmitted through the networks via electrical coupling. Moreover, time delay is able to enhance or destroy the periodic stochastic response behavior intermittently. In the time-delayed small-world neuronal networks, the introduction of electrical synapses can significantly improve the signal detection capability by widening the range of optimal noise intensity for the subthreshold signal, and the efficiency of SR is largely amplified in the case of pure chemical couplings. In addition, the stochastic response behavior is also profoundly influenced by the network topology. Increasing the rewiring probability in pure chemically coupled networks can always enhance the effect of SR, which is slightly influenced by information transmission delay. On the other hand, the capacity of information communication is robust to the network topology within the time-delayed neuronal systems including electrical couplings.

The Neighborhood Auditing Tool: A Hybrid Interface for Auditing the UMLS

PubMed Central

Morrey, C. Paul; Geller, James; Halper, Michael; Perl, Yehoshua

2009-01-01

The UMLS’s integration of more than 100 source vocabularies, not necessarily consistent with one another, causes some inconsistencies. The purpose of auditing the UMLS is to detect such inconsistencies and to suggest how to resolve them while observing the requirement of fully representing the content of each source in the UMLS. A software tool, called the Neighborhood Auditing Tool (NAT), that facilitates UMLS auditing is presented. The NAT supports “neighborhood-based” auditing, where, at any given time, an auditor concentrates on a single focus concept and one of a variety of neighborhoods of its closely related concepts. Typical diagrammatic displays of concept networks have a number of shortcomings, so the NAT utilizes a hybrid diagram/text interface that features stylized neighborhood views which retain some of the best features of both the diagrammatic layouts and text windows while avoiding the shortcomings. The NAT allows an auditor to display knowledge from both the Metathesaurus (concept) level and the Semantic Network (semantic type) level. Various additional features of the NAT that support the auditing process are described. The usefulness of the NAT is demonstrated through a group of case studies. Its impact is tested with a study involving a select group of auditors. PMID:19475725

Sequence-specific unusual (1-->2)-type helical turns in alpha/beta-hybrid peptides.

PubMed

Prabhakaran, Panchami; Kale, Sangram S; Puranik, Vedavati G; Rajamohanan, P R; Chetina, Olga; Howard, Judith A K; Hofmann, Hans-Jörg; Sanjayan, Gangadhar J

2008-12-31

This article describes novel conformationally ordered alpha/beta-hybrid peptides consisting of repeating l-proline-anthranilic acid building blocks. These oligomers adopt a compact, right-handed helical architecture determined by the intrinsic conformational preferences of the individual amino acid residues. The striking feature of these oligomers is their ability to display an unusual periodic pseudo beta-turn network of nine-membered hydrogen-bonded rings formed in the forward direction of the sequence by 1-->2 amino acid interactions both in solid-state and in solution. Conformational investigations of several of these oligomers by single-crystal X-ray diffraction, solution-state NMR, and ab initio MO theory suggest that the characteristic steric and dihedral angle restraints exerted by proline are essential for stabilizing the unusual pseudo beta-turn network found in these oligomers. Replacing proline by the conformationally flexible analogue alanine (Ala) or by the conformationally more constrained alpha-amino isobutyric acid (Aib) had an adverse effect on the stabilization of this structural architecture. These findings increase the potential to design novel secondary structure elements profiting from the steric and dihedral angle constraints of the amino acid constituents and help to augment the conformational space available for synthetic oligomer design with diverse backbone structures.

The mature anther-preferentially expressed genes are associated with pollen fertility, pollen germination and anther dehiscence in rice.

PubMed

Ling, Sheng; Chen, Caisheng; Wang, Yang; Sun, Xiaocong; Lu, Zhanhua; Ouyang, Yidan; Yao, Jialing

2015-02-19

The anthers and pollen grains are critical for male fertility and hybrid rice breeding. The development of rice mature anther and pollen consists of multiple continuous stages. However, molecular mechanisms regulating mature anther development were poorly understood. In this study, we have identified 291 mature anther-preferentially expressed genes (OsSTA) in rice based on Affymetrix microarray data. Gene Ontology (GO) analysis indicated that OsSTA genes mainly participated in metabolic and cellular processes that are likely important for rice anther and pollen development. The expression patterns of OsSTA genes were validated using real-time PCR and mRNA in situ hybridizations. Cis-element identification showed that most of the OsSTA genes had the cis-elements responsive to phytohormone regulation. Co-expression analysis of OsSTA genes showed that genes annotated with pectinesterase and calcium ion binding activities were rich in the network, suggesting that OsSTA genes could be involved in pollen germination and anther dehiscence. Furthermore, OsSTA RNAi transgenic lines showed male-sterility and pollen germination defects. The results suggested that OsSTA genes function in rice male fertility, pollen germination and anther dehiscence and established molecular regulating networks that lay the foundation for further functional studies.

Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

NASA Astrophysics Data System (ADS)

Furferi, Rocco; Governi, Lapo; Volpe, Yary

2016-11-01

Color matching of fabric blends is a key issue for the textile industry, mainly due to the rising need to create high-quality products for the fashion market. The process of mixing together differently colored fibers to match a desired color is usually performed by using some historical recipes, skillfully managed by company colorists. More often than desired, the first attempt in creating a blend is not satisfactory, thus requiring the experts to spend efforts in changing the recipe with a trial-and-error process. To confront this issue, a number of computer-based methods have been proposed in the last decades, roughly classified into theoretical and artificial neural network (ANN)-based approaches. Inspired by the above literature, the present paper provides a method for accurate estimation of spectrophotometric response of a textile blend composed of differently colored fibers made of different materials. In particular, the performance of the Kubelka-Munk (K-M) theory is enhanced by introducing an artificial intelligence approach to determine a more consistent value of the nonlinear function relationship between the blend and its components. Therefore, a hybrid K-M+ANN-based method capable of modeling the color mixing mechanism is devised to predict the reflectance values of a blend.

Source Localization Using Wireless Sensor Networks

DTIC Science & Technology

2006-06-01

performance of the hybrid SI/ML estimation method. A wireless sensor network is simulated in NS-2 to study the network throughput, delay and jitter...indicate that the wireless sensor network has low delay and can support fast information exchange needed in counter-sniper applications.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

NASA Astrophysics Data System (ADS)

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

PubMed Central

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889

Information Filtering on Coupled Social Networks

PubMed Central

Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

2014-01-01

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

Hybrid network defense model based on fuzzy evaluation.

PubMed

Cho, Ying-Chiang; Pan, Jen-Yi

2014-01-01

With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

PubMed

Pesavento, Michael J; Pinto, David J

2012-11-01

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

Architectures and Design for Next-Generation Hybrid Circuit/Packet Networks

NASA Astrophysics Data System (ADS)

Vadrevu, Sree Krishna Chaitanya

Internet traffic is increasing rapidly at an annual growth rate of 35% with aggregate traffic exceeding several Exabyte's per month. The traffic is also becoming heterogeneous in bandwidth and quality-of-service (QoS) requirements with growing popularity of cloud computing, video-on-demand (VoD), e-science, etc. Hybrid circuit/packet networks which can jointly support circuit and packet services along with the adoption of high-bit-rate transmission systems form an attractive solution to address the traffic growth. 10 Gbps and 40 Gbps transmission systems are widely deployed in telecom backbone networks such as Comcast, AT&T, etc., and network operators are considering migration to 100 Gbps and beyond. This dissertation proposes robust architectures, capacity migration strategies, and novel service frameworks for next-generation hybrid circuit/packet architectures. In this dissertation, we study two types of hybrid circuit/packet networks: a) IP-over-WDM networks, in which the packet (IP) network is overlaid on top of the circuit (optical WDM) network and b) Hybrid networks in which the circuit and packet networks are deployed side by side such as US DoE's ESnet. We investigate techniques to dynamically migrate capacity between the circuit and packet sections by exploiting traffic variations over a day, and our methods show that significant bandwidth savings can be obtained with improved reliability of services. Specifically, we investigate how idle backup circuit capacity can be used to support packet services in IP-over-WDM networks, and similarly, excess capacity in packet network to support circuit services in ESnet. Control schemes that enable our mechanisms are also discussed. In IP-over-WDM networks, with upcoming 100 Gbps and beyond, dedicated protection will induce significant under-utilization of backup resources. We investigate design strategies to loan idle circuit backup capacity to support IP/packet services. However, failure of backup circuits will preempt IP services routed over them, and thus it is important to ensure IP topology survivability to successfully re-route preempted IP services. Integer-linear-program (ILP) and heuristic solutions have been developed and network cost reduction up to 60% has been observed. In ESnet, we study loaning packet links to support circuit services. Mixed-line-rate (MLR) networks supporting 10/40/100 Gbps on the same fiber are becoming increasingly popular. Services that accept degradation in bandwidth, latency, jitter, etc. under failure scenarios for lower cost are known as degraded services. We study degradation in bandwidth for lower cost under failure scenarios, a concept called partial protection, in the context of MLR networks. We notice partial protection enables significant cost savings compared to full protection. To cope with traffic growth, network operators need to deploy equipment at periodic time intervals, and this is known as the multi-period planning and upgrade problem. We study three important multi-period planning approaches, namely incremental planning, all-period planning, and two-period planning with mixed line rates. Our approaches predict the network equipment that needs to be deployed optimally at which nodes and at which time periods in the network to meet QoS requirements.

Demonstration and field trial of a resilient hybrid NG-PON test-bed

NASA Astrophysics Data System (ADS)

Prat, Josep; Polo, Victor; Schrenk, Bernhard; Lazaro, Jose A.; Bonada, Francesc; Lopez, Eduardo T.; Omella, Mireia; Saliou, Fabienne; Le, Quang T.; Chanclou, Philippe; Leino, Dmitri; Soila, Risto; Spirou, Spiros; Costa, Liliana; Teixeira, Antonio; Tosi-Beleffi, Giorgio M.; Klonidis, Dimitrios; Tomkos, Ioannis

2014-10-01

A multi-layer next generation PON prototype has been built and tested, to show the feasibility of extended hybrid DWDM/TDM-XGPON FTTH networks with resilient optically-integrated ring-trees architecture, supporting broadband multimedia services. It constitutes a transparent common platform for the coexistence of multiple operators sharing the optical infrastructure of the central metro ring, passively combining the access and the metropolitan network sections. It features 32 wavelength connections at 10 Gbps, up to 1000 users distributed in 16 independent resilient sub-PONs over 100 km. This paper summarizes the network operation, demonstration and field trial results.

Key management schemes using routing information frames in secure wireless sensor networks

NASA Astrophysics Data System (ADS)

Kamaev, V. A.; Finogeev, A. G.; Finogeev, A. A.; Parygin, D. S.

2017-01-01

The article considers the problems and objectives of key management for data encryption in wireless sensor networks (WSN) of SCADA systems. The structure of the key information in the ZigBee network and methods of keys obtaining are discussed. The use of a hybrid key management schemes is most suitable for WSN. The session symmetric key is used to encrypt the sensor data, asymmetric keys are used to encrypt the session key transmitted from the routing information. Three algorithms of hybrid key management using routing information frames determined by routing methods and the WSN topology are presented.

Scalable Multicast Protocols for Overlapped Groups in Broker-Based Sensor Networks

NASA Astrophysics Data System (ADS)

Kim, Chayoung; Ahn, Jinho

In sensor networks, there are lots of overlapped multicast groups because of many subscribers, associated with their potentially varying specific interests, querying every event to sensors/publishers. And gossip based communication protocols are promising as one of potential solutions providing scalability in P(Publish)/ S(Subscribe) paradigm in sensor networks. Moreover, despite the importance of both guaranteeing message delivery order and supporting overlapped multicast groups in sensor or P2P networks, there exist little research works on development of gossip-based protocols to satisfy all these requirements. In this paper, we present two versions of causally ordered delivery guaranteeing protocols for overlapped multicast groups. The one is based on sensor-broker as delegates and the other is based on local views and delegates representing subscriber subgroups. In the sensor-broker based protocol, sensor-broker might lead to make overlapped multicast networks organized by subscriber's interests. The message delivery order has been guaranteed consistently and all multicast messages are delivered to overlapped subscribers using gossip based protocols by sensor-broker. Therefore, these features of the sensor-broker based protocol might be significantly scalable rather than those of the protocols by hierarchical membership list of dedicated groups like traditional committee protocols. And the subscriber-delegate based protocol is much stronger rather than fully decentralized protocols guaranteeing causally ordered delivery based on only local views because the message delivery order has been guaranteed consistently by all corresponding members of the groups including delegates. Therefore, this feature of the subscriber-delegate protocol is a hybrid approach improving the inherent scalability of multicast nature by gossip-based technique in all communications.

Network reliability maximization for stochastic-flow network subject to correlated failures using genetic algorithm and tabu\\xA0search

NASA Astrophysics Data System (ADS)

Yeh, Cheng-Ta; Lin, Yi-Kuei; Yang, Jo-Yun

2018-07-01

Network reliability is an important performance index for many real-life systems, such as electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. Most system supervisors respect the network reliability maximization by finding the optimal multi-state resource assignment, which is one resource to each arc. However, a disaster may cause correlated failures for the assigned resources, affecting the network reliability. This article focuses on determining the optimal resource assignment with maximal network reliability for SFNs. To solve the problem, this study proposes a hybrid algorithm integrating the genetic algorithm and tabu search to determine the optimal assignment, called the hybrid GA-TS algorithm (HGTA), and integrates minimal paths, recursive sum of disjoint products and the correlated binomial distribution to calculate network reliability. Several practical numerical experiments are adopted to demonstrate that HGTA has better computational quality than several popular soft computing algorithms.

Hybrid forum or network? The social and political construction of an international 'technical consultation': male circumcision and HIV prevention.

PubMed

Giami, Alain; Perrey, Christophe; Mendonça, André Luiz de Oliveira; de Camargo, Kenneth Rochel

2015-01-01

The technical consultation in Montreux, organised by World Health Organization and UNAIDS in 2007, recommended male circumcision as a method for preventing HIV transmission. This consultation came out of a long process of releasing reports and holding international and regional conferences, a process steered by an informal network. This network's relations with other parties is analysed along with its way of working and the exchanges during the technical consultation that led up to the formal adoption of a recommendation. Conducted in relation to the concepts of a 'hybrid forum' and 'network', this article shows that the decision was based on the formation and consolidation of a network of persons. They were active in all phases of this process, ranging from studies of the recommendation's efficacy, feasibility and acceptability to its adoption and implementation. In this sense, this consultation cannot be described as the constitution of a 'hybrid forum', which is characterised by its openness to a debate as well as a plurality of issues formulated by the actors and of resources used by them. On the contrary, little room was allowed for contradictory discussions, as if the decision had already been made before the Montreux consultation.

Applying differential dynamic logic to reconfigurable biological networks.

PubMed

Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena

2017-09-01

Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.

Autumn Algorithm-Computation of Hybridization Networks for Realistic Phylogenetic Trees.

PubMed

Huson, Daniel H; Linz, Simone

2018-01-01

A minimum hybridization network is a rooted phylogenetic network that displays two given rooted phylogenetic trees using a minimum number of reticulations. Previous mathematical work on their calculation has usually assumed the input trees to be bifurcating, correctly rooted, or that they both contain the same taxa. These assumptions do not hold in biological studies and "realistic" trees have multifurcations, are difficult to root, and rarely contain the same taxa. We present a new algorithm for computing minimum hybridization networks for a given pair of "realistic" rooted phylogenetic trees. We also describe how the algorithm might be used to improve the rooting of the input trees. We introduce the concept of "autumn trees", a nice framework for the formulation of algorithms based on the mathematics of "maximum acyclic agreement forests". While the main computational problem is hard, the run-time depends mainly on how different the given input trees are. In biological studies, where the trees are reasonably similar, our parallel implementation performs well in practice. The algorithm is available in our open source program Dendroscope 3, providing a platform for biologists to explore rooted phylogenetic networks. We demonstrate the utility of the algorithm using several previously studied data sets.

Hybrid intelligent methodology to design translation invariant morphological operators for Brazilian stock market prediction.

PubMed

Araújo, Ricardo de A

2010-12-01

This paper presents a hybrid intelligent methodology to design increasing translation invariant morphological operators applied to Brazilian stock market prediction (overcoming the random walk dilemma). The proposed Translation Invariant Morphological Robust Automatic phase-Adjustment (TIMRAA) method consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) with a Quantum-Inspired Evolutionary Algorithm (QIEA), which searches for the best time lags to reconstruct the phase space of the time series generator phenomenon and determines the initial (sub-optimal) parameters of the MMNN. Each individual of the QIEA population is further trained by the Back Propagation (BP) algorithm to improve the MMNN parameters supplied by the QIEA. Also, for each prediction model generated, it uses a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in stock market time series. Furthermore, an experimental analysis is conducted with the proposed method through four Brazilian stock market time series, and the achieved results are discussed and compared to results found with random walk models and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) and Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Physiological β-catenin signaling controls self-renewal networks and generation of stem-like cells from nasopharyngeal carcinoma.

PubMed

Cheng, Yue; Cheung, Arthur Kwok Leung; Ko, Josephine Mun Yee; Phoon, Yee Peng; Chiu, Pui Man; Lo, Paulisally Hau Yi; Waterman, Marian L; Lung, Maria Li

2013-09-27

A few reports suggested that low levels of Wnt signaling might drive cell reprogramming, but these studies could not establish a clear relationship between Wnt signaling and self-renewal networks. There are ongoing debates as to whether and how the Wnt/β-catenin signaling is involved in the control of pluripotency gene networks. Additionally, whether physiological β-catenin signaling generates stem-like cells through interactions with other pathways is as yet unclear. The nasopharyngeal carcinoma HONE1 cells have low expression of β-catenin and wild-type expression of p53, which provided a possibility to study regulatory mechanism of stemness networks induced by physiological levels of Wnt signaling in these cells. Introduction of increased β-catenin signaling, haploid expression of β-catenin under control by its natural regulators in transferred chromosome 3, resulted in activation of Wnt/β-catenin networks and dedifferentiation in HONE1 hybrid cell lines, but not in esophageal carcinoma SLMT1 hybrid cells that had high levels of endogenous β-catenin expression. HONE1 hybrid cells displayed stem cell-like properties, including enhancement of CD24(+) and CD44(+) populations and generation of spheres that were not observed in parental HONE1 cells. Signaling cascades were detected in HONE1 hybrid cells, including activation of p53- and RB1-mediated tumor suppressor pathways, up-regulation of Nanog-, Oct4-, Sox2-, and Klf4-mediated pluripotency networks, and altered E-cadherin expression in both in vitro and in vivo assays. qPCR array analyses further revealed interactions of physiological Wnt/β-catenin signaling with other pathways such as epithelial-mesenchymal transition, TGF-β, Activin, BMPR, FGFR2, and LIFR- and IL6ST-mediated cell self-renewal networks. Using β-catenin shRNA inhibitory assays, a dominant role for β-catenin in these cellular network activities was observed. The expression of cell surface markers such as CD9, CD24, CD44, CD90, and CD133 in generated spheres was progressively up-regulated compared to HONE1 hybrid cells. Thirty-four up-regulated components of the Wnt pathway were identified in these spheres. Wnt/β-catenin signaling regulates self-renewal networks and plays a central role in the control of pluripotency genes, tumor suppressive pathways and expression of cancer stem cell markers. This current study provides a novel platform to investigate the interaction of physiological Wnt/β-catenin signaling with stemness transition networks.

Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

PubMed

Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

PubMed Central

Banik, Shipra; Khodadad Khan, A. F. M.; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange. PMID:24701205

Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

NASA Astrophysics Data System (ADS)

Vafaei, Masoud; Afrand, Masoud; Sina, Nima; Kalbasi, Rasool; Sourani, Forough; Teimouri, Hamid

2017-01-01

In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1%, 0.15%, 0.2%, 0.4% and 0.6% in the temperature range of 25-50 °C. In this way, at the first, thirty six experimental data was presented to determine the thermal conductivity ratio of the hybrid nanofluid. Then, four optimal artificial neural networks with 6, 8, 10 and 12 neurons in hidden layer were designed to predict the thermal conductivity ratio of the nanofluid. The comparison between four optimal ANN results and experimental showed that the ANN with 12 neurons in hidden layer was the best model. Moreover, the results obtained from the best ANN indicated the maximum deviation margin of 0.8%.

Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

PubMed Central

Wang, Meng; Wu, Kai; Lu, Changhong; Kong, Xiangyin

2015-01-01

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer. PMID:26504486

A hybrid neural learning algorithm using evolutionary learning and derivative free local search method.

PubMed

Ghosh, Ranadhir; Yearwood, John; Ghosh, Moumita; Bagirov, Adil

2006-06-01

In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

2015-11-01

Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network and potentially contributes to development of improved therapy for neurological disorders such as Parkinson's disease.

Indoor communications networks realized through hybrid free-space optical and Wi-Fi links

NASA Astrophysics Data System (ADS)

Liverman, Spencer; Wang, Qiwei; Chu, Yu-Chung; Borah, Anindita; Wang, Songtao; Natarajan, Arun; Nguyen, Thinh; Wang, Alan X.

2018-01-01

Recently, free-space optical (FSO) networks have been investigated as a potential replacement for traditional WiFi networks due to their large bandwidth potentials. However, FSO networks often suffer from a lack of mobility. We present a hybrid free-space optical and radio frequency (RF) system that we have named WiFO, which seamlessly integrates free-space optical links with pre-existing WiFi networks. The free-space optical link in this system utilizes infrared LEDs operating at a wavelength of 850nm and is capable of transmitting 50Mbps over a three-meter distance. In this hybrid system, optical transmitters are embedded periodically throughout the ceiling of a workspace. Each transmitter directs an optical signal downward in a diffuse light cone, establishing a line of sight optical link. Line of sight communications links have an intrinsic physical layer of security due to the fact that a user must be directly in the path of transmission to access the link; however, this feature also poses a challenge for mobility. In our system, if the free-space optical link is interrupted, a control algorithm redirects traffic over a pre-existing WiFi link ensuring uninterrupted transmissions. After data packets are received, acknowledgments are sent back to a central access point via a WiFi link. As the demand for wireless bandwidth continues to increase exponentially, utilizing the unregulated bandwidth contained within optical spectrum will become necessary. Our fully functional hybrid free-space optical and WiFi prototype system takes full advantage of the untapped bandwidth potential in the optical spectrum, while also maintaining the mobility inherent in WiFi networks.

Hybrid silica monolith for microextraction by packed sorbent to determine drugs from plasma samples by liquid chromatography-tandem mass spectrometry.

PubMed

de Souza, Israel D; Domingues, Diego S; Queiroz, Maria E C

2015-08-01

The present study (1) reports on the synthesis of two hybrid silica monoliths functionalized with aminopropyl or cyanopropyl groups by the sol-gel process; (2) evaluates these monoliths as selective stationary phase for microextraction by packed sorbent (MEPS) to determine drugs in plasma samples via liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the multiple reactions monitoring (MRM) mode; and (3) discusses important factors related to the optimization of MEPS efficiency as well as the carryover effect. The prepared hybrid silica monoliths consisted of a uniform, porous, and continuous silica monolithic network. The structure of the aminopropyl hybrid silica monolith was more compact than the structure of the cyanopropyl hybrid silica monolith. The Fourier-transform infrared spectroscopy (FTIR) spectra of the hybrid silica monoliths displayed readily identifiable peaks, characteristic of the cyanopropyl and aminopropyl groups. Compared with the aminopropyl hybrid silica phase, the cyanopropyl hybrid silica phase exhibited higher binding capacity for most of the target drugs. The developed method afforded adequate linearity at concentrations ranging from the lower limit of quantification (0.05-1.00 ng mL(-1)) to the upper limit of quantification (40-10,500 ng mL(-1)); the coefficients of determination (r(2)) were higher than 0.9955. The precision of the method presented coefficients of variation (CV) lower than 14%; the relative standard error (RSE) of the accuracy ranged from -12% to 14%. The developed method allowed for simultaneous analysis of five antipsychotics (olanzapine, quetiapine, clozapine, haloperidol, and chlorpromazine) in combination with seven antidepressants (mirtazapine, paroxetine, citalopram, sertraline, imipramine, clomipramine, fluoxetine), two anticonvulsants (carbamazepine and lamotrigine), and two anxiolytics (diazepam and clonazepam) in plasma samples from schizophrenic patients, which should be valuable for therapeutic drug monitoring purposes. Copyright © 2015 Elsevier B.V. All rights reserved.

GDTN: Genome-Based Delay Tolerant Network Formation in Heterogeneous 5G Using Inter-UA Collaboration.

PubMed

You, Ilsun; Sharma, Vishal; Atiquzzaman, Mohammed; Choo, Kim-Kwang Raymond

2016-01-01

With a more Internet-savvy and sophisticated user base, there are more demands for interactive applications and services. However, it is a challenge for existing radio access networks (e.g. 3G and 4G) to cope with the increasingly demanding requirements such as higher data rates and wider coverage area. One potential solution is the inter-collaborative deployment of multiple radio devices in a 5G setting designed to meet exacting user demands, and facilitate the high data rate requirements in the underlying networks. These heterogeneous 5G networks can readily resolve the data rate and coverage challenges. Networks established using the hybridization of existing networks have diverse military and civilian applications. However, there are inherent limitations in such networks such as irregular breakdown, node failures, and halts during speed transmissions. In recent years, there have been attempts to integrate heterogeneous 5G networks with existing ad hoc networks to provide a robust solution for delay-tolerant transmissions in the form of packet switched networks. However, continuous connectivity is still required in these networks, in order to efficiently regulate the flow to allow the formation of a robust network. Therefore, in this paper, we present a novel network formation consisting of nodes from different network maneuvered by Unmanned Aircraft (UA). The proposed model utilizes the features of a biological aspect of genomes and forms a delay tolerant network with existing network models. This allows us to provide continuous and robust connectivity. We then demonstrate that the proposed network model has an efficient data delivery, lower overheads and lesser delays with high convergence rate in comparison to existing approaches, based on evaluations in both real-time testbed and simulation environment.

GDTN: Genome-Based Delay Tolerant Network Formation in Heterogeneous 5G Using Inter-UA Collaboration

PubMed Central

2016-01-01

With a more Internet-savvy and sophisticated user base, there are more demands for interactive applications and services. However, it is a challenge for existing radio access networks (e.g. 3G and 4G) to cope with the increasingly demanding requirements such as higher data rates and wider coverage area. One potential solution is the inter-collaborative deployment of multiple radio devices in a 5G setting designed to meet exacting user demands, and facilitate the high data rate requirements in the underlying networks. These heterogeneous 5G networks can readily resolve the data rate and coverage challenges. Networks established using the hybridization of existing networks have diverse military and civilian applications. However, there are inherent limitations in such networks such as irregular breakdown, node failures, and halts during speed transmissions. In recent years, there have been attempts to integrate heterogeneous 5G networks with existing ad hoc networks to provide a robust solution for delay-tolerant transmissions in the form of packet switched networks. However, continuous connectivity is still required in these networks, in order to efficiently regulate the flow to allow the formation of a robust network. Therefore, in this paper, we present a novel network formation consisting of nodes from different network maneuvered by Unmanned Aircraft (UA). The proposed model utilizes the features of a biological aspect of genomes and forms a delay tolerant network with existing network models. This allows us to provide continuous and robust connectivity. We then demonstrate that the proposed network model has an efficient data delivery, lower overheads and lesser delays with high convergence rate in comparison to existing approaches, based on evaluations in both real-time testbed and simulation environment. PMID:27973618

A hybrid MAC protocol design for energy-efficient very-high-throughput millimeter wave, wireless sensor communication networks

NASA Astrophysics Data System (ADS)

Jian, Wei; Estevez, Claudio; Chowdhury, Arshad; Jia, Zhensheng; Wang, Jianxin; Yu, Jianguo; Chang, Gee-Kung

2010-12-01

This paper presents an energy-efficient Medium Access Control (MAC) protocol for very-high-throughput millimeter-wave (mm-wave) wireless sensor communication networks (VHT-MSCNs) based on hybrid multiple access techniques of frequency division multiplexing access (FDMA) and time division multiplexing access (TDMA). An energy-efficient Superframe for wireless sensor communication network employing directional mm-wave wireless access technologies is proposed for systems that require very high throughput, such as high definition video signals, for sensing, processing, transmitting, and actuating functions. Energy consumption modeling for each network element and comparisons among various multi-access technologies in term of power and MAC layer operations are investigated for evaluating the energy-efficient improvement of proposed MAC protocol.

A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

PubMed Central

Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin

2016-01-01

Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay. PMID:27618044

Identification of Crowding Stress Tolerance Co-Expression Networks Involved in Sweet Corn Yield

PubMed Central

Choe, Eunsoo; Drnevich, Jenny; Williams, Martin M.

2016-01-01

Tolerance to crowding stress has played a crucial role in improving agronomic productivity in field corn; however, commercial sweet corn hybrids vary greatly in crowding stress tolerance. The objectives were to 1) explore transcriptional changes among sweet corn hybrids with differential yield under crowding stress, 2) identify relationships between phenotypic responses and gene expression patterns, and 3) identify groups of genes associated with yield and crowding stress tolerance. Under conditions of crowding stress, three high-yielding and three low-yielding sweet corn hybrids were grouped for transcriptional and phenotypic analyses. Transcriptional analyses identified from 372 to 859 common differentially expressed genes (DEGs) for each hybrid. Large gene expression pattern variation among hybrids and only 26 common DEGs across all hybrid comparisons were identified, suggesting each hybrid has a unique response to crowding stress. Over-represented biological functions of DEGs also differed among hybrids. Strong correlation was observed between: 1) modules with up-regulation in high-yielding hybrids and yield traits, and 2) modules with up-regulation in low-yielding hybrids and plant/ear traits. Modules linked with yield traits may be important crowding stress response mechanisms influencing crop yield. Functional analysis of the modules and common DEGs identified candidate crowding stress tolerant processes in photosynthesis, glycolysis, cell wall, carbohydrate/nitrogen metabolic process, chromatin, and transcription regulation. Moreover, these biological functions were greatly inter-connected, indicating the importance of improving the mechanisms as a network. PMID:26796516

Innovative Networking Concepts Tested on the Advanced Communications Technology Satellite

NASA Technical Reports Server (NTRS)

Friedman, Daniel; Gupta, Sonjai; Zhang, Chuanguo; Ephremides, Anthony

1996-01-01

This paper describes a program of experiments conducted over the advanced communications technology satellite (ACTS) and the associated TI-VSAT (very small aperture terminal). The experiments were motivated by the commercial potential of low-cost receive only satellite terminals that can operate in a hybrid network environment, and by the desire to demonstrate frame relay technology over satellite networks. The first experiment tested highly adaptive methods of satellite bandwidth allocation in an integrated voice-data service environment. The second involved comparison of forward error correction (FEC) and automatic repeat request (ARQ) methods of error control for satellite communication with emphasis on the advantage that a hybrid architecture provides, especially in the case of multicasts. Finally, the third experiment demonstrated hybrid access to databases and compared the performance of internetworking protocols for interconnecting local area networks (LANs) via satellite. A custom unit termed frame relay access switch (FRACS) was developed by COMSAT Laboratories for these experiments; the preparation and conduct of these experiments involved a total of 20 people from the University of Maryland, the University of Colorado and COMSAT Laboratories, from late 1992 until 1995.

A hybrid neural network model for noisy data regression.

PubMed

Lee, Eric W M; Lim, Chee Peng; Yuen, Richard K K; Lo, S M

2004-04-01

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

Hybrid Optimization in Urban Traffic Networks

DOT National Transportation Integrated Search

1979-04-01

The hybrid optimization problem is formulated to provide a general theoretical framework for the analysis of a class of traffic control problems which takes into account the role of individual drivers as independent decisionmakers. Different behavior...

Optimal Performance Monitoring of Hybrid Mid-Infrared Wavelength MIMO Free Space Optical and RF Wireless Networks in Fading Channels

NASA Astrophysics Data System (ADS)

Schmidt, Barnet Michael

An optimal performance monitoring metric for a hybrid free space optical and radio-frequency (RF) wireless network, the Outage Capacity Objective Function, is analytically developed and studied. Current and traditional methods of performance monitoring of both optical and RF wireless networks are centered on measurement of physical layer parameters, the most common being signal-to-noise ratio, error rate, Q factor, and eye diagrams, occasionally combined with link-layer measurements such as data throughput, retransmission rate, and/or lost packet rate. Network management systems frequently attempt to predict or forestall network failures by observing degradations of these parameters and to attempt mitigation (such as offloading traffic, increasing transmitter power, reducing the data rate, or combinations thereof) prior to the failure. These methods are limited by the frequent low sensitivity of the physical layer parameters to the atmospheric optical conditions (measured by optical signal-to-noise ratio) and the radio frequency fading channel conditions (measured by signal-to-interference ratio). As a result of low sensitivity, measurements of this type frequently are unable to predict impending failures sufficiently in advance for the network management system to take corrective action prior to the failure. We derive and apply an optimal measure of hybrid network performance based on the outage capacity of the hybrid optical and RF channel, the outage capacity objective function. The objective function provides high sensitivity and reliable failure prediction, and considers both the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The radio frequency segment analysis considers the three most common RF channel fading statistics: Rayleigh, Ricean, and Nakagami-m. The novel application of information theory to the underlying physics of the gamma-gamma optical channel and radio fading channels in determining the joint hybrid channel outage capacity provides the best performance estimate under any given set of operating conditions. It is shown that, unlike traditional physical layer performance monitoring techniques, the objective function based upon the outage capacity of the hybrid channel at any combination of OSNR and SIR, is able to predict channel degradation and failure well in advance of the actual outage. An outage in the information-theoretic definition occurs when the offered load exceeds the outage capacity under the current conditions of OSNR and SIR. The optical channel is operated at the "long" mid-infrared wavelength of 10000 nm. which provides improved resistance to scattering compared to shorter wavelengths such as 1550 nm.

Paired-pulse facilitation achieved in protonic/electronic hybrid indium gallium zinc oxide synaptic transistors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Li Qiang, E-mail: guoliqiang@ujs.edu.cn; Ding, Jian Ning; Huang, Yu Kai

2015-08-15

Neuromorphic devices with paired pulse facilitation emulating that of biological synapses are the key to develop artificial neural networks. Here, phosphorus-doped nanogranular SiO{sub 2} electrolyte is used as gate dielectric for protonic/electronic hybrid indium gallium zinc oxide (IGZO) synaptic transistor. In such synaptic transistors, protons within the SiO{sub 2} electrolyte are deemed as neurotransmitters of biological synapses. Paired-pulse facilitation (PPF) behaviors for the analogous information were mimicked. The temperature dependent PPF behaviors were also investigated systematically. The results indicate that the protonic/electronic hybrid IGZO synaptic transistors would be promising candidates for inorganic synapses in artificial neural network applications.

High accuracy demodulation for twin-grating based sensor network with hybrid TDM/FDM

NASA Astrophysics Data System (ADS)

Ai, Fan; Sun, Qizhen; Cheng, Jianwei; Luo, Yiyang; Yan, Zhijun; Liu, Deming

2017-04-01

We demonstrate a high accuracy demodulation platform with a tunable Fabry-Perot filter (TFF) for twin-grating based fiber optic sensing network with hybrid TDM/FDM. The hybrid TDM/FDM scheme can improve the spatial resolution to centimeter but increases the requirement of high spectrum resolution. To realize the demodulation of the complex twin-grating spectrum, we adopt the TFF demodulation method and compensate the environmental temperature change and nonlinear effect through calibration FBGs. The performance of the demodulation module is tested by a temperature experiment. Spectrum resolution of 1pm is realized with precision of 2.5pm while the environmental temperature of TFF changes 9.3°C.

A Neural Relevance Model for Feature Extraction from Hyperspectral Images, and Its Application in the Wavelet Domain

DTIC Science & Technology

2006-08-01

Nikolas Avouris. Evaluation of classifiers for an uneven class distribution problem. Applied Artificial Intellegence , pages 1-24, 2006. Draft manuscript...data by a hybrid artificial neural network so we may evaluate the classification capabilities of the baseline GRLVQ and our improved GRLVQI. Chapter 4...performance of GRLVQ(I), we compare the results against a baseline classification of the 23-class problem with a hybrid artificial neural network (ANN

Proceedings of the Fourth International Mobile Satellite Conference (IMSC 1995)

NASA Technical Reports Server (NTRS)

Rigley, Jack R. (Compiler); Estabrook, Polly (Compiler); Reekie, D. Hugh M. (Editor)

1995-01-01

The theme to the 1995 International Mobile Satellite Conference was 'Mobile Satcom Comes of Age'. The sessions included Modulation, Coding, and Multiple Access; Hybrid Networks - 1; Spacecraft Technology; propagation; Applications and Experiments - 1; Advanced System Concepts and Analysis; Aeronautical Mobile Satellite Communications; Mobile Terminal Antennas; Mobile Terminal Technology; Current and Planned Systems; Direct Broadcast Satellite; The Use of CDMA for LEO and ICO Mobile Satellite Systems; Hybrid Networks - 2; and Applications and Experiments - 2.

Carbon nanotube-templated assembly of regioregular poly(3-alkylthiophene) in solution

NASA Astrophysics Data System (ADS)

Zhu, Jiahua; Stevens, Eric; He, Youjun; Hong, Kunlun; Ivanov, Ilia

2016-09-01

Control of structural heterogeneity by rationally encoding of the molecular assemblies is a key enabling design of hierarchical, multifunctional materials of the future. Here we report the strategies to gain such control using solution- based assembly to construct a hybrid nano-assembly and a network hybrid structure of regioregular poly(3- alkylthiophene) - carbon nanotube (P3AT-CNT). The opto-electronic performance of conjugated polymer (P3AT) is defined by the structure of the aggregate in solution and in the solid film. Control of P3AT aggregation would allow formation of broad range of morphologies with very distinct electro-optical. We utilize interactive templating to confine the assembly behavior of conjugated polymers, replacing poorly controlled solution processing approach. Perfect crystalline surface of the single-walled and multi-walled carbon nanotube (SWCNT/MWCNT) acts as a template, seeding P3AT aggregation of the surface of the nanotube. The seed continues directional growth through pi-pi stacking leading to the formation of to well-defined P3AT-CNT morphologies, including comb-like nano-assemblies, super- structures and gel networks. Interconnected, highly-branched network structure of P3AT-CNT hybrids is of particular interest to enable efficient, long-range, balanced charge carrier transport. The structure and opto-electionic function of the intermediate assemblies and networks of P3AT/CNT hybrids are characterized by transmission election microscopy and UV-vis absorption.

Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation

NASA Astrophysics Data System (ADS)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

Deep learning networks are gaining popularity in many medical image analysis tasks due to their generalized ability to automatically extract relevant features from raw images. However, this can make the learning problem unnecessarily harder requiring network architectures of high complexity. In case of anomaly detection, in particular, there is often sufficient regional difference between the anomaly and the surrounding parenchyma that could be easily highlighted through bottom-up saliency operators. In this paper we propose a new hybrid deep learning network using a combination of raw image and such regional maps to more accurately learn the anomalies using simpler network architectures. Specifically, we modify a deep learning network called U-Net using both the raw and pre-segmented images as input to produce joint encoding (contraction) and expansion paths (decoding) in the U-Net. We present results of successfully delineating subdural and epidural hematomas in brain CT imaging and liver hemangioma in abdominal CT images using such network.

Design and simulation of programmable relational optoelectronic time-pulse coded processors as base elements for sorting neural networks

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Lazareva, Maria V.

2010-05-01

In the paper we show that the biologically motivated conception of time-pulse encoding usage gives a set of advantages (single methodological basis, universality, tuning simplicity, learning and programming et al) at creation and design of sensor systems with parallel input-output and processing for 2D structures hybrid and next generations neuro-fuzzy neurocomputers. We show design principles of programmable relational optoelectronic time-pulse encoded processors on the base of continuous logic, order logic and temporal waves processes. We consider a structure that execute analog signal extraction, analog and time-pulse coded variables sorting. We offer optoelectronic realization of such base relational order logic element, that consists of time-pulse coded photoconverters (pulse-width and pulse-phase modulators) with direct and complementary outputs, sorting network on logical elements and programmable commutation blocks. We make technical parameters estimations of devices and processors on such base elements by simulation and experimental research: optical input signals power 0.2 - 20 uW, processing time 1 - 10 us, supply voltage 1 - 3 V, consumption power 10 - 100 uW, extended functional possibilities, learning possibilities. We discuss some aspects of possible rules and principles of learning and programmable tuning on required function, relational operation and realization of hardware blocks for modifications of such processors. We show that it is possible to create sorting machines, neural networks and hybrid data-processing systems with untraditional numerical systems and pictures operands on the basis of such quasiuniversal hardware simple blocks with flexible programmable tuning.

Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data.

PubMed

Nagasaki, Masao; Yamaguchi, Rui; Yoshida, Ryo; Imoto, Seiya; Doi, Atsushi; Tamada, Yoshinori; Matsuno, Hiroshi; Miyano, Satoru; Higuchi, Tomoyuki

2006-01-01

We propose an automatic construction method of the hybrid functional Petri net as a simulation model of biological pathways. The problems we consider are how we choose the values of parameters and how we set the network structure. Usually, we tune these unknown factors empirically so that the simulation results are consistent with biological knowledge. Obviously, this approach has the limitation in the size of network of interest. To extend the capability of the simulation model, we propose the use of data assimilation approach that was originally established in the field of geophysical simulation science. We provide genomic data assimilation framework that establishes a link between our simulation model and observed data like microarray gene expression data by using a nonlinear state space model. A key idea of our genomic data assimilation is that the unknown parameters in simulation model are converted as the parameter of the state space model and the estimates are obtained as the maximum a posteriori estimators. In the parameter estimation process, the simulation model is used to generate the system model in the state space model. Such a formulation enables us to handle both the model construction and the parameter tuning within a framework of the Bayesian statistical inferences. In particular, the Bayesian approach provides us a way of controlling overfitting during the parameter estimations that is essential for constructing a reliable biological pathway. We demonstrate the effectiveness of our approach using synthetic data. As a result, parameter estimation using genomic data assimilation works very well and the network structure is suitably selected.

Highly efficient hydrogen evolution based on Ni3S4@MoS2 hybrids supported on N-doped reduced graphene oxide

NASA Astrophysics Data System (ADS)

Xu, Xiaobing; Zhong, Wei; Wu, Liqian; Sun, Yuan; Wang, Tingting; Wang, Yuanqi; Du, Youwei

2018-01-01

Hydrogen evolution reaction (HER) through water splitting at low overpotential is an appealing technology to produce renewable energy, wherein the design of stable electrocatalysts is very critical. To achieve optimal electrochemical performance, a highly efficient and stable noble-metal-free HER catalyst is synthesized by means of a facile hydrothermal co-synthesis. It consists of Ni3S4 nanosheets and MoS2 nanolayers supported on N-doped reduced graphene oxide (Ni3S4/MoS2@N-rGO). The optimized sample provides a large amount of active sites that benefit electron transfer in 3D conductive networks. Thanks to the strong synergistic effect in the catalyst network, we achieved a low overpotential of 94 mV, a small Tafel slope of 56 mV/dec and remarkable durability in an acidic medium.

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

PubMed

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Fault Tolerant Real-Time Networks

DTIC Science & Technology

2007-05-30

Alberto Sangiovanni-Vincentelli, editors Hybrid Systems: Computation and Control. Fourth International Workshop (HSCC󈧅, Rome, Italy, March 2001...average dwell time by solving optimization problems. In Ashish Tiwari and Joao P. Hespanha, editors, Hybrid Systems: Computation and Control (HSCC 06

Hybrid Network Defense Model Based on Fuzzy Evaluation

PubMed Central

2014-01-01

With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture. PMID:24574870

Hybrid architecture for building secure sensor networks

NASA Astrophysics Data System (ADS)

Owens, Ken R., Jr.; Watkins, Steve E.

2012-04-01

Sensor networks have various communication and security architectural concerns. Three approaches are defined to address these concerns for sensor networks. The first area is the utilization of new computing architectures that leverage embedded virtualization software on the sensor. Deploying a small, embedded virtualization operating system on the sensor nodes that is designed to communicate to low-cost cloud computing infrastructure in the network is the foundation to delivering low-cost, secure sensor networks. The second area focuses on securing the sensor. Sensor security components include developing an identification scheme, and leveraging authentication algorithms and protocols that address security assurance within the physical, communication network, and application layers. This function will primarily be accomplished through encrypting the communication channel and integrating sensor network firewall and intrusion detection/prevention components to the sensor network architecture. Hence, sensor networks will be able to maintain high levels of security. The third area addresses the real-time and high priority nature of the data that sensor networks collect. This function requires that a quality-of-service (QoS) definition and algorithm be developed for delivering the right data at the right time. A hybrid architecture is proposed that combines software and hardware features to handle network traffic with diverse QoS requirements.

Formation of Helically Structured Chitin/CaCO3 Hybrids through an Approach Inspired by the Biomineralization Processes of Crustacean Cuticles.

PubMed

Matsumura, Shunichi; Kajiyama, Satoshi; Nishimura, Tatsuya; Kato, Takashi

2015-10-01

Chitin/CaCO3 hybrids with helical structures are formed through a biomineralization-inspired crystallization process under ambient conditions. Liquid-crystalline chitin whiskers are used as helically ordered templates. The liquid-crystalline structures are stabilized by acidic polymer networks which interact with the chitin templates. The crystallization of CaCO3 is conducted by soaking the templates in the colloidal suspension of amorphous CaCO3 (ACC) at room temperature. At the initial stage of crystallization, ACC particles are introduced inside the templates, and they crystallize to CaCO3 nanocrystals. The acidic polymer networks induce CaCO3 crystallization. The characterization of the resultant hybrids reveals that they possess helical order and homogeneous hybrid structures of chitin and CaCO3 , which resemble the structure and composition of the exoskeleton of crustaceans. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

NASA Astrophysics Data System (ADS)

Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad

2013-06-01

This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

A hybrid optical switch architecture to integrate IP into optical networks to provide flexible and intelligent bandwidth on demand for cloud computing

NASA Astrophysics Data System (ADS)

Yang, Wei; Hall, Trevor J.

2013-12-01

The Internet is entering an era of cloud computing to provide more cost effective, eco-friendly and reliable services to consumer and business users. As a consequence, the nature of the Internet traffic has been fundamentally transformed from a pure packet-based pattern to today's predominantly flow-based pattern. Cloud computing has also brought about an unprecedented growth in the Internet traffic. In this paper, a hybrid optical switch architecture is presented to deal with the flow-based Internet traffic, aiming to offer flexible and intelligent bandwidth on demand to improve fiber capacity utilization. The hybrid optical switch is capable of integrating IP into optical networks for cloud-based traffic with predictable performance, for which the delay performance of the electronic module in the hybrid optical switch architecture is evaluated through simulation.

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

In-situ immobilization of quantum dots in polysaccharide-based nanogels for integration of optical pH-sensing, tumor cell imaging, and drug delivery.

PubMed

Wu, Weitai; Aiello, Michael; Zhou, Ting; Berliner, Alexandra; Banerjee, Probal; Zhou, Shuiqin

2010-04-01

We report a class of polysaccharide-based hybrid nanogels that can integrate the functional building blocks for optical pH-sensing, cancer cell imaging, and controlled drug release into a single nanoparticle system, which can offer broad opportunities for combined diagnosis and therapy. The hybrid nanogels were prepared by in-situ immobilization of CdSe quantum dots (QDs) in the interior of the pH and temperature dual responsive hydroxypropylcellulose-poly(acrylic acid) (HPC-PAA) semi-interpenetrating polymer networks. The-OH groups of the HPC chains are designed to sequester the precursor Cd(2+) ions into the nanogels as well as stabilize the in-situ formed CdSe QDs. The pH-sensitive PAA network chains are designed to induce a pH-responsive volume phase transition of the hybrid nanogels. The developed HPC-PAA-CdSe hybrid nanogels combine a strong trap emission at 741nm for sensing physicochemical environment in a pH dependent manner and a visible excitonic emission at 592nm for mouse melanoma B16F10 cell imaging. The hybrid nanogels also provide excellent stability as a drug carrier, which cannot only provide a high drug loading capacity for a model anticancer drug temozolomide, but also offer a pH-triggered sustained-release of the drug molecules in the gel network. Copyright 2010 Elsevier Ltd. All rights reserved.

Intelligent routing protocol for ad hoc wireless network

NASA Astrophysics Data System (ADS)

Peng, Chaorong; Chen, Chang Wen

2006-05-01

A novel routing scheme for mobile ad hoc networks (MANETs), which combines hybrid and multi-inter-routing path properties with a distributed topology discovery route mechanism using control agents is proposed in this paper. In recent years, a variety of hybrid routing protocols for Mobile Ad hoc wireless networks (MANETs) have been developed. Which is proactively maintains routing information for a local neighborhood, while reactively acquiring routes to destinations beyond the global. The hybrid protocol reduces routing discovery latency and the end-to-end delay by providing high connectivity without requiring much of the scarce network capacity. On the other side the hybrid routing protocols in MANETs likes Zone Routing Protocol still need route "re-discover" time when a route between zones link break. Sine the topology update information needs to be broadcast routing request on local zone. Due to this delay, the routing protocol may not be applicable for real-time data and multimedia communication. We utilize the advantages of a clustering organization and multi-routing path in routing protocol to achieve several goals at the same time. Firstly, IRP efficiently saves network bandwidth and reduces route reconstruction time when a routing path fails. The IRP protocol does not require global periodic routing advertisements, local control agents will automatically monitor and repair broke links. Secondly, it efficiently reduces congestion and traffic "bottlenecks" for ClusterHeads in clustering network. Thirdly, it reduces significant overheads associated with maintaining clusters. Fourthly, it improves clusters stability due to dynamic topology changing frequently. In this paper, we present the Intelligent Routing Protocol. First, we discuss the problem of routing in ad hoc networks and the motivation of IRP. We describe the hierarchical architecture of IRP. We describe the routing process and illustrate it with an example. Further, we describe the control manage mechanisms, which are used to control active route and reduce the traffic amount in the route discovery procedure. Finial, the numerical experiments are given to show the effectiveness of IRP routing protocol.

Hybrid metal organic scintillator materials system and particle detector

DOEpatents

Bauer, Christina A.; Allendorf, Mark D.; Doty, F. Patrick; Simmons, Blake A.

2011-07-26

We describe the preparation and characterization of two zinc hybrid luminescent structures based on the flexible and emissive linker molecule, trans-(4-R,4'-R') stilbene, where R and R' are mono- or poly-coordinating groups, which retain their luminescence within these solid materials. For example, reaction of trans-4,4'-stilbenedicarboxylic acid and zinc nitrate in the solvent dimethylformamide (DMF) yielded a dense 2-D network featuring zinc in both octahedral and tetrahedral coordination environments connected by trans-stilbene links. Similar reaction in diethylformamide (DEF) at higher temperatures resulted in a porous, 3-D framework structure consisting of two interpenetrating cubic lattices, each featuring basic to zinc carboxylate vertices joined by trans-stilbene, analogous to the isoreticular MOF (IRMOF) series. We demonstrate that the optical properties of both embodiments correlate directly with the local ligand environments observed in the crystal structures. We further demonstrate that these materials produce high luminescent response to proton radiation and high radiation tolerance relative to prior scintillators. These features can be used to create sophisticated scintillating detection sensors.

Evaluation of tensile strength of hybrid fiber (jute/gongura) reinforced hybrid polymer matrix composites

NASA Astrophysics Data System (ADS)

Venkatachalam, G.; Gautham Shankar, A.; Vijay, Kumar V.; Chandan, Byral R.; Prabaharan, G. P.; Raghav, Dasarath

2015-07-01

The polymer matrix composites attract many industrial applications due to its light weight, less cost and easy for manufacturing. In this paper, an attempt is made to prepare and study of the tensile strength of hybrid (two natural) fibers reinforced hybrid (Natural + Synthetic) polymer matrix composites. The samples were prepared with hybrid reinforcement consists of two different fibers such as jute and Gongura and hybrid polymer consists of polyester and cashew nut shell resins. The hybrid composites tensile strength is evaluated to study the influence of various fiber parameters on mechanical strength. The parameters considered here are the duration of fiber treatment, the concentration of alkali in fiber treatment and nature of fiber content in the composites.

Comparative Transcriptional Profiling and Preliminary Study on Heterosis Mechanism of Super-Hybrid Rice

PubMed Central

Song, Gui-Sheng; Zhai, Hong-Li; Peng, Yong-Gang; Zhang, Lei; Wei, Gang; Chen, Xiao-Ying; Xiao, Yu-Guo; Wang, Lili; Wu, Bin; Zhang, Yu; Feng, Xiu-Jing; Gong, Wan-Kui; Liu, Yao; Yin, Zhi-Jie; Wang, Feng; Liu, Guo-Zhen; Xu, Hong-Lin; Wei, Xiao-Li; Zhao, Xiao-Ling; Ouwerkerk, Pieter B.F.; Hankemeier, Thomas; Reijmers, Theo; van der Heijden, Rob; Wang, Mei; van der Greef, Jan; Zhu, Zhen

2010-01-01

Heterosis is a biological phenomenon whereby the offspring from two parents show improved and superior performance than either inbred parental lines. Hybrid rice is one of the most successful apotheoses in crops utilizing heterosis. Transcriptional profiling of F1 super-hybrid rice Liangyou-2186 and its parents by serial analysis of gene expression (SAGE) revealed 1183 differentially expressed genes (DGs), among which DGs were found significantly enriched in pathways such as photosynthesis and carbon-fixation, and most of the key genes involved in the carbon-fixation pathway exhibited up-regulated expression in F1 hybrid rice. Moreover, increased catabolic activity of corresponding enzymes and photosynthetic efficiency were also detected, which combined to indicate that carbon fixation is enhanced in F1 hybrid, and might probably be associated with the yield vigor and heterosis in super-hybrid rice. By correlating DGs with yield-related quantitative trait loci (QTL), a potential relationship between differential gene expression and phenotypic changes was also found. In addition, a regulatory network involving circadian-rhythms and light signaling pathways was also found, as previously reported in Arabidopsis, which suggest that such a network might also be related with heterosis in hybrid rice. Altogether, the present study provides another view for understanding the molecular mechanism underlying heterosis in rice. PMID:20729474

An Italian network to improve hybrid rocket performance: Strategy and results

NASA Astrophysics Data System (ADS)

Galfetti, L.; Nasuti, F.; Pastrone, D.; Russo, A. M.

2014-03-01

The new international attention to hybrid space propulsion points out the need of a deeper understanding of physico-chemical phenomena controlling combustion process and fluid dynamics inside the motor. This research project has been carried on by a network of four Italian Universities; each of them being responsible for a specific topic. The task of Politecnico di Milano is an experimental activity concerning the study, development, manufacturing and characterization of advanced hybrid solid fuels with a high regression rate. The University of Naples is responsible for experimental activities focused on rocket motor scale characterization of the solid fuels developed and characterized at laboratory scale by Politecnico di Milano. The University of Rome has been studying the combustion chamber and nozzle of the hybrid rocket, defined in the coordinated program by advanced physical-mathematical models and numerical methods. Politecnico di Torino has been working on a multidisciplinary optimization code for optimal design of hybrid rocket motors, strongly related to the mission to be performed. The overall research project aims to increase the scientific knowledge of the combustion processes in hybrid rockets, using a strongly linked experimental-numerical approach. Methods and obtained results will be applied to implement a potential upgrade for the current generation of hybrid rocket motors. This paper presents the overall strategy, the organization, and the first experimental and numerical results of this joined effort to contribute to the development of improved hybrid propulsion systems.

A Framework for Dynamic Constraint Reasoning Using Procedural Constraints

NASA Technical Reports Server (NTRS)

Jonsson, Ari K.; Frank, Jeremy D.

1999-01-01

Many complex real-world decision and control problems contain an underlying constraint reasoning problem. This is particularly evident in a recently developed approach to planning, where almost all planning decisions are represented by constrained variables. This translates a significant part of the planning problem into a constraint network whose consistency determines the validity of the plan candidate. Since higher-level choices about control actions can add or remove variables and constraints, the underlying constraint network is invariably highly dynamic. Arbitrary domain-dependent constraints may be added to the constraint network and the constraint reasoning mechanism must be able to handle such constraints effectively. Additionally, real problems often require handling constraints over continuous variables. These requirements present a number of significant challenges for a constraint reasoning mechanism. In this paper, we introduce a general framework for handling dynamic constraint networks with real-valued variables, by using procedures to represent and effectively reason about general constraints. The framework is based on a sound theoretical foundation, and can be proven to be sound and complete under well-defined conditions. Furthermore, the framework provides hybrid reasoning capabilities, as alternative solution methods like mathematical programming can be incorporated into the framework, in the form of procedures.

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.

PubMed

Yao, Kun; Herr, John E; Toth, David W; Mckintyre, Ryker; Parkhill, John

2018-02-28

Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near ab initio accuracy at low cost. However a data-driven approach is naturally inefficient for long-range interatomic forces that have simple physical formulas. In this manuscript we construct a hybrid model chemistry consisting of a nearsighted neural network potential with screened long-range electrostatic and van der Waals physics. This trained potential, simply dubbed "TensorMol-0.1", is offered in an open-source Python package capable of many of the simulation types commonly used to study chemistry: geometry optimizations, harmonic spectra, open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating its millihartree accuracy and scalability to tens-of-thousands of atoms on ordinary laptops. We demonstrate the performance of the model by reproducing vibrational spectra, and simulating the molecular dynamics of a protein. Our comparisons with electronic structure theory and experimental data demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulating chemistry.

Experimental damage detection of wind turbine blade using thin film sensor array

NASA Astrophysics Data System (ADS)

Downey, Austin; Laflamme, Simon; Ubertini, Filippo; Sarkar, Partha

2017-04-01

Damage detection of wind turbine blades is difficult due to their large sizes and complex geometries. Additionally, economic restraints limit the viability of high-cost monitoring methods. While it is possible to monitor certain global signatures through modal analysis, obtaining useful measurements over a blade's surface using off-the-shelf sensing technologies is difficult and typically not economical. A solution is to deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel large-area electronic sensor measuring strain over very large surfaces. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a hybrid dense sensor network of soft elastomeric capacitors to detect, localize, and quantify damage, and resistive strain gauges to augment such dense sensor network with high accuracy data at key locations. The proposed hybrid dense sensor network is installed inside a wind turbine blade model and tested in a wind tunnel to simulate an operational environment. Damage in the form of changing boundary conditions is introduced into the monitored section of the blade. Results demonstrate the ability of the hybrid dense sensor network, and associated algorithms, to detect, localize, and quantify damage.

Reconstruction of in-plane strain maps using hybrid dense sensor network composed of sensing skin

NASA Astrophysics Data System (ADS)

Downey, Austin; Laflamme, Simon; Ubertini, Filippo

2016-12-01

The authors have recently developed a soft-elastomeric capacitive (SEC)-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. Under plane stress conditions, the sensor output contains the additive measurement of the two principal strain components over the monitored surface. In applications where the evaluation of strain maps is useful, in structural health monitoring for instance, such signal must be decomposed into linear strain components along orthogonal directions. Previous work has led to an algorithm that enabled such decomposition by leveraging a dense sensor network configuration with the addition of assumed boundary conditions. Here, we significantly improve the algorithm’s accuracy by leveraging mature off-the-shelf solutions to create a hybrid dense sensor network (HDSN) to improve on the boundary condition assumptions. The system’s boundary conditions are enforced using unidirectional RSGs and assumed virtual sensors. Results from an extensive experimental investigation demonstrate the good performance of the proposed algorithm and its robustness with respect to sensors’ layout. Overall, the proposed algorithm is seen to effectively leverage the advantages of a hybrid dense network for application of the thin film sensor to reconstruct surface strain fields over large surfaces.

Metabolomic prediction of yield in hybrid rice.

PubMed

Xu, Shizhong; Xu, Yang; Gong, Liang; Zhang, Qifa

2016-10-01

Rice (Oryza sativa) provides a staple food source for more than 50% of the world's population. An increase in yield can significantly contribute to global food security. Hybrid breeding can potentially help to meet this goal because hybrid rice often shows a considerable increase in yield when compared with pure-bred cultivars. We recently developed a marker-guided prediction method for hybrid yield and showed a substantial increase in yield through genomic hybrid breeding. We now have transcriptomic and metabolomic data as potential resources for prediction. Using six prediction methods, including least absolute shrinkage and selection operator (LASSO), best linear unbiased prediction (BLUP), stochastic search variable selection, partial least squares, and support vector machines using the radial basis function and polynomial kernel function, we found that the predictability of hybrid yield can be further increased using these omic data. LASSO and BLUP are the most efficient methods for yield prediction. For high heritability traits, genomic data remain the most efficient predictors. When metabolomic data are used, the predictability of hybrid yield is almost doubled compared with genomic prediction. Of the 21 945 potential hybrids derived from 210 recombinant inbred lines, selection of the top 10 hybrids predicted from metabolites would lead to a ~30% increase in yield. We hypothesize that each metabolite represents a biologically built-in genetic network for yield; thus, using metabolites for prediction is equivalent to using information integrated from these hidden genetic networks for yield prediction. © 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.

Routing Algorithm based on Minimum Spanning Tree and Minimum Cost Flow for Hybrid Wireless-optical Broadband Access Network

NASA Astrophysics Data System (ADS)

Le, Zichun; Suo, Kaihua; Fu, Minglei; Jiang, Ling; Dong, Wen

2012-03-01

In order to minimize the average end to end delay for data transporting in hybrid wireless optical broadband access network, a novel routing algorithm named MSTMCF (minimum spanning tree and minimum cost flow) is devised. The routing problem is described as a minimum spanning tree and minimum cost flow model and corresponding algorithm procedures are given. To verify the effectiveness of MSTMCF algorithm, extensively simulations based on OWNS have been done under different types of traffic source.

Hybrid aerogel-derived Sn-Ni alloy immobilized within porous carbon/graphene dual matrices for high-performance lithium storage.

PubMed

Zhang, Hao; Zhang, Mengru; Zhang, Meiling; Zhang, Lin; Zhang, Anping; Zhou, Yiming; Wu, Ping; Tang, Yawen

2017-09-01

Nanoporous networks of tin-based alloys immobilized within carbon matrices possess unique structural and compositional superiorities toward lithium-storage, and are expected to manifest improved strain-accommodation and charge-transport capabilities and thus desirable anodic performance for advanced lithium-ion batteries (LIBs). Herein, a facile and scalable hybrid aerogel-derived thermal-autoreduction route has been developed for the construction of nanoporous network of SnNi alloy immobilized within carbon/graphene dual matrices (SnNi@C/G network). When applied as an anode material for LIBs, the SnNi@C/G network manifests desirable lithium-storage performances in terms of specific capacities, cycle life, and rate capability. The facile aerogel-derived route and desirable Li-storage performance of the SnNi@C/G network facilitate its practical application as a high-capacity, long-life, and high-rate anode material for advanced LIBs. Copyright © 2017 Elsevier Inc. All rights reserved.

Metropolitian area network services comprised of virtual local area networks running over hybrid fiber-coax and asynchronous transfer mode technologies

NASA Astrophysics Data System (ADS)

Biedron, William S.

1995-11-01

Since 1990 there has been a rapid increase in the demand for communication services, especially local and wide area network (LAN/WAN) oriented services. With the introduction of the DFB laser transmitter, hybrid-fiber-coax (HFC) cable plant designs, ATM transport technologies and rf modems, new LAN/WAN services can now be defined and marketed to residential and business customers over existing cable TV systems. The term metropolitan area network (MAN) can be used to describe this overall network. This paper discusses the technical components needed to provision these services as well as provides some perspectives on integration issues. Architecture at the headend and in the backbone is discussed, as well as specific service definitions and the technology issues associated with each. The TCP/IP protocol is suggested as a primary protocol to be used throughout the MAN.

RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

NASA Astrophysics Data System (ADS)

Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

2016-02-01

Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

Organic hybrid planar-nanocrystalline bulk heterojunctions

DOEpatents

Forrest, Stephen R [Ann Arbor, MI; Yang, Fan [Piscataway, NJ

2011-03-01

A photosensitive optoelectronic device having an improved hybrid planar bulk heterojunction includes a plurality of photoconductive materials disposed between the anode and the cathode. The photoconductive materials include a first continuous layer of donor material and a second continuous layer of acceptor material. A first network of donor material or materials extends from the first continuous layer toward the second continuous layer, providing continuous pathways for conduction of holes to the first continuous layer. A second network of acceptor material or materials extends from the second continuous layer toward the first continuous layer, providing continuous pathways for conduction of electrons to the second continuous layer. The first network and the second network are interlaced with each other. At least one other photoconductive material is interspersed between the interlaced networks. This other photoconductive material or materials has an absorption spectra different from the donor and acceptor materials.

Organic hybrid planar-nanocrystalline bulk heterojunctions

DOEpatents

Forrest, Stephen R.; Yang, Fan

2013-04-09

A photosensitive optoelectronic device having an improved hybrid planar bulk heterojunction includes a plurality of photoconductive materials disposed between the anode and the cathode. The photoconductive materials include a first continuous layer of donor material and a second continuous layer of acceptor material. A first network of donor material or materials extends from the first continuous layer toward the second continuous layer, providing continuous pathways for conduction of holes to the first continuous layer. A second network of acceptor material or materials extends from the second continuous layer toward the first continuous layer, providing continuous pathways for conduction of electrons to the second continuous layer. The first network and the second network are interlaced with each other. At least one other photoconductive material is interspersed between the interlaced networks. This other photoconductive material or materials has an absorption spectra different from the donor and acceptor materials.

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

PubMed

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

PubMed Central

Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

A hybrid adaptive routing algorithm for event-driven wireless sensor networks.

PubMed

Figueiredo, Carlos M S; Nakamura, Eduardo F; Loureiro, Antonio A F

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption.

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

NASA Technical Reports Server (NTRS)

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.

Linear and Nonlinear Elasticity of Networks Made of Comb-like Polymers and Bottle-Brushes

NASA Astrophysics Data System (ADS)

Liang, H.; Dobrynin, A.; Everhart, M.; Daniel, W.; Vatankhah-Varnoosfaderani, M.; Sheiko, S.

We study mechanical properties of networks made of combs and bottle-brushes by computer simulations, theoretical calculations and experimental techniques. The networks are prepared by cross-linking backbones of combs or bottle-brushes with linear chains. This results in ``hybrid'' networks consisting of linear chains and strands of combs or bottle-brushes. In the framework of the phantom network model, the network modulus at small deformations G0 can be represented as a sum of contributions from linear chains, G0 , l, and strands of comb or bottle-brush, G0 , bb. If the length of extended backbone between crosslinks, Rmax, is much longer than the Kuhn length, bk, the modulus scales with the degree of polymerization of the side chains, nsc, and number of monomers between side chains, ng, as G0 , bb (nsc/ng + 1)-1. In the limit when bk becomes of the order of Rmax, the combs and bottle-brushes can be considered as semiflexible chains, resulting in a network modulus to be G0 , bb (nsc/ng + 1)-1(nsc2/2/ng) . In the nonlinear deformation regime, the strain-hardening behavior is described by the nonlinear network deformation model, which predicts that the true stress is a universal function of the structural modulus, G, first strain invariant, I1, and deformation ratio, β. The results of the computer simulations and predictions of the theoretical model are in a good agreement with experimental results. NSF DMR-1409710, DMR-1407645, DMR-1624569, DMR-1436201.

Analysis of typical fault-tolerant architectures using HARP

NASA Technical Reports Server (NTRS)

Bavuso, Salvatore J.; Bechta Dugan, Joanne; Trivedi, Kishor S.; Rothmann, Elizabeth M.; Smith, W. Earl

1987-01-01

Difficulties encountered in the modeling of fault-tolerant systems are discussed. The Hybrid Automated Reliability Predictor (HARP) approach to modeling fault-tolerant systems is described. The HARP is written in FORTRAN, consists of nearly 30,000 lines of codes and comments, and is based on behavioral decomposition. Using the behavioral decomposition, the dependability model is divided into fault-occurrence/repair and fault/error-handling models; the characteristics and combining of these two models are examined. Examples in which the HARP is applied to the modeling of some typical fault-tolerant systems, including a local-area network, two fault-tolerant computer systems, and a flight control system, are presented.

A neural networks-based hybrid routing protocol for wireless mesh networks.

PubMed

Kojić, Nenad; Reljin, Irini; Reljin, Branimir

2012-01-01

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

PubMed Central

Kojić, Nenad; Reljin, Irini; Reljin, Branimir

2012-01-01

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

Inter-allotropic transformations in the heterogeneous carbon nanotube networks.

PubMed

Jung, Hyun Young; Jung, Sung Mi; Kim, Dong Won; Jung, Yung Joon

2017-01-19

The allotropic transformations of carbon provide an immense technological interest for tailoring the desired molecular structures in the scalable nanoelectronic devices. Herein, we explore the effects of morphology and geometric alignment of the nanotubes for the re-engineering of carbon bonds in the heterogeneous carbon nanotube (CNT) networks. By applying alternating voltage pulses and electrical forces, the single-walled CNTs in networks were predominantly transformed into other predetermined sp 2 carbon structures (multi-walled CNTs and multi-layered graphitic nanoribbons), showing a larger intensity in a coalescence-induced mode of Raman spectra with the increasing channel width. Moreover, the transformed networks have a newly discovered sp 2 -sp 3 hybrid nanostructures in accordance with the alignment. The sp 3 carbon structures at the small channel are controlled, such that they contain up to about 29.4% networks. This study provides a controllable method for specific types of inter-allotropic transformations/hybridizations, which opens up the further possibility for the engineering of nanocarbon allotropes in the robust large-scale network-based devices.

Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique

NASA Astrophysics Data System (ADS)

Arif, Sajjad; Tanwir Alam, Md; Ansari, Akhter H.; Bilal Naim Shaikh, Mohd; Arif Siddiqui, M.

2018-05-01

The tribological performance of aluminium hybrid composites reinforced with micro SiC (5 wt%) and nano zirconia (0, 3, 6 and 9 wt%) fabricated through powder metallurgy technique were investigated using statistical and artificial neural network (ANN) approach. The influence of zirconia reinforcement, sliding distance and applied load were analyzed with test based on full factorial design of experiments. Analysis of variance (ANOVA) was used to evaluate the percentage contribution of each process parameters on wear loss. ANOVA approach suggested that wear loss be mainly influenced by sliding distance followed by zirconia reinforcement and applied load. Further, a feed forward back propagation neural network was applied on input/output date for predicting and analyzing the wear behaviour of fabricated composite. A very close correlation between experimental and ANN output were achieved by implementing the model. Finally, ANN model was effectively used to find the influence of various control factors on wear behaviour of hybrid composites.

An inorganic/organic hybrid magnetic network as a colorimetric fluorescent nanosensor and its recognizing behavior toward Hg2+

NASA Astrophysics Data System (ADS)

Zeng, Xianfei; Xu, Yaohui; Chen, Xiumin; Ma, Wenhui; Zhou, Yang

2017-11-01

An inorganic/organic hybrid magnetic Fe3O4@SiO2 network functionalized with rhodamine derivatives was devised as a nanosensor for selective detection and removal of Hg2+ in this work. The inorganic/organic hybrid composites showed naked-eye color change in water/methanol media. The distinct color change on the surface of functionalized composite network was observed by separating and drying from aqueous solution after adsorbing Hg2+. The fluorescence spectra indicated that the functionalized nanosensor was highly sensitive and selective to Hg2+ in aqueous solution. Density functional theory (DFT) calculation was performed, which revealed a mechanism of fluorescence generated from Hg2+ induced desulfurization of rhodamine derivatives via forming new five-membered ring structure. The as-obtained composites not only had an excellent adsorption capability for Hg2+, but also showed a strong magnetic sensitivity, which allowed one to separate the functionalized magnetic nanocomposites from the solution.

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

PubMed

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-08-06

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

PubMed Central

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-01-01

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively. PMID:27509495

Time series forecasting using ERNN and QR based on Bayesian model averaging

NASA Astrophysics Data System (ADS)

Pwasong, Augustine; Sathasivam, Saratha

2017-08-01

The Bayesian model averaging technique is a multi-model combination technique. The technique was employed to amalgamate the Elman recurrent neural network (ERNN) technique with the quadratic regression (QR) technique. The amalgamation produced a hybrid technique known as the hybrid ERNN-QR technique. The potentials of forecasting with the hybrid technique are compared with the forecasting capabilities of individual techniques of ERNN and QR. The outcome revealed that the hybrid technique is superior to the individual techniques in the mean square error sense.

Hybrid computing using a neural network with dynamic external memory.

PubMed

Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

2016-10-27

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

Fibrous hybrid of graphene and sulfur nanocrystals for high-performance lithium-sulfur batteries.

PubMed

Zhou, Guangmin; Yin, Li-Chang; Wang, Da-Wei; Li, Lu; Pei, Songfeng; Gentle, Ian Ross; Li, Feng; Cheng, Hui-Ming

2013-06-25

Graphene-sulfur (G-S) hybrid materials with sulfur nanocrystals anchored on interconnected fibrous graphene are obtained by a facile one-pot strategy using a sulfur/carbon disulfide/alcohol mixed solution. The reduction of graphene oxide and the formation/binding of sulfur nanocrystals were integrated. The G-S hybrids exhibit a highly porous network structure constructed by fibrous graphene, many electrically conducting pathways, and easily tunable sulfur content, which can be cut and pressed into pellets to be directly used as lithium-sulfur battery cathodes without using a metal current-collector, binder, and conductive additive. The porous network and sulfur nanocrystals enable rapid ion transport and short Li(+) diffusion distance, the interconnected fibrous graphene provides highly conductive electron transport pathways, and the oxygen-containing (mainly hydroxyl/epoxide) groups show strong binding with polysulfides, preventing their dissolution into the electrolyte based on first-principles calculations. As a result, the G-S hybrids show a high capacity, an excellent high-rate performance, and a long life over 100 cycles. These results demonstrate the great potential of this unique hybrid structure as cathodes for high-performance lithium-sulfur batteries.

A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu

Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers. In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.

Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

ERIC Educational Resources Information Center

Ray, Loye Lynn

2014-01-01

The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

Energy efficient flexible hybrid wavelength division multiplexing-time division multiplexing passive optical network with pay as you grow deployment

NASA Astrophysics Data System (ADS)

Garg, Amit Kumar; Madavi, Amresh Ashok; Janyani, Vijay

2017-02-01

A flexible hybrid wavelength division multiplexing-time division multiplexing passive optical network architecture that allows dual rate signals to be sent at 1 and 10 Gbps to each optical networking unit depending upon the traffic load is proposed. The proposed design allows dynamic wavelength allocation with pay-as-you-grow deployment capability. This architecture is capable of providing up to 40 Gbps of equal data rates to all optical distribution networks (ODNs) and up to 70 Gbps of a asymmetrical data rate to the specific ODN. The proposed design handles broadcasting capability with simultaneous point-to-point transmission, which further reduces energy consumption. In this architecture, each module sends a wavelength to each ODN, thus making the architecture fully flexible; this flexibility allows network providers to use only required OLT components and switch off others. The design is also reliable to any module or TRx failure and provides services without any service disruption. Dynamic wavelength allocation and pay-as-you-grow deployment support network extensibility and bandwidth scalability to handle future generation access networks.

Comparing clinical automated, medical record, and hybrid data sources for diabetes quality measures.

PubMed

Kerr, Eve A; Smith, Dylan M; Hogan, Mary M; Krein, Sarah L; Pogach, Leonard; Hofer, Timothy P; Hayward, Rodney A

2002-10-01

Little is known about the relative reliability of medical record and clinical automated data, sources commonly used to assess diabetes quality of care. The agreement between diabetes quality measures constructed from clinical automated versus medical record data sources was compared, and the performance of hybrid measures derived from a combination of the two data sources was examined. Medical records were abstracted for 1,032 patients with diabetes who received care from 21 facilities in 4 Veterans Integrated Service Networks. Automated data were obtained from a central Veterans Health Administration diabetes registry containing information on laboratory tests and medication use. Success rates were higher for process measures derived from medical record data than from automated data, but no substantial differences among data sources were found for the intermediate outcome measures. Agreement for measures derived from the medical record compared with automated data was moderate for process measures but high for intermediate outcome measures. Hybrid measures yielded success rates similar to those of medical record-based measures but would have required about 50% fewer chart reviews. Agreement between medical record and automated data was generally high. Yet even in an integrated health care system with sophisticated information technology, automated data tended to underestimate the success rate in technical process measures for diabetes care and yielded different quartile performance rankings for facilities. Applying hybrid methodology yielded results consistent with the medical record but required less data to come from medical record reviews.

Hybridizing polypyrrole chains with laminated and two-dimensional Ti3C2Tx toward high-performance electromagnetic wave absorption

NASA Astrophysics Data System (ADS)

Tong, Yuan; He, Man; Zhou, Yuming; Zhong, Xi; Fan, Lidan; Huang, Tingyuan; Liao, Qiang; Wang, Yongjuan

2018-03-01

In this study, multilayer sandwich heterostructural Ti3C2Tx MXenes decorated with polypyrrole chains have been synthesized successfully via HF etching treatment and in-situ chemical oxidative polymerization approach. The hybrids were investigated as EM wave absorbers for the first time. It is found that the composites consisting of 25 wt% Ti3C2Tx/PPy hybrids in a paraffin matrix exhibit a minimum reflection loss of -49.2 dB (∼99.99% absorption) at the thickness of 3.2 mm and a maximum effective absorption bandwidth of 4.9 GHz (12.4-17.3 GHz) corresponding to an absorber thickness of 2.0 mm. Additionally, a broad effective absorption bandwidth of 13.7 GHz (4.3-18.0 GHz) can be reached up by adjusting the thickness from 1.5 to 5.0 mm. Furthermore, the highest effective absorption bandwidth of 5.7 GHz can be reached when the mass fraction is 15 wt%. The enhanced comprehensive electromagnetic wave absorption has close correlation with the well-designed heterogeneous multilayered microstructure, generated heterogeneous interfaces, conductive paths, surface functional groups, localized defects and synergistic effect between laminated Ti3C2Tx and conductive polypyrrole network, which significantly improve impedance matching and attenuation abilities. The superior absorbing performance together with strong absorption and broad bandwidth endows the Ti3C2Tx/PPy hybrids with the potential prospect to be advanced EM wave absorbers.

Model reduction for stochastic chemical systems with abundant species.

PubMed

Smith, Stephen; Cianci, Claudia; Grima, Ramon

2015-12-07

Biochemical processes typically involve many chemical species, some in abundance and some in low molecule numbers. We first identify the rate constant limits under which the concentrations of a given set of species will tend to infinity (the abundant species) while the concentrations of all other species remains constant (the non-abundant species). Subsequently, we prove that, in this limit, the fluctuations in the molecule numbers of non-abundant species are accurately described by a hybrid stochastic description consisting of a chemical master equation coupled to deterministic rate equations. This is a reduced description when compared to the conventional chemical master equation which describes the fluctuations in both abundant and non-abundant species. We show that the reduced master equation can be solved exactly for a number of biochemical networks involving gene expression and enzyme catalysis, whose conventional chemical master equation description is analytically impenetrable. We use the linear noise approximation to obtain approximate expressions for the difference between the variance of fluctuations in the non-abundant species as predicted by the hybrid approach and by the conventional chemical master equation. Furthermore, we show that surprisingly, irrespective of any separation in the mean molecule numbers of various species, the conventional and hybrid master equations exactly agree for a class of chemical systems.

Model reduction for stochastic chemical systems with abundant species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Stephen; Cianci, Claudia; Grima, Ramon

2015-12-07

Biochemical processes typically involve many chemical species, some in abundance and some in low molecule numbers. We first identify the rate constant limits under which the concentrations of a given set of species will tend to infinity (the abundant species) while the concentrations of all other species remains constant (the non-abundant species). Subsequently, we prove that, in this limit, the fluctuations in the molecule numbers of non-abundant species are accurately described by a hybrid stochastic description consisting of a chemical master equation coupled to deterministic rate equations. This is a reduced description when compared to the conventional chemical master equationmore » which describes the fluctuations in both abundant and non-abundant species. We show that the reduced master equation can be solved exactly for a number of biochemical networks involving gene expression and enzyme catalysis, whose conventional chemical master equation description is analytically impenetrable. We use the linear noise approximation to obtain approximate expressions for the difference between the variance of fluctuations in the non-abundant species as predicted by the hybrid approach and by the conventional chemical master equation. Furthermore, we show that surprisingly, irrespective of any separation in the mean molecule numbers of various species, the conventional and hybrid master equations exactly agree for a class of chemical systems.« less

Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study.

PubMed

von Stosch, Moritz; Hamelink, Jan-Martijn; Oliveira, Rui

2016-05-01

Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.

PGTandMe: social networking-based genetic testing and the evolving research model.

PubMed

Koch, Valerie Gutmann

2012-01-01

The opportunity to use extensive genetic data, personal information, and family medical history for research purposes may be naturally appealing to the personal genetic testing (PGT) industry, which is already coupling direct-to-consumer (DTC) products with social networking technologies, as well as to potential industry or institutional partners. This article evaluates the transformation in research that the hybrid of PGT and social networking will bring about, and--highlighting the challenges associated with a new paradigm of "patient-driven" genomic research--focuses on the consequences of shifting the structure, locus, timing, and scope of research through genetic crowd-sourcing. This article also explores potential ethical, legal, and regulatory issues that arise from the hybrid between personal genomic research and online social networking, particularly regarding informed consent, institutional review board (IRB) oversight, and ownership/intellectual property (IP) considerations.

A hybrid multiscale Monte Carlo algorithm (HyMSMC) to cope with disparity in time scales and species populations in intracellular networks.

PubMed

Samant, Asawari; Ogunnaike, Babatunde A; Vlachos, Dionisios G

2007-05-24

The fundamental role that intrinsic stochasticity plays in cellular functions has been shown via numerous computational and experimental studies. In the face of such evidence, it is important that intracellular networks are simulated with stochastic algorithms that can capture molecular fluctuations. However, separation of time scales and disparity in species population, two common features of intracellular networks, make stochastic simulation of such networks computationally prohibitive. While recent work has addressed each of these challenges separately, a generic algorithm that can simultaneously tackle disparity in time scales and population scales in stochastic systems is currently lacking. In this paper, we propose the hybrid, multiscale Monte Carlo (HyMSMC) method that fills in this void. The proposed HyMSMC method blends stochastic singular perturbation concepts, to deal with potential stiffness, with a hybrid of exact and coarse-grained stochastic algorithms, to cope with separation in population sizes. In addition, we introduce the computational singular perturbation (CSP) method as a means of systematically partitioning fast and slow networks and computing relaxation times for convergence. We also propose a new criteria of convergence of fast networks to stochastic low-dimensional manifolds, which further accelerates the algorithm. We use several prototype and biological examples, including a gene expression model displaying bistability, to demonstrate the efficiency, accuracy and applicability of the HyMSMC method. Bistable models serve as stringent tests for the success of multiscale MC methods and illustrate limitations of some literature methods.

Cascaded neural networks for sequenced propagation estimation, multiuser detection, and adaptive radio resource control of third-generation wireless networks for multimedia services

NASA Astrophysics Data System (ADS)

Hortos, William S.

1999-03-01

A hybrid neural network approach is presented to estimate radio propagation characteristics and multiuser interference and to evaluate their combined impact on throughput, latency and information loss in third-generation (3G) wireless networks. The latter three performance parameters influence the quality of service (QoS) for multimedia services under consideration for 3G networks. These networks, based on a hierarchical architecture of overlaying macrocells on top of micro- and picocells, are planned to operate in mobile urban and indoor environments with service demands emanating from circuit-switched, packet-switched and satellite-based traffic sources. Candidate radio interfaces for these networks employ a form of wideband CDMA in 5-MHz and wider-bandwidth channels, with possible asynchronous operation of the mobile subscribers. The proposed neural network (NN) architecture allocates network resources to optimize QoS metrics. Parameters of the radio propagation channel are estimated, followed by control of an adaptive antenna array at the base station to minimize interference, and then joint multiuser detection is performed at the base station receiver. These adaptive processing stages are implemented as a sequence of NN techniques that provide their estimates as inputs to a final- stage Kohonen self-organizing feature map (SOFM). The SOFM optimizes the allocation of available network resources to satisfy QoS requirements for variable-rate voice, data and video services. As the first stage of the sequence, a modified feed-forward multilayer perceptron NN is trained on the pilot signals of the mobile subscribers to estimate the parameters of shadowing, multipath fading and delays on the uplinks. A recurrent NN (RNN) forms the second stage to control base stations' adaptive antenna arrays to minimize intra-cell interference. The third stage is based on a Hopfield NN (HNN), modified to detect multiple users on the uplink radio channels to mitigate multiaccess interference, control carrier-sense multiple-access (CSMA) protocols, and refine call handoff procedures. In the final stage, the Kohonen SOFM, operating in a hybrid continuous and discrete space, adaptively allocates the resources of antenna-based cell sectorization, activity monitoring, variable-rate coding, power control, handoff and caller admission to meet user demands for various multimedia services at minimum QoS levels. The performance of the NN cascade is evaluated through simulation of a candidate 3G wireless network using W-CDMA parameters in a small-cell environment. The simulated network consists of a representative number of cells. Mobile users with typical movement patterns are assumed. QoS requirements for different classes of multimedia services are considered. The proposed method is shown to provide relatively low probability of new call blocking and handoff dropping, while maintaining efficient use of the network's radio resources.

Magnetic Exchange Couplings from Semilocal Functionals Evaluated Nonself-Consistently on Hybrid Densities: Insights on Relative Importance of Exchange, Correlation, and Delocalization.

PubMed

Phillips, Jordan J; Peralta, Juan E

2012-09-11

Semilocal functionals generally yield poor magnetic exchange couplings for transition-metal complexes, typically overpredicting in magnitude the experimental values. Here we show that semilocal functionals evaluated nonself-consistently on densities from hybrid functionals can yield magnetic exchange couplings that are greatly improved with respect to their self-consistent semilocal values. Furthermore, when semilocal functionals are evaluated nonself-consistently on densities from a "half-and-half" hybrid, their errors with respect to experimental values can actually be lower than those from self-consistent calculations with standard hybrid functionals such as PBEh or TPSSh. This illustrates that despite their notoriously poor performance for exchange couplings, for many systems semilocal functionals are capable of delivering accurate relative energies for magnetic states provided that their electron delocalization error is corrected. However, while self-consistent calculations with hybrids uniformly improve results for all complexes, evaluating nonself-consistently with semilocal functionals does not give a balanced improvement for both ferro- and antiferromagnetically coupled complexes, indicating that there is more at play with the overestimation problem than simply the delocalization error. Additionally, we show that for some systems the conventional wisdom of choice of exchange functional mattering more than correlation does not hold. This combined with results from the nonself-consistent calculations provide insight on clarifying the relative roles of exchange, correlation, and delocalization in calculating magnetic exchange coupling parameters in Kohn-Sham Density Functional Theory.

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

Hybrid intelligent monironing systems for thermal power plant trips

NASA Astrophysics Data System (ADS)

Barsoum, Nader; Ismail, Firas Basim

2012-11-01

Steam boiler is one of the main equipment in thermal power plants. If the steam boiler trips it may lead to entire shutdown of the plant, which is economically burdensome. Early boiler trips monitoring is crucial to maintain normal and safe operational conditions. In the present work two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MATLAB environment. The training and validation of the two systems has been performed using real operational data captured from the plant control system of selected power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables has been proposed for IMSs data analysis. The first IMS represents the use of pure Artificial Neural Network system for boiler trip detection. All seven boiler trips under consideration have been detected by IMSs before or at the same time of the plant control system. The second IMS represents the use of Genetic Algorithms and Artificial Neural Networks as a hybrid intelligent system. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables performed successfully by the hybrid intelligent system.

Hybrid Fiber/Copper LAN Meets School's 25-Year Networking Requirements.

ERIC Educational Resources Information Center

Petruso, Sam; Humes, Vince

1994-01-01

Describes an innovative new curriculum being implemented at Walnut Creek Middle School (Pennsylvania) and an advanced networked computer environment that supports it now and will also meet future needs. Topics addressed include physical facilities; networking goals, both short-term and long-term; fiber-optic cable versus copper; and future…

Networked Environments that Create Hybrid Spaces for Learning Science

ERIC Educational Resources Information Center

Otrel-Cass, Kathrin; Khoo, Elaine; Cowie, Bronwen

2014-01-01

Networked learning environments that embed the essence of the Community of Inquiry (CoI) framework utilise pedagogies that encourage dialogic practices. This can be of significance for classroom teaching across all curriculum areas. In science education, networked environments are thought to support student investigations of scientific problems,…

Ultra-thin microporous/hybrid materials

DOEpatents

Jiang, Ying-Bing [Albuquerque, NM; Cecchi, Joseph L [Albuquerque, NM; Brinker, C Jeffrey [Albuquerque, NM

2012-05-29

Ultra-thin hybrid and/or microporous materials and methods for their fabrication are provided. In one embodiment, the exemplary hybrid membranes can be formed including successive surface activation and reaction steps on a porous support that is patterned or non-patterned. The surface activation can be performed using remote plasma exposure to locally activate the exterior surfaces of porous support. Organic/inorganic hybrid precursors such as organometallic silane precursors can be condensed on the locally activated exterior surfaces, whereby ALD reactions can then take place between the condensed hybrid precursors and a reactant. Various embodiments can also include an intermittent replacement of ALD precursors during the membrane formation so as to enhance the hybrid molecular network of the membranes.

Programmed optoelectronic time-pulse coded relational processor as base element for sorting neural networks

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Bardachenko, Vitaliy F.; Nikolsky, Alexander I.; Lazarev, Alexander A.

2007-04-01

In the paper we show that the biologically motivated conception of the use of time-pulse encoding gives the row of advantages (single methodological basis, universality, simplicity of tuning, training and programming et al) at creation and designing of sensor systems with parallel input-output and processing, 2D-structures of hybrid and neuro-fuzzy neurocomputers of next generations. We show principles of construction of programmable relational optoelectronic time-pulse coded processors, continuous logic, order logic and temporal waves processes, that lie in basis of the creation. We consider structure that executes extraction of analog signal of the set grade (order), sorting of analog and time-pulse coded variables. We offer optoelectronic realization of such base relational elements of order logic, which consists of time-pulse coded phototransformers (pulse-width and pulse-phase modulators) with direct and complementary outputs, sorting network on logical elements and programmable commutations blocks. We make estimations of basic technical parameters of such base devices and processors on their basis by simulation and experimental research: power of optical input signals - 0.200-20 μW, processing time - microseconds, supply voltage - 1.5-10 V, consumption power - hundreds of microwatts per element, extended functional possibilities, training possibilities. We discuss some aspects of possible rules and principles of training and programmable tuning on the required function, relational operation and realization of hardware blocks for modifications of such processors. We show as on the basis of such quasiuniversal hardware simple block and flexible programmable tuning it is possible to create sorting machines, neural networks and hybrid data-processing systems with the untraditional numerical systems and pictures operands.

Capacity-Delay Trade-Off in Collaborative Hybrid Ad-Hoc Networks with Coverage Sensing.

PubMed

Chen, Lingyu; Luo, Wenbin; Liu, Chen; Hong, Xuemin; Shi, Jianghong

2017-01-26

The integration of ad hoc device-to-device (D2D) communications and open-access small cells can result in a networking paradigm called hybrid the ad hoc network, which is particularly promising in delivering delay-tolerant data. The capacity-delay performance of hybrid ad hoc networks has been studied extensively under a popular framework called scaling law analysis. These studies, however, do not take into account aspects of interference accumulation and queueing delay and, therefore, may lead to over-optimistic results. Moreover, focusing on the average measures, existing works fail to give finer-grained insights into the distribution of delays. This paper proposes an alternative analytical framework based on queueing theoretic models and physical interference models. We apply this framework to study the capacity-delay performance of a collaborative cellular D2D network with coverage sensing and two-hop relay. The new framework allows us to fully characterize the delay distribution in the transform domain and pinpoint the impacts of coverage sensing, user and base station densities, transmit power, user mobility and packet size on the capacity-delay trade-off. We show that under the condition of queueing equilibrium, the maximum throughput capacity per device saturates to an upper bound of 0.7239 λ b / λ u bits/s/Hz, where λ b and λ u are the densities of base stations and mobile users, respectively.

Capacity-Delay Trade-Off in Collaborative Hybrid Ad-Hoc Networks with Coverage Sensing

PubMed Central

Chen, Lingyu; Luo, Wenbin; Liu, Chen; Hong, Xuemin; Shi, Jianghong

2017-01-01

The integration of ad hoc device-to-device (D2D) communications and open-access small cells can result in a networking paradigm called hybrid the ad hoc network, which is particularly promising in delivering delay-tolerant data. The capacity-delay performance of hybrid ad hoc networks has been studied extensively under a popular framework called scaling law analysis. These studies, however, do not take into account aspects of interference accumulation and queueing delay and, therefore, may lead to over-optimistic results. Moreover, focusing on the average measures, existing works fail to give finer-grained insights into the distribution of delays. This paper proposes an alternative analytical framework based on queueing theoretic models and physical interference models. We apply this framework to study the capacity-delay performance of a collaborative cellular D2D network with coverage sensing and two-hop relay. The new framework allows us to fully characterize the delay distribution in the transform domain and pinpoint the impacts of coverage sensing, user and base station densities, transmit power, user mobility and packet size on the capacity-delay trade-off. We show that under the condition of queueing equilibrium, the maximum throughput capacity per device saturates to an upper bound of 0.7239 λb/λu bits/s/Hz, where λb and λu are the densities of base stations and mobile users, respectively. PMID:28134769

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.

PubMed

Lo, C C; Chang, W H

2000-01-01

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.

Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+

NASA Astrophysics Data System (ADS)

Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew

2007-04-01

One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

A Hybrid Neural Network and Feature Extraction Technique for Target Recognition.

DTIC Science & Technology

target features are extracted, the extracted data being evaluated in an artificial neural network to identify a target at a location within the image scene from which the different viewing angles extend.

Optimal Scheduling for Underwater Communications in Multiple-user Scenarios

DTIC Science & Technology

2014-09-30

underwater acoustic sensor networks . These techniques aim at consuming as less energy as... underwater acoustic networks disrupt the behavior of surrounding species of marine mammals. As a consequence of these two studies, we aim at developing...Markov models of incremental redundancy hybrid ARQ over underwater acoustic channels. Elsevier Journal on Ad-hoc Networks (Special Issue on Underwater Communications and Networks ), 2014. 4

Max-E47, a Designed Minimalist Protein that Targets the E-Box DNA Site In Vivo and In Vitro

PubMed Central

Xu, Jing; Chen, Gang; De Jong, Antonia T.; Shahravan, S. Hesam; Shin, Jumi A.

2009-01-01

Max-E47 is a designed hybrid protein comprising the Max DNA-binding basic region and E47 HLH dimerization subdomain. In the yeast one-hybrid system (Y1H), Max-E47 shows strong transcriptional activation from the E-box site, 5'-CACGTG, targeted by the Myc/Max/Mad network of transcription factors; two mutants, Max-E47Y and Max-E47YF, activate more weakly from the E-box in the Y1H. Quantitative fluorescence anisotropy titrations to gain free energies of protein:DNA binding gave low nM Kd values for the native MaxbHLHZ, Max-E47, and the Y and YF mutants binding to the E-box site (14 nM, 15 nM, 9 nM, and 6 nM, respectively), with no detectable binding to a nonspecific control duplex. Because these minimalist, E-box-binding hybrids have no activation domain and no interactions with the c-MycbHLHZ, as shown by the yeast two-hybrid assay, they can potentially serve as dominant-negative inhibitors that suppress activation of E-box-responsive genes targeted by transcription factors including the c-Myc/Max complex. As proof-of-principle, we used our modified Y1H, which allows direct competition between two proteins vying for a DNA target, to show that Max-E47 effectively outcompetes the native MaxbHLHZ for the E-box; weaker competition is observed from the two mutants, consistent with Y1H results. These hybrids provide a minimalist scaffold for further exploration of the relationship between protein structure and DNA-binding function and may have applications as protein therapeutics or biochemical probes capable of targeting the E-box site. PMID:19449889

Distributed reservation control protocols for random access broadcasting channels

NASA Technical Reports Server (NTRS)

Greene, E. P.; Ephremides, A.

1981-01-01

Attention is given to a communication network consisting of an arbitrary number of nodes which can communicate with each other via a time-division multiple access (TDMA) broadcast channel. The reported investigation is concerned with the development of efficient distributed multiple access protocols for traffic consisting primarily of single packet messages in a datagram mode of operation. The motivation for the design of the protocols came from the consideration of efficient multiple access utilization of moderate to high bandwidth (4-40 Mbit/s capacity) communication satellite channels used for the transmission of short (1000-10,000 bits) fixed length packets. Under these circumstances, the ratio of roundtrip propagation time to packet transmission time is between 100 to 10,000. It is shown how a TDMA channel can be adaptively shared by datagram traffic and constant bandwidth users such as in digital voice applications. The distributed reservation control protocols described are a hybrid between contention and reservation protocols.

Demonstration of hybrid orbital angular momentum multiplexing and time-division multiplexing passive optical network.

PubMed

Wang, Andong; Zhu, Long; Liu, Jun; Du, Cheng; Mo, Qi; Wang, Jian

2015-11-16

Mode-division multiplexing passive optical network (MDM-PON) is a promising scheme for next-generation access networks to further increase fiber transmission capacity. In this paper, we demonstrate the proof-of-concept experiment of hybrid mode-division multiplexing (MDM) and time-division multiplexing (TDM) PON architecture by exploiting orbital angular momentum (OAM) modes. Bidirectional transmissions with 2.5-Gbaud 4-level pulse amplitude modulation (PAM-4) downstream and 2-Gbaud on-off keying (OOK) upstream are demonstrated in the experiment. The observed optical signal-to-noise ratio (OSNR) penalties for downstream and upstream transmissions at a bit-error rate (BER) of 2 × 10(-3) are less than 2.0 dB and 3.0 dB, respectively.

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

PubMed Central

Ramadan Suleiman, Ahmed; Nehdi, Moncef L.

2017-01-01

This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. PMID:28772495

Hybrid scheduling mechanisms for Next-generation Passive Optical Networks based on network coding

NASA Astrophysics Data System (ADS)

Zhao, Jijun; Bai, Wei; Liu, Xin; Feng, Nan; Maier, Martin

2014-10-01

Network coding (NC) integrated into Passive Optical Networks (PONs) is regarded as a promising solution to achieve higher throughput and energy efficiency. To efficiently support multimedia traffic under this new transmission mode, novel NC-based hybrid scheduling mechanisms for Next-generation PONs (NG-PONs) including energy management, time slot management, resource allocation, and Quality-of-Service (QoS) scheduling are proposed in this paper. First, we design an energy-saving scheme that is based on Bidirectional Centric Scheduling (BCS) to reduce the energy consumption of both the Optical Line Terminal (OLT) and Optical Network Units (ONUs). Next, we propose an intra-ONU scheduling and an inter-ONU scheduling scheme, which takes NC into account to support service differentiation and QoS assurance. The presented simulation results show that BCS achieves higher energy efficiency under low traffic loads, clearly outperforming the alternative NC-based Upstream Centric Scheduling (UCS) scheme. Furthermore, BCS is shown to provide better QoS assurance.

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

PubMed

Ramadan Suleiman, Ahmed; Nehdi, Moncef L

2017-02-07

This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

Managing the Cooperative Network.

ERIC Educational Resources Information Center

Segal, JoAn S.

1983-01-01

Discussion of the management of not-for-profit corporations which provide computerized library networks highlights marketing, nonprofit constraints, multiple goals, consumer demands, professional commitment, external influences, motivation and control, dependence on charisma, management and altruism, hybrid organizations, and rational management.…

Moving the boundary between wavelength resources in optical packet and circuit integrated ring network.

PubMed

Furukawa, Hideaki; Miyazawa, Takaya; Wada, Naoya; Harai, Hiroaki

2014-01-13

Optical packet and circuit integrated (OPCI) networks provide both optical packet switching (OPS) and optical circuit switching (OCS) links on the same physical infrastructure using a wavelength multiplexing technique in order to deal with best-effort services and quality-guaranteed services. To immediately respond to changes in user demand for OPS and OCS links, OPCI networks should dynamically adjust the amount of wavelength resources for each link. We propose a resource-adjustable hybrid optical packet/circuit switch and transponder. We also verify that distributed control of resource adjustments can be applied to the OPCI ring network testbed we developed. In cooperation with the resource adjustment mechanism and the hybrid switch and transponder, we demonstrate that automatically allocating a shared resource and moving the wavelength resource boundary between OPS and OCS links can be successfully executed, depending on the number of optical paths in use.

Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

NASA Astrophysics Data System (ADS)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

The adaptive safety analysis and monitoring system

NASA Astrophysics Data System (ADS)

Tu, Haiying; Allanach, Jeffrey; Singh, Satnam; Pattipati, Krishna R.; Willett, Peter

2004-09-01

The Adaptive Safety Analysis and Monitoring (ASAM) system is a hybrid model-based software tool for assisting intelligence analysts to identify terrorist threats, to predict possible evolution of the terrorist activities, and to suggest strategies for countering terrorism. The ASAM system provides a distributed processing structure for gathering, sharing, understanding, and using information to assess and predict terrorist network states. In combination with counter-terrorist network models, it can also suggest feasible actions to inhibit potential terrorist threats. In this paper, we will introduce the architecture of the ASAM system, and discuss the hybrid modeling approach embedded in it, viz., Hidden Markov Models (HMMs) to detect and provide soft evidence on the states of terrorist network nodes based on partial and imperfect observations, and Bayesian networks (BNs) to integrate soft evidence from multiple HMMs. The functionality of the ASAM system is illustrated by way of application to the Indian Airlines Hijacking, as modeled from open sources.

A New Stochastic Technique for Painlevé Equation-I Using Neural Network Optimized with Swarm Intelligence

PubMed Central

Raja, Muhammad Asif Zahoor; Khan, Junaid Ali; Ahmad, Siraj-ul-Islam; Qureshi, Ijaz Mansoor

2012-01-01

A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method. PMID:22919371

Applications of UV/Vis Spectroscopy in Characterization and Catalytic Activity of Noble Metal Nanoparticles Fabricated in Responsive Polymer Microgels: A Review.

PubMed

Begum, Robina; Farooqi, Zahoor H; Naseem, Khalida; Ali, Faisal; Batool, Madeeha; Xiao, Jianliang; Irfan, Ahmad

2018-11-02

Noble metal nanoparticles loaded smart polymer microgels have gained much attention due to fascinating combination of their properties in a single system. These hybrid systems have been extensively used in biomedicines, photonics, and catalysis. Hybrid microgels are characterized by using various techniques but UV/Vis spectroscopy is an easily available technique for characterization of noble metal nanoparticles loaded microgels. This technique is widely used for determination of size and shape of metal nanoparticles. The tuning of optical properties of noble metal nanoparticles under various stimuli can be studied using UV/Vis spectroscopic method. Time course UV/Vis spectroscopy can also be used to monitor the kinetics of swelling and deswelling of microgels and hybrid microgels. Growth of metal nanoparticles in polymeric network or growth of polymeric network around metal nanoparticle core can be studied by using UV/Vis spectroscopy. This technique can also be used for investigation of various applications of hybrid materials in catalysis, photonics, and sensing. This tutorial review describes the uses of UV/Vis spectroscopy in characterization and catalytic applications of responsive hybrid microgels with respect to recent research progress in this area.

A B-C-N hybrid porous sheet: an efficient metal-free visible-light absorption material.

PubMed

Lu, Ruifeng; Li, Feng; Salafranca, Juan; Kan, Erjun; Xiao, Chuanyun; Deng, Kaiming

2014-03-07

The polyphenylene network, known as porous graphene, is one of the most important and widely studied two-dimensional materials. As a potential candidate for photocatalysis and photovoltaic energy generation, its application has been limited by the low photocatalytic activity in the visible-light region. State-of-the-art hybrid density functional theory investigations are presented to show that an analogous B-C-N porous sheet outperforms the pristine polyphenylene network with significantly enhanced visible-light absorption. Compared with porous graphene, the calculated energy gap of the B-C-N hybrid crystal shrinks to 2.7 eV and the optical absorption peak remarkably shifts to the visible light region. The redox potentials of water splitting are well positioned in the middle of the band gap. Hybridizations among B_p, N_p and C_p orbitals are responsible for these findings. Valence and conduction band calculations indicate that the electrons and holes can be effectively separated, reducing charge recombination and improving the photoconversion efficiency. Moreover, the band gap and optical properties of the B-C-N hybrid porous sheet can be further finely engineered by external strain.

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Hybrid WDM/OCDMA for next generation access network

NASA Astrophysics Data System (ADS)

Wang, Xu; Wada, Naoya; Miyazaki, T.; Cincotti, G.; Kitayama, Ken-ichi

2007-11-01

Hybrid wavelength division multiplexing/optical code division multiple access (WDM/OCDMA) passive optical network (PON), where asynchronous OCDMA traffic transmits over WDM network, can be one potential candidate for gigabit-symmetric fiber-to-the-home (FTTH) services. In a cost-effective WDM/OCDMA network, a large scale multi-port encoder/decoder can be employed in the central office, and a low cost encoder/decoder will be used in optical network unit (ONU). The WDM/OCDMA system could be one promising solution to the symmetric high capacity access network with high spectral efficiency, cost effective, good flexibility and enhanced security. Asynchronous WDM/OCDMA systems have been experimentally demonstrated using superstructured fiber Bragg gratings (SSFBG) and muti-port OCDMA en/decoders. The total throughput has reached above Tera-bit/s with spectral efficiency of about 0.41. The key enabling techniques include ultra-long SSFBG, multi-port E/D with high power contrast ratio, optical thresholding, differential phase shift keying modulation with balanced detection, forward error correction, and etc. Using multi-level modulation formats to carry multi-bit information with single pulse, the total capacity and spectral efficiency could be further enhanced.

A method for exploring implicit concept relatedness in biomedical knowledge network.

PubMed

Bai, Tian; Gong, Leiguang; Wang, Ye; Wang, Yan; Kulikowski, Casimir A; Huang, Lan

2016-07-19

Biomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community. In this study, a hybrid biomedical knowledge network is constructed by linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledge sources. To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge network, for which a relatedness network (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-theoretic operations. Semantic constraints are designed and implemented to prune the search space of the relatedness network. Experiments to test examples of several biomedical applications have been carried out, and the evaluation of the results showed an encouraging potential of the proposed approach to biomedical knowledge discovery.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization.

PubMed

Jiang, Ailian; Zheng, Lihong

2018-03-29

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

PubMed Central

2018-01-01

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime. PMID:29596336

A hybrid modeling approach for option pricing

NASA Astrophysics Data System (ADS)

Hajizadeh, Ehsan; Seifi, Abbas

2011-11-01

The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.

Patent Abstract Digest. Volume II.

DTIC Science & Technology

1981-03-01

THE AIR FORCE SYSTEMS COMMAND United States Patent 191 [J 4,190,815 Albanese [45] Feb. 26, 1980 [541 HIGH POWER HYBRID SWITCH 3,659.227 4/1972...R.F. power are controlled and switched [22] Filed: Mar. 9, 1978 by means of a hybrid switching network that employs [511 nt. C. 2...broadband quadrature 3dB hybrid . Switching is accomplished by selectively inserting a [561 Referenees Cited 180 phase shift means into the lower power

Energy optimization in mobile sensor networks

NASA Astrophysics Data System (ADS)

Yu, Shengwei

Mobile sensor networks are considered to consist of a network of mobile robots, each of which has computation, communication and sensing capabilities. Energy efficiency is a critical issue in mobile sensor networks, especially when mobility (i.e., locomotion control), routing (i.e., communications) and sensing are unique characteristics of mobile robots for energy optimization. This thesis focuses on the problem of energy optimization of mobile robotic sensor networks, and the research results can be extended to energy optimization of a network of mobile robots that monitors the environment, or a team of mobile robots that transports materials from stations to stations in a manufacturing environment. On the energy optimization of mobile robotic sensor networks, our research focuses on the investigation and development of distributed optimization algorithms to exploit the mobility of robotic sensor nodes for network lifetime maximization. In particular, the thesis studies these five problems: 1. Network-lifetime maximization by controlling positions of networked mobile sensor robots based on local information with distributed optimization algorithms; 2. Lifetime maximization of mobile sensor networks with energy harvesting modules; 3. Lifetime maximization using joint design of mobility and routing; 4. Optimal control for network energy minimization; 5. Network lifetime maximization in mobile visual sensor networks. In addressing the first problem, we consider only the mobility strategies of the robotic relay nodes in a mobile sensor network in order to maximize its network lifetime. By using variable substitutions, the original problem is converted into a convex problem, and a variant of the sub-gradient method for saddle-point computation is developed for solving this problem. An optimal solution is obtained by the method. Computer simulations show that mobility of robotic sensors can significantly prolong the lifetime of the whole robotic sensor network while consuming negligible amount of energy for mobility cost. For the second problem, the problem is extended to accommodate mobile robotic nodes with energy harvesting capability, which makes it a non-convex optimization problem. The non-convexity issue is tackled by using the existing sequential convex approximation method, based on which we propose a novel procedure of modified sequential convex approximation that has fast convergence speed. For the third problem, the proposed procedure is used to solve another challenging non-convex problem, which results in utilizing mobility and routing simultaneously in mobile robotic sensor networks to prolong the network lifetime. The results indicate that joint design of mobility and routing has an edge over other methods in prolonging network lifetime, which is also the justification for the use of mobility in mobile sensor networks for energy efficiency purpose. For the fourth problem, we include the dynamics of the robotic nodes in the problem by modeling the networked robotic system using hybrid systems theory. A novel distributed method for the networked hybrid system is used to solve the optimal moving trajectories for robotic nodes and optimal network links, which are not answered by previous approaches. Finally, the fact that mobility is more effective in prolonging network lifetime for a data-intensive network leads us to apply our methods to study mobile visual sensor networks, which are useful in many applications. We investigate the joint design of mobility, data routing, and encoding power to help improving the video quality while maximizing the network lifetime. This study leads to a better understanding of the role mobility can play in data-intensive surveillance sensor networks.

DNA-nanoparticle assemblies go organic: macroscopic polymeric materials with nanosized features.

PubMed

Mentovich, Elad D; Livanov, Konstantin; Prusty, Deepak K; Sowwan, Mukules; Richter, Shachar

2012-05-30

One of the goals in the field of structural DNA nanotechnology is the use of DNA to build up 2- and 3-D nanostructures. The research in this field is motivated by the remarkable structural features of DNA as well as by its unique and reversible recognition properties. Nucleic acids can be used alone as the skeleton of a broad range of periodic nanopatterns and nanoobjects and in addition, DNA can serve as a linker or template to form DNA-hybrid structures with other materials. This approach can be used for the development of new detection strategies as well as nanoelectronic structures and devices. Here we present a new method for the generation of unprecedented all-organic conjugated-polymer nanoparticle networks guided by DNA, based on a hierarchical self-assembly process. First, microphase separation of amphiphilic block copolymers induced the formation of spherical nanoobjects. As a second ordering concept, DNA base pairing has been employed for the controlled spatial definition of the conjugated-polymer particles within the bulk material. These networks offer the flexibility and the diversity of soft polymeric materials. Thus, simple chemical methodologies could be applied in order to tune the network's electrical, optical and mechanical properties. One- two- and three-dimensional networks have been successfully formed. Common to all morphologies is the integrity of the micelles consisting of DNA block copolymer (DBC), which creates an all-organic engineered network.

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

PubMed Central

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy. PMID:29768463

Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population.

PubMed

Wei, Xile; Zhang, Danhong; Lu, Meili; Wang, Jiang; Yu, Haitao; Che, Yanqiu

2015-01-01

This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

PubMed

Rani R, Hannah Jessie; Victoire T, Aruldoss Albert

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

Static-dynamic hybrid communication scheduling and control co-design for networked control systems.

PubMed

Wen, Shixi; Guo, Ge

2017-11-01

In this paper, the static-dynamic hybrid communication scheduling and control co-design is proposed for the networked control systems (NCSs) to solve the capacity limitation of the wireless communication network. The analytical most regular binary sequences (MRBSs) are used as the communication scheduling function for NCSs. When the communication conflicts yielded in the binary sequence MRBSs, a dynamic scheduling strategy is proposed to on-line reallocate the medium access status for each plant. Under such static-dynamic hybrid scheduling policy, plants in NCSs are described as the non-uniform sampled-control systems, whose controller have a group of controller gains and switch according to the sampling interval yielded by the binary sequence. A useful communication scheduling and control co-design framework is proposed for the NCSs to simultaneously decide the controller gains and the parameters used to generate the communication sequences MRBS. Numerical example and realistic example are respectively given to demonstrate the effectiveness of the proposed co-design method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Fiber sensor network with multipoint sensing using double-pass hybrid LPFG-FBG sensor configuration

NASA Astrophysics Data System (ADS)

Yong, Yun-Thung; Lee, Sheng-Chyan; Rahman, Faidz Abd

2017-03-01

This is a study on double-pass intensity-based hybrid Long Period Fiber Grating (LPFG)and Fiber Bragg Grating (FBG) sensor configuration where a fiber sensor network was constructed with multiple sensing capability. The sensing principle is based on interrogation of intensity changes of the reflected signal from an FBG caused by the LPFG spectral response to the surrounding perturbations. The sensor network developed was tested in monitoring diesel adulteration of up to a distance of 8 km. Kerosene concentration from 0% to 50% was added as adulterant into diesel. The sensitivity of the double-pass hybrid LPFG-FBG sensor over multiple points was>0.21 dB/% (for adulteration range of 0-30%) and >0.45 dB/% from 30% to 50% adulteration. It is found that the sensitivity can drop up to 35% when the fiber length increased from 0 km to 8 km (for the case of adulteration of 0-30%). With the multiple sensing capabilities, normalized FBG's reflected power can be demodulated at the same time for comparison of sensitivity performance across various fiber sensors.

Photonic quantum state transfer between a cold atomic gas and a crystal.

PubMed

Maring, Nicolas; Farrera, Pau; Kutluer, Kutlu; Mazzera, Margherita; Heinze, Georg; de Riedmatten, Hugues

2017-11-22

Interfacing fundamentally different quantum systems is key to building future hybrid quantum networks. Such heterogeneous networks offer capabilities superior to those of their homogeneous counterparts, as they merge the individual advantages of disparate quantum nodes in a single network architecture. However, few investigations of optical hybrid interconnections have been carried out, owing to fundamental and technological challenges such as wavelength and bandwidth matching of the interfacing photons. Here we report optical quantum interconnection of two disparate matter quantum systems with photon storage capabilities. We show that a quantum state can be transferred faithfully between a cold atomic ensemble and a rare-earth-doped crystal by means of a single photon at 1,552  nanometre telecommunication wavelength, using cascaded quantum frequency conversion. We demonstrate that quantum correlations between a photon and a single collective spin excitation in the cold atomic ensemble can be transferred to the solid-state system. We also show that single-photon time-bin qubits generated in the cold atomic ensemble can be converted, stored and retrieved from the crystal with a conditional qubit fidelity of more than 85 per cent. Our results open up the prospect of optically connecting quantum nodes with different capabilities and represent an important step towards the realization of large-scale hybrid quantum networks.

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing.

PubMed

Popescu, Dan; Ichim, Loretta; Stoican, Florin

2017-02-23

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes-fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms.

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

PubMed Central

Popescu, Dan; Ichim, Loretta; Stoican, Florin

2017-01-01

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms. PMID:28241479

Novel Flexible Transparent Conductive Films with Enhanced Chemical and Electromechanical Sustainability: TiO2 Nanosheet-Ag Nanowire Hybrid.

PubMed

Sohn, Hiesang; Kim, Seyun; Shin, Weonho; Lee, Jong Min; Lee, Hyangsook; Yun, Dong-Jin; Moon, Kyoung-Seok; Han, In Taek; Kwak, Chan; Hwang, Seong-Ju

2018-01-24

Flexible transparent conductive films (TCFs) of TiO 2 nanosheet (TiO 2 NS) and silver nanowire (Ag NW) network hybrid were prepared through a simple and scalable solution-based process. The as-formed TiO 2 NS-Ag NW hybrid TCF shows a high optical transmittance (TT: 97% (90.2% including plastic substrate)) and low sheet resistance (R s : 40 Ω/sq). In addition, the TiO 2 NS-Ag NW hybrid TCF exhibits a long-time chemical/aging and electromechanical stability. As for the chemical/aging stability, the hybrid TCF of Ag NW and TiO 2 NS reveals a retained initial conductivity (ΔR s /R s < 1%) under ambient oxidant gas over a month, superior to that of bare Ag NW (ΔR s /R s > 4000%) or RuO 2 NS-Ag NW hybrid (ΔR s /R s > 200%). As corroborated by the density functional theory simulation, the superb chemical stability of TiO 2 NS-Ag NW hybrid is attributable to the unique role of TiO 2 NS as a barrier, which prevents Ag NW's chemical corrosion via the attenuated adsorption of sulfidation molecules (H 2 S) on TiO 2 NS. With respect to the electromechanical stability, in contrast to Ag NWs (ΔR/R 0 ∼ 152.9%), our hybrid TCF shows a limited increment of fractional resistivity (ΔR/R 0 ∼ 14.4%) after 200 000 cycles of the 1R bending test (strain: 6.7%) owing to mechanically welded Ag NW networks by TiO 2 NS. Overall, our unique hybrid of TiO 2 NS and Ag NW exhibits excellent electrical/optical properties and reliable chemical/electromechanical stabilities.

Regulatory Divergence between Parental Alleles Determines Gene Expression Patterns in Hybrids

PubMed Central

Combes, Marie-Christine; Hueber, Yann; Dereeper, Alexis; Rialle, Stéphanie; Herrera, Juan-Carlos; Lashermes, Philippe

2015-01-01

Both hybridization and allopolyploidization generate novel phenotypes by conciliating divergent genomes and regulatory networks in the same cellular context. To understand the rewiring of gene expression in hybrids, the total expression of 21,025 genes and the allele-specific expression of over 11,000 genes were quantified in interspecific hybrids and their parental species, Coffea canephora and Coffea eugenioides using RNA-seq technology. Between parental species, cis- and trans-regulatory divergences affected around 32% and 35% of analyzed genes, respectively, with nearly 17% of them showing both. The relative importance of trans-regulatory divergences between both species could be related to their low genetic divergence and perennial habit. In hybrids, among divergently expressed genes between parental species and hybrids, 77% was expressed like one parent (expression level dominance), including 65% like C. eugenioides. Gene expression was shown to result from the expression of both alleles affected by intertwined parental trans-regulatory factors. A strong impact of C. eugenioides trans-regulatory factors on the upregulation of C. canephora alleles was revealed. The gene expression patterns appeared determined by complex combinations of cis- and trans-regulatory divergences. In particular, the observed biased expression level dominance seemed to be derived from the asymmetric effects of trans-regulatory parental factors on regulation of alleles. More generally, this study illustrates the effects of divergent trans-regulatory parental factors on the gene expression pattern in hybrids. The characteristics of the transcriptional response to hybridization appear to be determined by the compatibility of gene regulatory networks and therefore depend on genetic divergences between the parental species and their evolutionary history. PMID:25819221

Engineered 3D-scaffolds of photocrosslinked chitosan-gelatin hydrogel hybrids for chronic wound dressings and regeneration.

PubMed

Carvalho, Isadora C; Mansur, Herman S

2017-09-01

Wound repair is one of the most complex biological processes in human life. To date, no ideal biomaterial solution has been identified, which that encompasses all functions and properties of real skin tissue. Thus, this study focused on the synthesis of new biocompatible hybrid hydrogel scaffolds based on methacrylate-functionalized high molecular mass chitosan with gelatin-A photocrosslinked with UV radiation to tailor matrix network properties. These hybrid hydrogels were produced via freeze-drying and were extensively characterized by swelling and degradation measurements, Fourier transform infrared spectroscopy (FTIR), UV-visible spectroscopy (UV-Vis), scanning electron microscopy (SEM-EDS), and micro-computed tomography (micro-CT). The results demonstrated that hydrogels were produced with broadly designed swelling degrees typically ranging from 500% to 2000%, which were significantly dependent on the relative concentration of polymers and irradiation time for crosslinking. Analogously, degradation was reduced with increased photocrosslinking of the network. Moreover, insights into the mechanism of photochemical crosslinking were suggested based on FTIR and UV-Vis analyses of the characteristic functional groups involved in the reactions. SEM analysis associated with micro-CT imaging of the hybrid scaffolds showed uniformly interconnected 3D porous structures, with architectural features affected by the crosslinking of the network. These hydrogels were biocompatible, with live cell viability responses of human embryonic kidney (HEK293T) cells being above 95%. Hence, novel hybrid hydrogels were designed and produced with tunable properties through photocrosslinking and with a biocompatible response suitable for use in wound dressing and skin tissue repair applications. Copyright © 2017 Elsevier B.V. All rights reserved.

Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak

NASA Astrophysics Data System (ADS)

Zheng, W.; Hu, F. R.; Zhang, M.; Chen, Z. Y.; Zhao, X. Q.; Wang, X. L.; Shi, P.; Zhang, X. L.; Zhang, X. Q.; Zhou, Y. N.; Wei, Y. N.; Pan, Y.; J-TEXT team

2018-05-01

Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time ({{T}warn} ). In particular, the {{T}warn} is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.

Hybrid Teacher Leaders and the New Professional Development Ecology

ERIC Educational Resources Information Center

Margolis, Jason

2012-01-01

This two-year study examines an emergent model for promoting classroom change amidst systemic professional development efforts--the hybrid teacher leader (HTL). Utilizing ecological and teacher social network frameworks, the relative strengths and weaknesses of educators who both teach and lead teachers are explored. In-depth qualitative data from…

Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

PubMed Central

Stover, Lori J.; Nair, Niketh S.; Faeder, James R.

2014-01-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This “network-free” approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of “partial network expansion” into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. PMID:24699269

Exact hybrid particle/population simulation of rule-based models of biochemical systems.

PubMed

Hogg, Justin S; Harris, Leonard A; Stover, Lori J; Nair, Niketh S; Faeder, James R

2014-04-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

PubMed

Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

PubMed Central

Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363

Analysis of physical layer performance of hybrid optical-wireless access network

NASA Astrophysics Data System (ADS)

Shaddad, R. Q.; Mohammad, A. B.; Al-hetar, A. M.

2011-09-01

The hybrid optical-wireless access network (HOWAN) is a favorable architecture for next generation access network. It is an optimal combination of an optical backhaul and a wireless front-end for an efficient access network. In this paper, the HOWAN architecture is designed based on a wavelengths division multiplexing/time division multiplexing passive optical network (WDM/TDM PON) at the optical backhaul and a wireless fidelity (WiFi) technology at the wireless front-end. The HOWAN is proposed that can provide blanket coverage of broadband and flexible connection for end-users. Most of the existing works, based on performance evaluation are concerned on network layer aspects. This paper reports physical layer performance in terms of the bit error rate (BER), eye diagram, and signal-to-noise ratio (SNR) of the communication system. It accommodates 8 wavelength channels with 32 optical network unit/wireless access points (ONU/APs). It is demonstrated that downstream and upstream of 2 Gb/s can be achieved by optical backhaul for each wavelength channel along optical fiber length of 20 km and a data rate of 54 Mb/s per ONU/AP along a 50 m outdoor wireless link.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

PubMed Central

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-01-01

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting. PMID:29883381

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

PubMed

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-05-21

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks.

PubMed

Jung, Young-Ho; Choi, Jihoon

2017-02-25

A new hybrid automatic repeat request (HARQ) scheme for multicast service for wireless sensor networks is proposed in this study. In the proposed algorithm, the HARQ operation is combined with an autonomous retransmission method that ensure a data packet is transmitted irrespective of whether or not the packet is successfully decoded at the receivers. The optimal number of autonomous retransmissions is determined to ensure maximum spectral efficiency, and a practical method that adjusts the number of autonomous retransmissions for realistic conditions is developed. Simulation results show that the proposed method achieves higher spectral efficiency than existing HARQ techniques.

Strain-mediated mechanical coupling to diamond spins

NASA Astrophysics Data System (ADS)

Bleszynski Jayich, Ania

2015-03-01

Nitrogen-vacancy (NV) centers in diamond are atomic-scale spin systems with remarkable quantum properties that persist to room temperature. The recent demonstration of high-quality single-crystal diamond resonators has led to significant interest in a hybrid system consisting of NV spins that interact with the resonant phonon modes of a macroscopic mechanical resonator through crystal strain. We demonstrate dynamic, strain-mediated coupling of the mechanical motion of a diamond cantilever to the spin of an embedded NV. Via quantum control of the spin, we quantitatively characterize the axial and transverse strain sensitivities of the nitrogen-vacancy ground-state spin. The nitrogen-vacancy center is an atomic scale sensor and we demonstrate spin-based strain imaging with a strain sensitivity of 3x10-6 strain Hz1/2. We discuss prospects for reaching the regime of quantum coupling between phonons and spins, and we present our results in this direction. This hybrid system has exciting prospects for a phonon-based approach to integrating NVs into quantum networks. Funding from the AFOSR MURI and NSF CAREER programs are gratefully acknowledged.

A semi-symmetric image encryption scheme based on the function projective synchronization of two hyperchaotic systems

PubMed Central

Li, Jinqing; Qi, Hui; Cong, Ligang; Yang, Huamin

2017-01-01

Both symmetric and asymmetric color image encryption have advantages and disadvantages. In order to combine their advantages and try to overcome their disadvantages, chaos synchronization is used to avoid the key transmission for the proposed semi-symmetric image encryption scheme. Our scheme is a hybrid chaotic encryption algorithm, and it consists of a scrambling stage and a diffusion stage. The control law and the update rule of function projective synchronization between the 3-cell quantum cellular neural networks (QCNN) response system and the 6th-order cellular neural network (CNN) drive system are formulated. Since the function projective synchronization is used to synchronize the response system and drive system, Alice and Bob got the key by two different chaotic systems independently and avoid the key transmission by some extra security links, which prevents security key leakage during the transmission. Both numerical simulations and security analyses such as information entropy analysis, differential attack are conducted to verify the feasibility, security, and efficiency of the proposed scheme. PMID:28910349

A Hybrid LCC-VSC HVDC Transmission System Supplying a Passive Load

NASA Astrophysics Data System (ADS)

Kotb, Omar

High Voltage Direct Current (HVDC) transmission systems continue to be an excellent asset in modern power systems, mainly for their ability to overcome the problems of AC transmission, such as the interconnection of asynchronous grids, stability of long transmission lines, and use of long cables for power transmission. In the past 20 years, Voltage Source Converter (VSC)-HVDC transmission systems were developed and installed in many projects, thereby adding more operational benefits to DC transmission option, such as high controllability, ability to supply weak networks, and reduced converter reactive power demand. Nevertheless, VSC-HVDC transmission suffers from the disadvantages of high losses and cost. In this research, a hybrid HVDC employing a Line Commutated Converter (LCC) as rectifier and a VSC as inverter is used to supply a passive network through a DC cable. The hybrid system is best suited for unidirectional power transmission scenarios, such as power transmission to islands and remote load centers, where the construction of new transmission lines is prohibitively expensive. Control modes for the rectifier and inverter are selected and implemented using Proportional Integral (PI) controllers. Special control schemes are developed for abnormal operating conditions such as starting at light load and recovering from AC network faults. The system performance under steady state and transient conditions is investigated by EMTP-RV simulations. The results show the feasibility of the hybrid system.

Hybrid networks based on epoxidized camelina oil

PubMed Central

Balanuca, Brindusa; Stan, Raluca; Lungu, Adriana; Vasile, Eugeniu; Iovu, Horia

2017-01-01

Abstract Lately, renewable resources received great attention in the macromolecular compounds area, regarding the design of the monomers and polymers with different applications. In this study the capacity of several modified vegetable oil-based monomers to build competitive hybrid networks was investigate, taking into account thermal and mechanical behavior of the designed materials. In order to synthesize such competitive nanocomposites, the selected renewable raw material, camelina oil, was employed due to the non-toxicity and biodegradability behavior. General properties of epoxidized camelina oil-based materials were improved by loading of different types of organic-inorganic hybrid compounds – polyhedral oligomeric silsesquioxane (POSS) bearing one (POSS1Ep) or eight (POSS8Ep) epoxy rings on the cages. In order to identify the chemical changes occurring after the thermal curing reactions, FT-IR spectrometry was employed. The new synthesized nanocomposites based on epoxidized camelina oil (ECO) were characterized by dynamic mechanical analyze and thermogravimetric analyze. The morphology of the ECO-based materials was investigate by scanning electron microscopy and supplementary information regarding the presence of the POSS compounds were establish by energy dispersive X-ray analysis and X-ray photoelectron spectroscopy. The smooth materials without any separation phase indicates a well dispersion of the Si–O–Si cages within the organic matrix and the incorporation of this hybrid compounds into the ECO network demonstrates to be a well strategy to improve the thermal and mechanical properties, simultaneously. PMID:29491775

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

PubMed

Adalsteinsson, David; McMillen, David; Elston, Timothy C

2004-03-08

Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Application of nonlinear rheology to assess the effect of secondary nanofiller on network structure of hybrid polymer nanocomposites

NASA Astrophysics Data System (ADS)

Kamkar, Milad; Aliabadian, Ehsan; Shayesteh Zeraati, Ali; Sundararaj, Uttandaraman

2018-02-01

Carbon nanotube (CNT)/polymer nanocomposites exhibit excellent electrical properties by forming a percolated network. Adding a secondary filler can significantly affect the CNTs' network, resulting in changing the electrical properties. In this work, we investigated the effect of adding manganese dioxide nanowires (MnO2NWs) as a secondary nanofiller on the CNTs' network structure inside a poly(vinylidene fluoride) (PVDF) matrix. Incorporating MnO2NWs to PVDF/CNT samples produced a better state of dispersion of CNTs, as corroborated by light microscopy and transmission electron microscopy. The steady shear and oscillatory shear flows were employed to obtain a better insight into the nanofiller structure and viscoelastic behavior of the nanocomposites. The transient response under steady shear flow revealed that the stress overshoot of hybrid nanocomposites (two-fillers), PVDF/CNT/MnO2NWs, increased dramatically in comparison to binary nanocomposites (single-filler), PVDF/CNT and PVDF/MnO2NWs. This can be attributed to microstructural changes. Large amplitude oscillatory shear characterization was also performed to further investigate the effect of the secondary nanofiller on the nonlinear viscoelastic behavior of the samples. The nonlinear rheological observations were explained using quantitative nonlinear parameters [strain-stiffening ratio (S) and shear-thickening ratio (T)] and Lissajous-Bowditch plots. Results indicated that a more rigid nanofiller network was formed for the hybrid nanocomposites due to the better dispersion state of CNTs and this led to a more nonlinear viscoelastic behavior.

A hybrid model based on neural networks for biomedical relation extraction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Zhang, Shaowu; Sun, Yuanyuan; Yang, Liang

2018-05-01

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance. Copyright © 2018 Elsevier Inc. All rights reserved.

Capacity allocation mechanism based on differentiated QoS in 60 GHz radio-over-fiber local access network

NASA Astrophysics Data System (ADS)

Kou, Yanbin; Liu, Siming; Zhang, Weiheng; Shen, Guansheng; Tian, Huiping

2017-03-01

We present a dynamic capacity allocation mechanism based on the Quality of Service (QoS) for different mobile users (MU) in 60 GHz radio-over-fiber (RoF) local access networks. The proposed mechanism is capable for collecting the request information of MUs to build a full list of MU capacity demands and service types at the Central Office (CO). A hybrid algorithm is introduced to implement the capacity allocation which can satisfy the requirements of different MUs at different network traffic loads. Compared with the weight dynamic frames assignment (WDFA) scheme, the Hybrid scheme can keep high priority MUs in low delay and maintain the packet loss rate less than 1% simultaneously. At the same time, low priority MUs have a relatively better performance.

Robust dynamics in minimal hybrid models of genetic networks

PubMed Central

Perkins, Theodore J.; Wilds, Roy; Glass, Leon

2010-01-01

Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast. PMID:20921006

Robust dynamics in minimal hybrid models of genetic networks.

PubMed

Perkins, Theodore J; Wilds, Roy; Glass, Leon

2010-11-13

Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast.

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.

PubMed

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

Monitoring technique for a hybrid PS/WDM-PON by using a tunable OTDR and FBGs

NASA Astrophysics Data System (ADS)

Hann, Swook; Yoo, Jun-sang; Park, Chang-Soo

2006-05-01

A monitoring technique for hybrid passive optical networks (PON) is presented. The technique is based on the remote sensing of fibre Bragg gratings (FBGs) using a tunable optical time domain reflectometer (OTDR). The FBG would help discern an individual event during the monitoring of the hybrid PON in collaboration with the information provided by the Rayleigh backscattered power. The hybrid architecture of passive splitter-PON and WDM-PON can be analysed by the monitoring method by using the tunable OTDR and FBGs at the central office under the in-service state of PON.

Complexity, Robustness, and Multistability in Network Systems with Switching Topologies: A Hierarchical Hybrid Control Approach

DTIC Science & Technology

2015-05-22

sensor networks for managing power levels of wireless networks ; air and ground transportation systems for air traffic control and payload transport and... network systems, large-scale systems, adaptive control, discontinuous systems 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF...cover a broad spectrum of ap- plications including cooperative control of unmanned air vehicles, autonomous underwater vehicles, distributed sensor

An Energy-Aware Hybrid ARQ Scheme with Multi-ACKs for Data Sensing Wireless Sensor Networks.

PubMed

Zhang, Jinhuan; Long, Jun

2017-06-12

Wireless sensor networks (WSNs) are one of the important supporting technologies of edge computing. In WSNs, reliable communications are essential for most applications due to the unreliability of wireless links. In addition, network lifetime is also an important performance metric and needs to be considered in many WSN studies. In the paper, an energy-aware hybrid Automatic Repeat-reQuest protocol (ARQ) scheme is proposed to ensure energy efficiency under the guarantee of network transmission reliability. In the scheme, the source node sends data packets continuously with the correct window size and it does not need to wait for the acknowledgement (ACK) confirmation for each data packet. When the destination receives K data packets, it will return multiple copies of one ACK for confirmation to avoid ACK packet loss. The energy consumption of each node in flat circle network applying the proposed scheme is statistical analyzed and the cases under which it is more energy efficiency than the original scheme is discussed. Moreover, how to select parameters of the scheme is addressed to extend the network lifetime under the constraint of the network reliability. In addition, the energy efficiency of the proposed schemes is evaluated. Simulation results are presented to demonstrate that a node energy consumption reduction could be gained and the network lifetime is prolonged.

Effect of calcium source on structure and properties of sol-gel derived bioactive glasses.

PubMed

Yu, Bobo; Turdean-Ionescu, Claudia A; Martin, Richard A; Newport, Robert J; Hanna, John V; Smith, Mark E; Jones, Julian R

2012-12-18

The aim was to determine the most effective calcium precursor for synthesis of sol-gel hybrids and for improving homogeneity of sol-gel bioactive glasses. Sol-gel derived bioactive calcium silicate glasses are one of the most promising materials for bone regeneration. Inorganic/organic hybrid materials, which are synthesized by incorporating a polymer into the sol-gel process, have also recently been produced to improve toughness. Calcium nitrate is conventionally used as the calcium source, but it has several disadvantages. Calcium nitrate causes inhomogeneity by forming calcium-rich regions, and it requires high temperature treatment (>400 °C) for calcium to be incorporated into the silicate network. Nitrates are also toxic and need to be burnt off. Calcium nitrate therefore cannot be used in the synthesis of hybrids as the highest temperature used in the process is typically 40-60 °C. Therefore, a different precursor is needed that can incorporate calcium into the silica network and enhance the homogeneity of the glasses at low (room) temperature. In this work, calcium methoxyethoxide (CME) was used to synthesize sol-gel bioactive glasses with a range of final processing temperatures from 60 to 800 °C. Comparison is made between the use of CME and calcium chloride and calcium nitrate. Using advanced probe techniques, the temperature at which Ca is incorporated into the network was identified for 70S30C (70 mol % SiO(2), 30 mol % CaO) for each of the calcium precursors. When CaCl(2) was used, the Ca did not seem to enter the network at any of the temperatures used. In contrast, Ca from CME entered the silica network at room temperature, as confirmed by X-ray diffraction, (29)Si magic angle spinning nuclear magnetic resonance spectroscopy, and dissolution studies. CME should be used in preference to calcium salts for hybrid synthesis and may improve homogeneity of sol-gel glasses.

Surveillance technique for hybrid WDM/PS-PON using a tunable OTDR

NASA Astrophysics Data System (ADS)

Hann, Swook; Yoo, Jun-sang; Park, Chang-soo

2005-05-01

A surveillance technique for passive optical networks (PON) is presented. The technique is based on the remote sensing of fiber Bragg grating using a tunable OTDR. Hybrid architecture of WDM and passive splitter-PON can be analyzed by the surveillance method at the central office under in-service state of PON.

Modulation of molecular hybridization and charge screening in a carbon nanotube network channel using the electrical pulse method.

PubMed

Woo, Jun-Myung; Kim, Seok Hyang; Chun, Honnggu; Kim, Sung Jae; Ahn, Jinhong; Park, Young June

2013-09-21

In this paper, we investigate the effect of electrical pulse bias on DNA hybridization events in a biosensor platform, using a Carbon Nanotube Network (CNN) and Gold Nano Particles (GNP) as an electrical channel. The scheme provides both hybridization rate enhancement of bio molecules, and electrical measurement in a transient state to avoid the charge screening effect, thereby significantly improving the sensitivity. As an example, the probe DNA molecules oscillate with pulse trains, resulting in the enhancement of DNA hybridization efficiency, and accordingly of the sensor performances in Tris-EDTA (TE) buffer solution, by as much as over three times, compared to the non-biasing conditions. More importantly, a wide dynamic range of 10(6) (target-DNA concentration from 5 pM to 5 μM) is achieved in human serum. In addition, the pulse biasing method enables one to obtain the conductance change, before the ions within the Electrical Double Layer (EDL) are redistributed, to avoid the charge screening effect, leading to an additional sensitivity enhancement.

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

Neural-network hybrid control for antilock braking systems.

PubMed

Lin, Chih-Min; Hsu, C F

2003-01-01

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

Multi-element logic gates for trapped-ion qubits

NASA Astrophysics Data System (ADS)

Tan, T. R.; Gaebler, J. P.; Lin, Y.; Wan, Y.; Bowler, R.; Leibfried, D.; Wineland, D. J.

2015-12-01

Precision control over hybrid physical systems at the quantum level is important for the realization of many quantum-based technologies. In the field of quantum information processing (QIP) and quantum networking, various proposals discuss the possibility of hybrid architectures where specific tasks are delegated to the most suitable subsystem. For example, in quantum networks, it may be advantageous to transfer information from a subsystem that has good memory properties to another subsystem that is more efficient at transporting information between nodes in the network. For trapped ions, a hybrid system formed of different species introduces extra degrees of freedom that can be exploited to expand and refine the control of the system. Ions of different elements have previously been used in QIP experiments for sympathetic cooling, creation of entanglement through dissipation, and quantum non-demolition measurement of one species with another. Here we demonstrate an entangling quantum gate between ions of different elements which can serve as an important building block of QIP, quantum networking, precision spectroscopy, metrology, and quantum simulation. A geometric phase gate between a 9Be+ ion and a 25Mg+ ion is realized through an effective spin-spin interaction generated by state-dependent forces induced with laser beams. Combined with single-qubit gates and same-species entangling gates, this mixed-element entangling gate provides a complete set of gates over such a hybrid system for universal QIP. Using a sequence of such gates, we demonstrate a CNOT (controlled-NOT) gate and a SWAP gate. We further demonstrate the robustness of these gates against thermal excitation and show improved detection in quantum logic spectroscopy. We also observe a strong violation of a CHSH (Clauser-Horne-Shimony-Holt)-type Bell inequality on entangled states composed of different ion species.

Multi-element logic gates for trapped-ion qubits.

PubMed

Tan, T R; Gaebler, J P; Lin, Y; Wan, Y; Bowler, R; Leibfried, D; Wineland, D J

2015-12-17

Precision control over hybrid physical systems at the quantum level is important for the realization of many quantum-based technologies. In the field of quantum information processing (QIP) and quantum networking, various proposals discuss the possibility of hybrid architectures where specific tasks are delegated to the most suitable subsystem. For example, in quantum networks, it may be advantageous to transfer information from a subsystem that has good memory properties to another subsystem that is more efficient at transporting information between nodes in the network. For trapped ions, a hybrid system formed of different species introduces extra degrees of freedom that can be exploited to expand and refine the control of the system. Ions of different elements have previously been used in QIP experiments for sympathetic cooling, creation of entanglement through dissipation, and quantum non-demolition measurement of one species with another. Here we demonstrate an entangling quantum gate between ions of different elements which can serve as an important building block of QIP, quantum networking, precision spectroscopy, metrology, and quantum simulation. A geometric phase gate between a (9)Be(+) ion and a (25)Mg(+) ion is realized through an effective spin-spin interaction generated by state-dependent forces induced with laser beams. Combined with single-qubit gates and same-species entangling gates, this mixed-element entangling gate provides a complete set of gates over such a hybrid system for universal QIP. Using a sequence of such gates, we demonstrate a CNOT (controlled-NOT) gate and a SWAP gate. We further demonstrate the robustness of these gates against thermal excitation and show improved detection in quantum logic spectroscopy. We also observe a strong violation of a CHSH (Clauser-Horne-Shimony-Holt)-type Bell inequality on entangled states composed of different ion species.

Directly Grown Nanostructured Electrodes for High Volumetric Energy Density Binder-Free Hybrid Supercapacitors: A Case Study of CNTs//Li4Ti5O12

NASA Astrophysics Data System (ADS)

Zuo, Wenhua; Wang, Chong; Li, Yuanyuan; Liu, Jinping

2015-01-01

Hybrid supercapacitor (HSC), which typically consists of a Li-ion battery electrode and an electric double-layer supercapacitor electrode, has been extensively investigated for large-scale applications such as hybrid electric vehicles, etc. Its application potential for thin-film downsized energy storage systems that always prefer high volumetric energy/power densities, however, has not yet been explored. Herein, as a case study, we develop an entirely binder-free HSC by using multiwalled carbon nanotube (MWCNT) network film as the cathode and Li4Ti5O12 (LTO) nanowire array as the anode and study the volumetric energy storage capability. Both the electrode materials are grown directly on carbon cloth current collector, ensuring robust mechanical/electrical contacts and flexibility. Our 3 V HSC device exhibits maximum volumetric energy density of ~4.38 mWh cm-3, much superior to those of previous supercapacitors based on thin-film electrodes fabricated directly on carbon cloth and even comparable to the commercial thin-film lithium battery. It also has volumetric power densities comparable to that of the commercial 5.5 V/100 mF supercapacitor (can be operated within 3 s) and has excellent cycling stability (~92% retention after 3000 cycles). The concept of utilizing binder-free electrodes to construct HSC for thin-film energy storage may be readily extended to other HSC electrode systems.

Stability assessment of a multi-port power electronic interface for hybrid micro-grid applications

NASA Astrophysics Data System (ADS)

Shamsi, Pourya

Migration to an industrial society increases the demand for electrical energy. Meanwhile, social causes for preserving the environment and reducing pollutions seek cleaner forms of energy sources. Therefore, there has been a growth in distributed generation from renewable sources in the past decade. Existing regulations and power system coordination does not allow for massive integration of distributed generation throughout the grid. Moreover, the current infrastructures are not designed for interfacing distributed and deregulated generation. In order to remedy this problem, a hybrid micro-grid based on nano-grids is introduced. This system consists of a reliable micro-grid structure that provides a smooth transition from the current distribution networks to smart micro-grid systems. Multi-port power electronic interfaces are introduced to manage the local generation, storage, and consumption. Afterwards, a model for this micro-grid is derived. Using this model, the stability of the system under a variety of source and load induced disturbances is studied. Moreover, pole-zero study of the micro-grid is performed under various loading conditions. An experimental setup of this micro-grid is developed, and the validity of the model in emulating the dynamic behavior of the system is verified. This study provides a theory for a novel hybrid micro-grid as well as models for stability assessment of the proposed micro-grid.

An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking

NASA Astrophysics Data System (ADS)

Raihan A. V, Dilshad; Chakravorty, Suman

2018-03-01

Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, probabilistic estimation of space objects is made difficult by the non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present an unscented Kalman-particle hybrid filtering framework for recursive Bayesian estimation of space objects. The hybrid filtering scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, it updates the state probability density function (PDF) via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed filter transition scheme. To assess the performance of the hybrid filtering approach, we consider two test cases of space objects that are assumed to undergo full three dimensional orbital motion under the effects of J 2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.

Band alignment of semiconductors and insulators using dielectric-dependent hybrid functionals: Toward high-throughput evaluation

NASA Astrophysics Data System (ADS)

Hinuma, Yoyo; Kumagai, Yu; Tanaka, Isao; Oba, Fumiyasu

2017-02-01

The band alignment of prototypical semiconductors and insulators is investigated using first-principles calculations. A dielectric-dependent hybrid functional, where the nonlocal Fock exchange mixing is set at the reciprocal of the static electronic dielectric constant and the exchange correlation is otherwise treated as in the Perdew-Burke-Ernzerhof (PBE0) hybrid functional, is used as well as the Heyd-Scuseria-Ernzerhof (HSE06) hybrid and PBE semilocal functionals. In addition, these hybrid functionals are applied non-self-consistently to accelerate calculations. The systems considered include C and Si in the diamond structure, BN, AlP, AlAs, AlSb, GaP, GaAs, InP, ZnS, ZnSe, ZnTe, CdS, CdSe, and CdTe in the zinc-blende structure, MgO in the rocksalt structure, and GaN and ZnO in the wurtzite structure. Surface band positions with respect to the vacuum level, i.e., ionization potentials and electron affinities, and band offsets at selected zinc-blende heterointerfaces are evaluated as well as band gaps. The non-self-consistent approach speeds up hybrid functional calculations by an order of magnitude, while it is shown using HSE06 that the resultant band gaps and surface band positions are similar to the self-consistent results. The dielectric-dependent hybrid functional improves the band gaps and surface band positions of wide-gap systems over HSE06. The interfacial band offsets are predicted with a similar degree of precision. Overall, the performance of the dielectric-dependent hybrid functional is comparable to the G W0 approximation based on many-body perturbation theory in the prediction of band gaps and alignments for most systems. The present results demonstrate that the dielectric-dependent hybrid functional, particularly when applied non-self-consistently, is promising for applications to systematic calculations or high-throughput screening that demand both computational efficiency and sufficient accuracy.

40 CFR 1037.525 - Special procedures for testing hybrid vehicles with power take-off.

Code of Federal Regulations, 2014 CFR

2014-07-01

... of this section to allow testing hybrid vehicles other than electric-battery hybrids, consistent with... model, use good engineering judgment to select the vehicle type with the maximum number of PTO circuits... as needed to stabilize the battery at a full state of charge. For electric hybrid vehicles, we...

Hybrid Computation at Louisiana State University.

ERIC Educational Resources Information Center

Corripio, Armando B.

Hybrid computation facilities have been in operation at Louisiana State University since the spring of 1969. In part, they consist of an Electronics Associates, Inc. (EAI) Model 680 analog computer, an EAI Model 693 interface, and a Xerox Data Systems (XDS) Sigma 5 digital computer. The hybrid laboratory is used in a course on hybrid computation…

Comparison of fumonisin contamination using HPLC and ELISA methods in bt and near-isogenic maize hybrids infested with European corn borer or western bean cutworm.

PubMed

Bowers, Erin; Hellmich, Richard; Munkvold, Gary

2014-07-09

Field trials were conducted from 2007 to 2010 to compare grain fumonisin levels among non-Bt maize hybrids and Bt hybrids with transgenic protection against manual infestations of European corn borer (ECB) and Western bean cutworm (WBC). HPLC and ELISA were used to measure fumonisin levels. Results of the methods were highly correlated, but ELISA estimates were higher. Bt hybrids experienced less insect injury, Fusarium ear rot, and fumonisin contamination compared to non-Bt hybrids. WBC infestation increased fumonisin content compared to natural infestation in non-Bt and hybrids expressing Cry1Ab protein in five of eight possible comparisons; in Cry1F hybrids, WBC did not impact fumonisins. These results indicate that WBC is capable of increasing fumonisin levels in maize. Under WBC infestation, Cry1F mitigated this risk more consistently than Cry1Ab or non-Bt hybrids. Transgenically expressed Bt proteins active against multiple lepidopteran pests can provide broad, consistent reductions in the risk of fumonisin contamination.

Sorption Behavior of Compressed CO2 and CH4 on Ultrathin Hybrid Poly(POSS-imide) Layers.

PubMed

Raaijmakers, Michiel J T; Ogieglo, Wojciech; Wiese, Martin; Wessling, Matthias; Nijmeijer, Arian; Benes, Nieck E

2015-12-09

Sorption of compressed gases into thin polymeric films is essential for applications including gas sensors and membrane based gas separation. For glassy polymers, the sorption behavior is dependent on the nonequilibrium status of the polymer. The uptake of molecules by a polymer is generally accompanied by dilation, or swelling, of the polymer material. In turn, this dilation can result in penetrant induced plasticization and physical aging that affect the nonequilibrium status of the polymer. Here, we investigate the dilation and sorption behavior of ultrathin membrane layers of a hybrid inorganic-organic network material that consists of alternating polyhedral oligomeric silsesquioxane and imide groups, upon exposure to compressed carbon dioxide and methane. The imide precursor contains fluoroalkene groups that provide affinity toward carbon dioxide, while the octa-functionalized silsesquioxane provides a high degree of cross-linking. This combination allows for extremely high sorption capacities, while structural rearrangements of the network are hindered. We study the simultaneous uptake of gases and dilation of the thin films at high pressures using spectroscopic ellipsometry measurements. Ellipsometry provides the changes in both the refractive index and the film thickness, and allows for accurate quantification of sorption and swelling. In contrast, gravimetric and volumetric measurements only provide a single parameter; this does not allow an accurate correction for, for instance, the changes in buoyancy because of the extensive geometrical changes of highly swelling films. The sorption behavior of the ultrathin hybrid layers depends on the fluoroalkene group content. At low pressure, the apparent molar volume of the gases is low compared to the liquid molar volume of carbon dioxide and methane, respectively. At high gas concentrations in the polymer film, the apparent molar volume of carbon dioxide and methane exceeds that of the liquid molar volume, and approaches that of the gas phase. The high sorption capacity and reversible dilation characteristics of the presented materials provide new directions for applications including gas sensors and gas separation membranes.

Label-free detection of DNA hybridization using carbon nanotube network field-effect transistors

NASA Astrophysics Data System (ADS)

Star, Alexander; Tu, Eugene; Niemann, Joseph; Gabriel, Jean-Christophe P.; Joiner, C. Steve; Valcke, Christian

2006-01-01

We report carbon nanotube network field-effect transistors (NTNFETs) that function as selective detectors of DNA immobilization and hybridization. NTNFETs with immobilized synthetic oligonucleotides have been shown to specifically recognize target DNA sequences, including H63D single-nucleotide polymorphism (SNP) discrimination in the HFE gene, responsible for hereditary hemochromatosis. The electronic responses of NTNFETs upon single-stranded DNA immobilization and subsequent DNA hybridization events were confirmed by using fluorescence-labeled oligonucleotides and then were further explored for label-free DNA detection at picomolar to micromolar concentrations. We have also observed a strong effect of DNA counterions on the electronic response, thus suggesting a charge-based mechanism of DNA detection using NTNFET devices. Implementation of label-free electronic detection assays using NTNFETs constitutes an important step toward low-cost, low-complexity, highly sensitive and accurate molecular diagnostics. hemochromatosis | SNP | biosensor

The hybrid photonic planar integrated receiver with a polymer optical waveguide

NASA Astrophysics Data System (ADS)

Busek, Karel; Jerábek, Vitezslav; Armas Arciniega, Julio; Prajzler, Václav

2008-11-01

This article describes design of the photonic receiver composed of the system polymer planar waveguides, InGaAs p-i-n photodiode and integrated HBT amplifier on a low loss composite substrate. The photonic receiver was the main part of the hybrid integrated microwave optoelectronic transceiver TRx (transciever TRx) for the optical networks PON (passive optical networks) with FTTH (fiber-to-the-home) topology. In this article are presented the research results of threedimensional field between output facet of a optical waveguide and p-i-n photodiode. In terms of our research, there was optimized the optical coupling among the facet waveguide and pi-n photodiode and the electrical coupling among p-i-n photodiode and input of HBT amplifier. The hybrid planar lightwave circuit (PLC) of the transceiver TRx will be composed from a two parts - polymer optical waveguide including VHGT filter section and a optoelectronic microwave section.

Hybrid discrete/continuum algorithms for stochastic reaction networks

DOE PAGES

Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; ...

2014-10-22

Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker-Planck equation. The Fokker-Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components to avoid negative probability values. The numerical construction at the interface between the discretemore » and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. As a result, the performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.« less

Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network

PubMed Central

López-Caraballo, C. H.; Lazzús, J. A.; Salfate, I.; Rojas, P.; Rivera, M.; Palma-Chilla, L.

2015-01-01

An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σ N) from 0.01 to 0.1. PMID:26351449

Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network.

PubMed

López-Caraballo, C H; Lazzús, J A; Salfate, I; Rojas, P; Rivera, M; Palma-Chilla, L

2015-01-01

An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σ(N)) from 0.01 to 0.1.

Conductivity and properties of polysiloxane-polyether cluster-LiTFSI networks as hybrid polymer electrolytes

NASA Astrophysics Data System (ADS)

Boaretto, Nicola; Joost, Christine; Seyfried, Mona; Vezzù, Keti; Di Noto, Vito

2016-09-01

This report describes the synthesis and the properties of a series of polymer electrolytes, composed of a hybrid inorganic-organic matrix doped with LiTFSI. The matrix is based on ring-like oligo-siloxane clusters, bearing pendant, partially cross-linked, polyether chains. The dependency of the thermo-mechanic and of the transport properties on several structural parameters, such as polyether chains' length, cross-linkers' concentration, and salt concentration is studied. Altogether, the materials show good thermo-mechanical and electrochemical stabilities, with conductivities reaching, at best, 8·10-5 S cm-1 at 30 °C. In conclusion, the cell performances of one representative sample are shown. The scope of this report is to analyze the correlations between structure and properties in networked and hybrid polymer electrolytes. This could help the design of optimized polymer electrolytes for application in lithium metal batteries.

Regulatory divergence between parental alleles determines gene expression patterns in hybrids.

PubMed

Combes, Marie-Christine; Hueber, Yann; Dereeper, Alexis; Rialle, Stéphanie; Herrera, Juan-Carlos; Lashermes, Philippe

2015-03-29

Both hybridization and allopolyploidization generate novel phenotypes by conciliating divergent genomes and regulatory networks in the same cellular context. To understand the rewiring of gene expression in hybrids, the total expression of 21,025 genes and the allele-specific expression of over 11,000 genes were quantified in interspecific hybrids and their parental species, Coffea canephora and Coffea eugenioides using RNA-seq technology. Between parental species, cis- and trans-regulatory divergences affected around 32% and 35% of analyzed genes, respectively, with nearly 17% of them showing both. The relative importance of trans-regulatory divergences between both species could be related to their low genetic divergence and perennial habit. In hybrids, among divergently expressed genes between parental species and hybrids, 77% was expressed like one parent (expression level dominance), including 65% like C. eugenioides. Gene expression was shown to result from the expression of both alleles affected by intertwined parental trans-regulatory factors. A strong impact of C. eugenioides trans-regulatory factors on the upregulation of C. canephora alleles was revealed. The gene expression patterns appeared determined by complex combinations of cis- and trans-regulatory divergences. In particular, the observed biased expression level dominance seemed to be derived from the asymmetric effects of trans-regulatory parental factors on regulation of alleles. More generally, this study illustrates the effects of divergent trans-regulatory parental factors on the gene expression pattern in hybrids. The characteristics of the transcriptional response to hybridization appear to be determined by the compatibility of gene regulatory networks and therefore depend on genetic divergences between the parental species and their evolutionary history. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Chemical hybridizing agent SQ-1-induced male sterility in Triticum aestivum L.: a comparative analysis of the anther proteome.

PubMed

Liu, Hongzhan; Zhang, Gaisheng; Wang, Junsheng; Li, Jingjing; Song, Yulong; Qiao, Lin; Niu, Na; Wang, Junwei; Ma, Shoucai; Li, Lili

2018-01-05

Heterosis is widely used to increase the yield of many crops. However, as wheat is a self-pollinating crop, hybrid breeding is not so successful in this organism. Even though male sterility induced by chemical hybridizing agents is an important aspect of crossbreeding, the mechanisms by which these agents induce male sterility in wheat is not well understood. We performed proteomic analyses using the wheat Triticum aestivum L.to identify those proteins involved in physiological male sterility (PHYMS) induced by the chemical hybridizing agent CHA SQ-1. A total of 103 differentially expressed proteins were found by 2D-PAGE and subsequently identified by MALDI-TOF/TOF MS/MS. In general, these proteins had obvious functional tendencies implicated in carbohydrate metabolism, oxidative stress and resistance, protein metabolism, photosynthesis, and cytoskeleton and cell structure. In combination with phenotypic, tissue section, and bioinformatics analyses, the identified differentially expressed proteins revealed a complex network behind the regulation of PHYMS and pollen development. Accordingly, we constructed a protein network of male sterility in wheat, drawing relationships between the 103 differentially expressed proteins and their annotated biological pathways. To further validate our proposed protein network, we determined relevant physiological values and performed real-time PCR assays. Our proteomics based approach has enabled us to identify certain tendencies in PHYMS anthers. Anomalies in carbohydrate metabolism and oxidative stress, together with premature tapetum degradation, may be the cause behind carbohydrate starvation and male sterility in CHA SQ-1 treated plants. Here, we provide important insight into the mechanisms underlying CHA SQ-1-induced male sterility. Our findings have practical implications for the application of hybrid breeding in wheat.

Multiobjective optimization of hybrid regenerative life support technologies. Topic D: Technology Assessment

NASA Technical Reports Server (NTRS)

Manousiouthakis, Vasilios

1995-01-01

We developed simple mathematical models for many of the technologies constituting the water reclamation system in a space station. These models were employed for subsystem optimization and for the evaluation of the performance of individual water reclamation technologies, by quantifying their operational 'cost' as a linear function of weight, volume, and power consumption. Then we performed preliminary investigations on the performance improvements attainable by simple hybrid systems involving parallel combinations of technologies. We are developing a software tool for synthesizing a hybrid water recovery system (WRS) for long term space missions. As conceptual framework, we are employing the state space approach. Given a number of available technologies and the mission specifications, the state space approach would help design flowsheets featuring optimal process configurations, including those that feature stream connections in parallel, series, or recycles. We visualize this software tool to function as follows: given the mission duration, the crew size, water quality specifications, and the cost coefficients, the software will synthesize a water recovery system for the space station. It should require minimal user intervention. The following tasks need to be solved for achieving this goal: (1) formulate a problem statement that will be used to evaluate the advantages of a hybrid WRS over a single technology WBS; (2) model several WRS technologies that can be employed in the space station; (3) propose a recycling network design methodology (since the WRS synthesis task is a recycling network design problem, it is essential to employ a systematic method in synthesizing this network); (4) develop a software implementation for this design methodology, design a hybrid system using this software, and compare the resulting WRS with a base-case WRS; and (5) create a user-friendly interface for this software tool.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferrada, J.J.; Osborne-Lee, I.W.; Grizzaffi, P.A.

Expert systems are known to be useful in capturing expertise and applying knowledge to chemical engineering problems such as diagnosis, process control, process simulation, and process advisory. However, expert system applications are traditionally limited to knowledge domains that are heuristic and involve only simple mathematics. Neural networks, on the other hand, represent an emerging technology capable of rapid recognition of patterned behavior without regard to mathematical complexity. Although useful in problem identification, neural networks are not very efficient in providing in-depth solutions and typically do not promote full understanding of the problem or the reasoning behind its solutions. Hence, applicationsmore » of neural networks have certain limitations. This paper explores the potential for expanding the scope of chemical engineering areas where neural networks might be utilized by incorporating expert systems and neural networks into the same application, a process called hybridization. In addition, hybrid applications are compared with those using more traditional approaches, the results of the different applications are analyzed, and the feasibility of converting the preliminary prototypes described herein into useful final products is evaluated. 12 refs., 8 figs.« less

Towards secure quantum key distribution protocol for wireless LANs: a hybrid approach

NASA Astrophysics Data System (ADS)

Naik, R. Lalu; Reddy, P. Chenna

2015-12-01

The primary goals of security such as authentication, confidentiality, integrity and non-repudiation in communication networks can be achieved with secure key distribution. Quantum mechanisms are highly secure means of distributing secret keys as they are unconditionally secure. Quantum key distribution protocols can effectively prevent various attacks in the quantum channel, while classical cryptography is efficient in authentication and verification of secret keys. By combining both quantum cryptography and classical cryptography, security of communications over networks can be leveraged. Hwang, Lee and Li exploited the merits of both cryptographic paradigms for provably secure communications to prevent replay, man-in-the-middle, and passive attacks. In this paper, we propose a new scheme with the combination of quantum cryptography and classical cryptography for 802.11i wireless LANs. Since quantum cryptography is premature in wireless networks, our work is a significant step forward toward securing communications in wireless networks. Our scheme is known as hybrid quantum key distribution protocol. Our analytical results revealed that the proposed scheme is provably secure for wireless networks.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method

PubMed Central

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-01-01

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs. PMID:29113310

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

PubMed

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Novel organic–inorganic amorphous photoactive hybrid films with rare earth (Eu{sup 3+}, Tb{sup 3+}) covalently embedded into silicon–oxygen network via sol–gel process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Qiang; Sheng, Ye; Zheng, Keyan

2015-10-15

Highlights: • The hybrids are prepared through the hydrolysis and condensation process. • The hybrids are amorphous and heat stabilized. • The hybrids containing Eu{sup 3+} and Tb{sup 3+} show the typical red and green emissions. - Abstract: Novel organic–inorganic hybrid amorphous thin films were synthesized by linking lanthanide (Tb{sup 3+}, Eu{sup 3+}) complexes through 3,4-bis(3-(triethoxysilyl)propylcarbamoyloxy)benzoic acid using sol–gel method. These inorganic–organic hybrids were characterized in detail by Fourier transform infrared spectroscopy, wide angle X-ray diffraction, themogravimetric analysis, scanning electron microscope, and fluorescence spectra. The above research results indicate that the hybrids possess high thermal-stability, amorphous structure features and especiallymore » favorable luminescent performances, such as long luminescent decay lifetime, high quantum yield etc.« less

Integrating Emerging Topics through Online Team Design in a Hybrid Communication Networks Course: Interaction Patterns and Impact of Prior Knowledge

ERIC Educational Resources Information Center

Reisslein, Jana; Seeling, Patrick; Reisslein, Martin

2005-01-01

An important challenge in the introductory communication networks course in electrical and computer engineering curricula is to integrate emerging topics, such as wireless Internet access and network security, into the already content-intensive course. At the same time it is essential to provide students with experiences in online collaboration,…

Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

PubMed

Mammu, Aboobeker Sidhik Koyamparambil; Hernandez-Jayo, Unai; Sainz, Nekane; de la Iglesia, Idoia

2015-04-09

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

PubMed Central

Huang, Chao-Chi; Chiu, Yang-Hung; Wen, Chih-Yu

2014-01-01

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs. PMID:25350506

Mild in situ growth of platinum nanoparticles on multiwalled carbon nanotube-poly (vinyl alcohol) hydrogel electrode for glucose electrochemical oxidation

NASA Astrophysics Data System (ADS)

Liu, Shumin; Zheng, Yudong; Qiao, Kun; Su, Lei; Sanghera, Amendeep; Song, Wenhui; Yue, Lina; Sun, Yi

2015-12-01

This investigation describes an effective strategy to fabricate an electrochemically active hybrid hydrogel made from platinum nanoparticles that are highly dense, uniformly dispersed, and tightly embedded throughout the conducting hydrogel network for the electrochemical oxidation of glucose. A suspension of multiwalled carbon nanotubes and polyvinyl alcohol aqueous was coated on glassy carbon electrode by electrophoretic deposition and then physically crosslinked to form a three-dimensional porous conductive hydrogel network by a process of freezing and thawing. The network offered 3D interconnected mass-transport channels (around 200 nm) and confined nanotemplates for in situ growth of uniform platinum nanoparticles via the moderate reduction agent, ascorbic acid. The resulting hybrid hydrogel electrode membrane demonstrates an effective method for loading platinum nanoparticles on multiwalled carbon nanotubes by the electrostatic adsorption between multiwalled carbon nanotubes and platinum ions within porous hydrogel network. The average diameter of platinum nanoparticles is 37 ± 14 nm, which is less than the particle size by only using the moderate reduction agent. The hybrid hydrogel electrode membrane-coated glassy carbon electrode showed excellent electrocatalytic activity and good long-term stability toward glucose electrochemical oxidation. The glucose oxidation current exhibited a linear relationship with the concentration of glucose in the presence of chloride ions, promising for potential applications of implantable biofuel cells, biosensors, and electronic devices.

Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

PubMed

Bildirici, Melike; Ersin, Özgür

2014-01-01

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.

Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

PubMed Central

Bildirici, Melike; Ersin, Özgür

2014-01-01

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications. PMID:24977200

Self-consistent hybrid functionals for solids: a fully-automated implementation

NASA Astrophysics Data System (ADS)

Erba, A.

2017-08-01

A fully-automated algorithm for the determination of the system-specific optimal fraction of exact exchange in self-consistent hybrid functionals of the density-functional-theory is illustrated, as implemented into the public Crystal program. The exchange fraction of this new class of functionals is self-consistently updated proportionally to the inverse of the dielectric response of the system within an iterative procedure (Skone et al 2014 Phys. Rev. B 89, 195112). Each iteration of the present scheme, in turn, implies convergence of a self-consistent-field (SCF) and a coupled-perturbed-Hartree-Fock/Kohn-Sham (CPHF/KS) procedure. The present implementation, beside improving the user-friendliness of self-consistent hybrids, exploits the unperturbed and electric-field perturbed density matrices from previous iterations as guesses for subsequent SCF and CPHF/KS iterations, which is documented to reduce the overall computational cost of the whole process by a factor of 2.

A hybrid deep neural network and physically based distributed model for river stage prediction

NASA Astrophysics Data System (ADS)

hitokoto, Masayuki; sakuraba, Masaaki

2016-04-01

We developed the real-time river stage prediction model, using the hybrid deep neural network and physically based distributed model. As the basic model, 4 layer feed-forward artificial neural network (ANN) was used. As a network training method, the deep learning technique was applied. To optimize the network weight, the stochastic gradient descent method based on the back propagation method was used. As a pre-training method, the denoising autoencoder was used. Input of the ANN model is hourly change of water level and hourly rainfall, output data is water level of downstream station. In general, the desirable input of the ANN has strong correlation with the output. In conceptual hydrological model such as tank model and storage-function model, river discharge is governed by the catchment storage. Therefore, the change of the catchment storage, downstream discharge subtracted from rainfall, can be the potent input candidate of the ANN model instead of rainfall. From this point of view, the hybrid deep neural network and physically based distributed model was developed. The prediction procedure of the hybrid model is as follows; first, downstream discharge was calculated by the distributed model, and then estimates the hourly change of catchment storage form rainfall and calculated discharge as the input of the ANN model, and finally the ANN model was calculated. In the training phase, hourly change of catchment storage can be calculated by the observed rainfall and discharge data. The developed model was applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. The modeled catchment is 695 square km. For the training data, 5 water level gauging station and 14 rain-gauge station in the catchment was used. The training floods, superior 24 events, were selected during the period of 2005-2014. Prediction was made up to 6 hours, and 6 models were developed for each prediction time. To set the proper learning parameters and network architecture of the ANN model, sensitivity analysis was done by the case study approach. The prediction result was evaluated by the superior 4 flood events by the leave-one-out cross validation. The prediction result of the basic 4 layer ANN was better than the conventional 3 layer ANN model. However, the result did not reproduce well the biggest flood event, supposedly because the lack of the sufficient high-water level flood event in the training data. The result of the hybrid model outperforms the basic ANN model and distributed model, especially improved the performance of the basic ANN model in the biggest flood event.

Assessing the performance of self-consistent hybrid functional for band gap calculation in oxide semiconductors

NASA Astrophysics Data System (ADS)

He, Jiangang; Franchini, Cesare

2017-11-01

In this paper we assess the predictive power of the self-consistent hybrid functional scPBE0 in calculating the band gap of oxide semiconductors. The computational procedure is based on the self-consistent evaluation of the mixing parameter α by means of an iterative calculation of the static dielectric constant using the perturbation expansion after discretization method and making use of the relation \

Bio-inspired computational heuristics to study Lane-Emden systems arising in astrophysics model.

PubMed

Ahmad, Iftikhar; Raja, Muhammad Asif Zahoor; Bilal, Muhammad; Ashraf, Farooq

2016-01-01

This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane-Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.

An artificial neural network controller based on MPSO-BFGS hybrid optimization for spherical flying robot

NASA Astrophysics Data System (ADS)

Liu, Xiaolin; Li, Lanfei; Sun, Hanxu

2017-12-01

Spherical flying robot can perform various tasks in the complex and varied environment to reduce labor costs. However, it is difficult to guarantee the stability of the spherical flying robot in the case of strong coupling and time-varying disturbance. In this paper, an artificial neural network controller (ANNC) based on MPSO-BFGS hybrid optimization algorithm is proposed. The MPSO algorithm is used to optimize the initial weights of the controller to avoid the local optimal solution. The BFGS algorithm is introduced to improve the convergence ability of the network. We use Lyapunov method to analyze the stability of ANNC. The controller is simulated under the condition of nonlinear coupling disturbance. The experimental results show that the proposed controller can obtain the expected value in shoter time compared with the other considered methods.

Polyaniline nanowires-gold nanoparticles hybrid network based chemiresistive hydrogen sulfide sensor

NASA Astrophysics Data System (ADS)

Shirsat, Mahendra D.; Bangar, Mangesh A.; Deshusses, Marc A.; Myung, Nosang V.; Mulchandani, Ashok

2009-02-01

We report a sensitive, selective, and fast responding room temperature chemiresistive sensor for hydrogen sulfide detection and quantification using polyaniline nanowires-gold nanoparticles hybrid network. The sensor was fabricated by facile electrochemical technique. Initially, polyaniline nanowires with a diameter of 250-320 nm bridging the gap between a pair of microfabricated gold electrodes were synthesized using templateless electrochemical polymerization using a two step galvanostatic technique. Polyaniline nanowires were then electrochemically functionalized with gold nanoparticles using cyclic voltammetry technique. These chemiresistive sensors show an excellent limit of detection (0.1 ppb), wide dynamic range (0.1-100 ppb), and very good selectivity and reproducibility.

Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

PubMed

Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

2015-01-01

Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

Hybrid phase transition into an absorbing state: Percolation and avalanches

NASA Astrophysics Data System (ADS)

Lee, Deokjae; Choi, S.; Stippinger, M.; Kertész, J.; Kahng, B.

2016-04-01

Interdependent networks are more fragile under random attacks than simplex networks, because interlayer dependencies lead to cascading failures and finally to a sudden collapse. This is a hybrid phase transition (HPT), meaning that at the transition point the order parameter has a jump but there are also critical phenomena related to it. Here we study these phenomena on the Erdős-Rényi and the two-dimensional interdependent networks and show that the hybrid percolation transition exhibits two kinds of critical behaviors: divergence of the fluctuations of the order parameter and power-law size distribution of finite avalanches at a transition point. At the transition point global or "infinite" avalanches occur, while the finite ones have a power law size distribution; thus the avalanche statistics also has the nature of a HPT. The exponent βm of the order parameter is 1 /2 under general conditions, while the value of the exponent γm characterizing the fluctuations of the order parameter depends on the system. The critical behavior of the finite avalanches can be described by another set of exponents, βa and γa. These two critical behaviors are coupled by a scaling law: 1 -βm=γa .

Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-02-01

Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

GUI Type Fault Diagnostic Program for a Turboshaft Engine Using Fuzzy and Neural Networks

NASA Astrophysics Data System (ADS)

Kong, Changduk; Koo, Youngju

2011-04-01

The helicopter to be operated in a severe flight environmental condition must have a very reliable propulsion system. On-line condition monitoring and fault detection of the engine can promote reliability and availability of the helicopter propulsion system. A hybrid health monitoring program using Fuzzy Logic and Neural Network Algorithms can be proposed. In this hybrid method, the Fuzzy Logic identifies easily the faulted components from engine measuring parameter changes, and the Neural Networks can quantify accurately its identified faults. In order to use effectively the fault diagnostic system, a GUI (Graphical User Interface) type program is newly proposed. This program is composed of the real time monitoring part, the engine condition monitoring part and the fault diagnostic part. The real time monitoring part can display measuring parameters of the study turboshaft engine such as power turbine inlet temperature, exhaust gas temperature, fuel flow, torque and gas generator speed. The engine condition monitoring part can evaluate the engine condition through comparison between monitoring performance parameters the base performance parameters analyzed by the base performance analysis program using look-up tables. The fault diagnostic part can identify and quantify the single faults the multiple faults from the monitoring parameters using hybrid method.

A novel application of artificial neural network for wind speed estimation

NASA Astrophysics Data System (ADS)

Fang, Da; Wang, Jianzhou

2017-05-01

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.

Mean-field approximation for the Sznajd model in complex networks

NASA Astrophysics Data System (ADS)

Araújo, Maycon S.; Vannucchi, Fabio S.; Timpanaro, André M.; Prado, Carmen P. C.

2015-02-01

This paper studies the Sznajd model for opinion formation in a population connected through a general network. A master equation describing the time evolution of opinions is presented and solved in a mean-field approximation. Although quite simple, this approximation allows us to capture the most important features regarding the steady states of the model. When spontaneous opinion changes are included, a discontinuous transition from consensus to polarization can be found as the rate of spontaneous change is increased. In this case we show that a hybrid mean-field approach including interactions between second nearest neighbors is necessary to estimate correctly the critical point of the transition. The analytical prediction of the critical point is also compared with numerical simulations in a wide variety of networks, in particular Barabási-Albert networks, finding reasonable agreement despite the strong approximations involved. The same hybrid approach that made it possible to deal with second-order neighbors could just as well be adapted to treat other problems such as epidemic spreading or predator-prey systems.

Hybrid multiphoton volumetric functional imaging of large-scale bioengineered neuronal networks

NASA Astrophysics Data System (ADS)

Dana, Hod; Marom, Anat; Paluch, Shir; Dvorkin, Roman; Brosh, Inbar; Shoham, Shy

2014-06-01

Planar neural networks and interfaces serve as versatile in vitro models of central nervous system physiology, but adaptations of related methods to three dimensions (3D) have met with limited success. Here, we demonstrate for the first time volumetric functional imaging in a bioengineered neural tissue growing in a transparent hydrogel with cortical cellular and synaptic densities, by introducing complementary new developments in nonlinear microscopy and neural tissue engineering. Our system uses a novel hybrid multiphoton microscope design combining a 3D scanning-line temporal-focusing subsystem and a conventional laser-scanning multiphoton microscope to provide functional and structural volumetric imaging capabilities: dense microscopic 3D sampling at tens of volumes per second of structures with mm-scale dimensions containing a network of over 1,000 developing cells with complex spontaneous activity patterns. These developments open new opportunities for large-scale neuronal interfacing and for applications of 3D engineered networks ranging from basic neuroscience to the screening of neuroactive substances.

A Hybrid Key Management Scheme for WSNs Based on PPBR and a Tree-Based Path Key Establishment Method

PubMed Central

Zhang, Ying; Liang, Jixing; Zheng, Bingxin; Chen, Wei

2016-01-01

With the development of wireless sensor networks (WSNs), in most application scenarios traditional WSNs with static sink nodes will be gradually replaced by Mobile Sinks (MSs), and the corresponding application requires a secure communication environment. Current key management researches pay less attention to the security of sensor networks with MS. This paper proposes a hybrid key management schemes based on a Polynomial Pool-based key pre-distribution and Basic Random key pre-distribution (PPBR) to be used in WSNs with MS. The scheme takes full advantages of these two kinds of methods to improve the cracking difficulty of the key system. The storage effectiveness and the network resilience can be significantly enhanced as well. The tree-based path key establishment method is introduced to effectively solve the problem of communication link connectivity. Simulation clearly shows that the proposed scheme performs better in terms of network resilience, connectivity and storage effectiveness compared to other widely used schemes. PMID:27070624

Quantum Transduction with Adaptive Control

NASA Astrophysics Data System (ADS)

Zhang, Mengzhen; Zou, Chang-Ling; Jiang, Liang

2018-01-01

Quantum transducers play a crucial role in hybrid quantum networks. A good quantum transducer can faithfully convert quantum signals from one mode to another with minimum decoherence. Most investigations of quantum transduction are based on the protocol of direct mode conversion. However, the direct protocol requires the matching condition, which in practice is not always feasible. Here we propose an adaptive protocol for quantum transducers, which can convert quantum signals without requiring the matching condition. The adaptive protocol only consists of Gaussian operations, feasible in various physical platforms. Moreover, we show that the adaptive protocol can be robust against imperfections associated with finite squeezing, thermal noise, and homodyne detection, and it can be implemented to realize quantum state transfer between microwave and optical modes.

Quantum Transduction with Adaptive Control.

PubMed

Zhang, Mengzhen; Zou, Chang-Ling; Jiang, Liang

2018-01-12

Quantum transducers play a crucial role in hybrid quantum networks. A good quantum transducer can faithfully convert quantum signals from one mode to another with minimum decoherence. Most investigations of quantum transduction are based on the protocol of direct mode conversion. However, the direct protocol requires the matching condition, which in practice is not always feasible. Here we propose an adaptive protocol for quantum transducers, which can convert quantum signals without requiring the matching condition. The adaptive protocol only consists of Gaussian operations, feasible in various physical platforms. Moreover, we show that the adaptive protocol can be robust against imperfections associated with finite squeezing, thermal noise, and homodyne detection, and it can be implemented to realize quantum state transfer between microwave and optical modes.

Proceedings of the Workshop on Advanced Network and Technology Concepts for Mobile, Micro, and Personal Communications

NASA Technical Reports Server (NTRS)

Paul, Lori (Editor)

1991-01-01

The Workshop on Advanced Network and Technology Concepts for Mobile, Micro, and Personal Communications was held at NASA's JPL Laboratory on 30-31 May 1991. It provided a forum for reviewing the development of advanced network and technology concepts for turn-of-the-century telecommunications. The workshop was organized into three main categories: (1) Satellite-Based Networks (L-band, C-band, Ku-band, and Ka-band); (2) Terrestrial-Based Networks (cellular, CT2, PCN, GSM, and other networks); and (3) Hybrid Satellite/Terrestrial Networks. The proceedings contain presentation papers from each of the above categories.

Hybrid network modeling and the effect of image resolution on digitally-obtained petrophysical and two-phase flow properties

NASA Astrophysics Data System (ADS)

Aghaei, A.

2017-12-01

Digital imaging and modeling of rocks and subsequent simulation of physical phenomena in digitally-constructed rock models are becoming an integral part of core analysis workflows. One of the inherent limitations of image-based analysis, at any given scale, is image resolution. This limitation becomes more evident when the rock has multiple scales of porosity such as in carbonates and tight sandstones. Multi-scale imaging and constructions of hybrid models that encompass images acquired at multiple scales and resolutions are proposed as a solution to this problem. In this study, we investigate the effect of image resolution and unresolved porosity on petrophysical and two-phase flow properties calculated based on images. A helical X-ray micro-CT scanner with a high cone-angle is used to acquire digital rock images that are free of geometric distortion. To remove subjectivity from the analyses, a semi-automated image processing technique is used to process and segment the acquired data into multiple phases. Direct and pore network based models are used to simulate physical phenomena and obtain absolute permeability, formation factor and two-phase flow properties such as relative permeability and capillary pressure. The effect of image resolution on each property is investigated. Finally a hybrid network model incorporating images at multiple resolutions is built and used for simulations. The results from the hybrid model are compared against results from the model built at the highest resolution and those from laboratory tests.

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

PubMed Central

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Background Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Methods Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. Results The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Conclusion Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS. PMID:26270814

DNA-nanoparticle assemblies go organic: Macroscopic polymeric materials with nanosized features

PubMed Central

2012-01-01

Background One of the goals in the field of structural DNA nanotechnology is the use of DNA to build up 2- and 3-D nanostructures. The research in this field is motivated by the remarkable structural features of DNA as well as by its unique and reversible recognition properties. Nucleic acids can be used alone as the skeleton of a broad range of periodic nanopatterns and nanoobjects and in addition, DNA can serve as a linker or template to form DNA-hybrid structures with other materials. This approach can be used for the development of new detection strategies as well as nanoelectronic structures and devices. Method Here we present a new method for the generation of unprecedented all-organic conjugated-polymer nanoparticle networks guided by DNA, based on a hierarchical self-assembly process. First, microphase separation of amphiphilic block copolymers induced the formation of spherical nanoobjects. As a second ordering concept, DNA base pairing has been employed for the controlled spatial definition of the conjugated-polymer particles within the bulk material. These networks offer the flexibility and the diversity of soft polymeric materials. Thus, simple chemical methodologies could be applied in order to tune the network's electrical, optical and mechanical properties. Results and conclusions One- two- and three-dimensional networks have been successfully formed. Common to all morphologies is the integrity of the micelles consisting of DNA block copolymer (DBC), which creates an all-organic engineered network. PMID:22646980

Adaptive Code Division Multiple Access Protocol for Wireless Network-on-Chip Architectures

NASA Astrophysics Data System (ADS)

Vijayakumaran, Vineeth

Massive levels of integration following Moore's Law ushered in a paradigm shift in the way on-chip interconnections were designed. With higher and higher number of cores on the same die traditional bus based interconnections are no longer a scalable communication infrastructure. On-chip networks were proposed enabled a scalable plug-and-play mechanism for interconnecting hundreds of cores on the same chip. Wired interconnects between the cores in a traditional Network-on-Chip (NoC) system, becomes a bottleneck with increase in the number of cores thereby increasing the latency and energy to transmit signals over them. Hence, there has been many alternative emerging interconnect technologies proposed, namely, 3D, photonic and multi-band RF interconnects. Although they provide better connectivity, higher speed and higher bandwidth compared to wired interconnects; they also face challenges with heat dissipation and manufacturing difficulties. On-chip wireless interconnects is one other alternative proposed which doesn't need physical interconnection layout as data travels over the wireless medium. They are integrated into a hybrid NOC architecture consisting of both wired and wireless links, which provides higher bandwidth, lower latency, lesser area overhead and reduced energy dissipation in communication. However, as the bandwidth of the wireless channels is limited, an efficient media access control (MAC) scheme is required to enhance the utilization of the available bandwidth. This thesis proposes using a multiple access mechanism such as Code Division Multiple Access (CDMA) to enable multiple transmitter-receiver pairs to send data over the wireless channel simultaneously. It will be shown that such a hybrid wireless NoC with an efficient CDMA based MAC protocol can significantly increase the performance of the system while lowering the energy dissipation in data transfer. In this work it is shown that the wireless NoC with the proposed CDMA based MAC protocol outperformed the wired counterparts and several other wireless architectures proposed in literature in terms of bandwidth and packet energy dissipation. Significant gains were observed in packet energy dissipation and bandwidth even with scaling the system to higher number of cores. Non-uniform traffic simulations showed that the proposed CDMA-WiNoC was consistent in bandwidth across all traffic patterns. It is also shown that the CDMA based MAC scheme does not introduce additional reliability concerns in data transfer over the on-chip wireless interconnects.

Modelling the solar wind interaction with Mercury by a quasi-neutral hybrid model

NASA Astrophysics Data System (ADS)

Kallio, E.; Janhunen, P.

2003-11-01

Quasi-neutral hybrid model is a self-consistent modelling approach that includes positively charged particles and an electron fluid. The approach has received an increasing interest in space plasma physics research because it makes it possible to study several plasma physical processes that are difficult or impossible to model by self-consistent fluid models, such as the effects associated with the ions’ finite gyroradius, the velocity difference between different ion species, or the non-Maxwellian velocity distribution function. By now quasi-neutral hybrid models have been used to study the solar wind interaction with the non-magnetised Solar System bodies of Mars, Venus, Titan and comets. Localized, two-dimensional hybrid model runs have also been made to study terrestrial dayside magnetosheath. However, the Hermean plasma environment has not yet been analysed by a global quasi-neutral hybrid model.

Efficient Dependency Computation for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications

DTIC Science & Technology

2014-10-02

intervals (Neil, Tailor, Marquez, Fenton , & Hear, 2007). This is cumbersome, error prone and usually inaccurate. Even though a universal framework...Science. Neil, M., Tailor, M., Marquez, D., Fenton , N., & Hear. (2007). Inference in Bayesian networks using dynamic discretisation. Statistics

Layer-based buffer aware rate adaptation design for SHVC video streaming

NASA Astrophysics Data System (ADS)

Gudumasu, Srinivas; Hamza, Ahmed; Asbun, Eduardo; He, Yong; Ye, Yan

2016-09-01

This paper proposes a layer based buffer aware rate adaptation design which is able to avoid abrupt video quality fluctuation, reduce re-buffering latency and improve bandwidth utilization when compared to a conventional simulcast based adaptive streaming system. The proposed adaptation design schedules DASH segment requests based on the estimated bandwidth, dependencies among video layers and layer buffer fullness. Scalable HEVC video coding is the latest state-of-art video coding technique that can alleviate various issues caused by simulcast based adaptive video streaming. With scalable coded video streams, the video is encoded once into a number of layers representing different qualities and/or resolutions: a base layer (BL) and one or more enhancement layers (EL), each incrementally enhancing the quality of the lower layers. Such layer based coding structure allows fine granularity rate adaptation for the video streaming applications. Two video streaming use cases are presented in this paper. The first use case is to stream HD SHVC video over a wireless network where available bandwidth varies, and the performance comparison between proposed layer-based streaming approach and conventional simulcast streaming approach is provided. The second use case is to stream 4K/UHD SHVC video over a hybrid access network that consists of a 5G millimeter wave high-speed wireless link and a conventional wired or WiFi network. The simulation results verify that the proposed layer based rate adaptation approach is able to utilize the bandwidth more efficiently. As a result, a more consistent viewing experience with higher quality video content and minimal video quality fluctuations can be presented to the user.

Adaptive Topological Configuration of an Integrated Circuit/Packet-Switched Computer Network.

DTIC Science & Technology

1984-01-01

Gitman et al. [45] state that there are basically two approaches to the integrated network design problem: (1) solve the link/capacity problem for...1972), 1385-1397. 33. Frank, H., and Gitman , I. Economic analysis of integrated voice and data networks: a case study. Proc. of IEEE 66 , 11 (Nov. 1978...1974), 1074-1079. 45. Gitman , I., Hsieh, W., and Occhiogrosso, B. J. Analysis and design of hybrid switching networks. IEEE Trans. on Comm. Com-29

Mobilizing Homeless Youth for HIV Prevention: A Social Network Analysis of the Acceptability of a Face-to-Face and Online Social Networking Intervention

ERIC Educational Resources Information Center

Rice, Eric; Tulbert, Eve; Cederbaum, Julie; Adhikari, Anamika Barman; Milburn, Norweeta G.

2012-01-01

The objective of the study is to use social network analysis to examine the acceptability of a youth-led, hybrid face-to-face and online social networking HIV prevention program for homeless youth. Seven peer leaders (PLs) engaged face-to-face homeless youth (F2F) in the creation of digital media projects (e.g. You Tube videos). PL and F2F…

Interests diffusion in social networks

NASA Astrophysics Data System (ADS)

D'Agostino, Gregorio; D'Antonio, Fulvio; De Nicola, Antonio; Tucci, Salvatore

2015-10-01

We provide a model for diffusion of interests in Social Networks (SNs). We demonstrate that the topology of the SN plays a crucial role in the dynamics of the individual interests. Understanding cultural phenomena on SNs and exploiting the implicit knowledge about their members is attracting the interest of different research communities both from the academic and the business side. The community of complexity science is devoting significant efforts to define laws, models, and theories, which, based on acquired knowledge, are able to predict future observations (e.g. success of a product). In the mean time, the semantic web community aims at engineering a new generation of advanced services by defining constructs, models and methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to come from a hybrid approach merging the disciplines above. Along this line, this work focuses on the propagation of individual interests in social networks. The proposed framework consists of the following main components: a method to gather information about the members of the social networks; methods to perform some semantic analysis of the Domain of Interest; a procedure to infer members' interests; and an interests evolution theory to predict how the interests propagate in the network. As a result, one achieves an analytic tool to measure individual features, such as members' susceptibilities and authorities. Although the approach applies to any type of social network, here it is has been tested against the computer science research community. The DBLP (Digital Bibliography and Library Project) database has been elected as test-case since it provides the most comprehensive list of scientific production in this field.

Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children.

PubMed

Berchialla, Paola; Scarinzi, Cecilia; Snidero, Silvia; Gregori, Dario

2016-08-01

Risk Assessment is the systematic study of decisions subject to uncertain consequences. An increasing interest has been focused on modeling techniques like Bayesian Networks since their capability of (1) combining in the probabilistic framework different type of evidence including both expert judgments and objective data; (2) overturning previous beliefs in the light of the new information being received and (3) making predictions even with incomplete data. In this work, we proposed a comparison among Bayesian Networks and other classical Quantitative Risk Assessment techniques such as Neural Networks, Classification Trees, Random Forests and Logistic Regression models. Hybrid approaches, combining both Classification Trees and Bayesian Networks, were also considered. Among Bayesian Networks, a clear distinction between purely data-driven approach and combination of expert knowledge with objective data is made. The aim of this paper consists in evaluating among this models which best can be applied, in the framework of Quantitative Risk Assessment, to assess the safety of children who are exposed to the risk of inhalation/insertion/aspiration of consumer products. The issue of preventing injuries in children is of paramount importance, in particular where product design is involved: quantifying the risk associated to product characteristics can be of great usefulness in addressing the product safety design regulation. Data of the European Registry of Foreign Bodies Injuries formed the starting evidence for risk assessment. Results showed that Bayesian Networks appeared to have both the ease of interpretability and accuracy in making prediction, even if simpler models like logistic regression still performed well. © The Author(s) 2013.

Evaluation results after seven years of operation for the permanent Hellenic Seismological Network of Crete (HSNC).

NASA Astrophysics Data System (ADS)

Vallianatos, F.; Hloupis, G.; Papadopoulos, I.

2012-04-01

The Hellenic arc and the adjacent areas of the Greek mainland are the most active in western Eurasia and some of the most seismically active zones of the world. The seismicity of South Aegean is extremely high and is characterised by the frequent occurrence of large shallow and intermediate depth earthquakes. Until 2004, the installed seismological stations from several providers (NOA, GEOFON, MEDNET) provide average interstation distance around 130km resulting to catalogues with minimum magnitude of completeness (Mc) equals to 3.7. Towards to the direction of providing dense and state of the art instrumental coverage of seismicity in the South Aegean, HSNC begun its operation in 2004. Today it consists of (12) permanent seismological stations equipped with short period and broadband seismographs coupled with 3rd generation 24bit data loggers as well as from (2) accelerographs . The addition of HSNC along with combined use of all the active networks in South Aegean area (NOA, GEOFON, AUTH) decrease the average interstation distance to 60km and provide catalogues with Mc≥3.2. Data transmission and telemetry is implemented by a hybrid network consisting of dedicated wired ADSL links as well as VSAT links by using a unique private satellite hub. Real time data spread over collaborating networks (AUTH) and laboratories (Department of Earth Science - UCL) while at the same time, events are appended automatically and manually to EMSC database. Additional value to the network is provided by means of prototype systems which deployed in-situ for the purposes of: a) Acquiring aftershock data in the minimum time after main event. This is a mobile seismological network called RaDeSeis (Rapid Deployment Seismological network) which consists of a central station acting also as the central communication hub and wifi coupled mobile stations. b) The development of dedicated hardware and software solutions for rapid installation times (around 1 hour for each station) leading to infrastructure that can provide data for aftershock studies can be initiated after a few hours. c) Real time algorithms for Early Warning Purposes. These include the rapid estimation of magnitude and epicentre after 5secs from the initial P-wave arrival. Acknowledgements. This work was supported in part by the ARCHIMEDES III Program of the Ministry of Education of Greece and the European Union in the framework of the project entitled "Intredisciplinary Multi-Scale Research of Earthquake Physics and Seismotectonics at the front of the Hellenic Arc (IMPACT ARC)"

Bioactive Molecules Release and Cellular Responses of Alginate-Tricalcium Phosphate Particles Hybrid Gel

PubMed Central

Bang, Sumi; Zhang, Shengmin

2017-01-01

In this article, a hybrid gel has been developed using sodium alginate (Alg) and α-tricalcium phosphate (α-TCP) particles through ionic crosslinking process for the application in bone tissue engineering. The effects of pH and composition of the gel on osteoblast cells (MC3T3) response and bioactive molecules release have been evaluated. At first, a slurry of Alg and α-TCP has been prepared using an ultrasonicator for the homogeneous distribution of α-TCP particles in the Alg network and to achieve adequate interfacial interaction between them. After that, CaCl2 solution has been added to the slurry so that ionic crosslinked gel (Alg-α-TCP) is formed. The developed hybrid gel has been physico-chemically characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and a swelling study. The SEM analysis depicted the presence of α-TCP micro-particles on the surface of the hybrid gel, while cross-section images signified that the α-TCP particles are fully embedded in the porous gel network. Different % swelling ratio at pH 4, 7 and 7.4 confirmed the pH responsiveness of the Alg-α-TCP gel. The hybrid gel having lower % α-TCP particles showed higher % swelling at pH 7.4. The hybrid gel demonstrated a faster release rate of bovine serum albumin (BSA), tetracycline (TCN) and dimethyloxalylglycine (DMOG) at pH 7.4 and for the grade having lower % α-TCP particles. The MC3T3 cells are viable inside the hybrid gel, while the rate of cell proliferation is higher at pH 7.4 compared to pH 7. The in vitro cytotoxicity analysis using thiazolyl blue tetrazolium bromide (MTT), bromodeoxyuridine (BrdU) and neutral red assays ascertained that the hybrid gel is non-toxic for MC3T3 cells. The experimental results implied that the non-toxic and biocompatible Alg-α-TCP hybrid gel could be used as scaffold in bone tissue engineering. PMID:29135939

A Quantum Network with Atoms and Photons

DTIC Science & Technology

2016-09-01

The long - term goal is to entangle distant atomic memories between ARL and JQI, and explore the possibility of entangling hybrid quantum memories . 2...ARL) environment. The long - term goal is to achieve a quantum repeater network capability for the US Army. Initially, a quantum channel between ARL and...SUBJECT  TERMS Quantum, atoms, photons, entanglement, teleportation, communications, network, memory 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

A Quantum Network with Atoms and Photons

DTIC Science & Technology

2016-09-30

The long - term goal is to entangle distant atomic memories between ARL and JQI, and explore the possibility of entangling hybrid quantum memories . 2...ARL) environment. The long - term goal is to achieve a quantum repeater network capability for the US Army. Initially, a quantum channel between ARL and...SUBJECT  TERMS Quantum, atoms, photons, entanglement, teleportation, communications, network, memory 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

The physics of teams: Interdependence, measurable entropy and computational emotion

NASA Astrophysics Data System (ADS)

Lawless, William F.

2017-08-01

Most of the social sciences, including psychology, economics and subjective social network theory, are modeled on the individual, leaving the field not only a-theoretical, but also inapplicable to a physics of hybrid teams, where hybrid refers to arbitrarily combining humans, machines and robots into a team to perform a dedicated mission (e.g., military, business, entertainment) or to solve a targeted problem (e.g., with scientists, engineers, entrepreneurs). As a common social science practice, the ingredient at the heart of the social interaction, interdependence, is statistically removed prior to the replication of social experiments; but, as an analogy, statistically removing social interdependence to better study the individual is like statistically removing quantum effects as a complication to the study of the atom. Further, in applications of Shannon’s information theory to teams, the effects of interdependence are minimized, but even there, interdependence is how classical information is transmitted. Consequently, numerous mistakes are made when applying non-interdependent models to policies, the law and regulations, impeding social welfare by failing to exploit the power of social interdependence. For example, adding redundancy to human teams is thought by subjective social network theorists to improve the efficiency of a network, easily contradicted by our finding that redundancy is strongly associated with corruption in non-free markets. Thus, built atop the individual, most of the social sciences, economics and social network theory have little if anything to contribute to the engineering of hybrid teams. In defense of the social sciences, the mathematical physics of interdependence is elusive, non-intuitive and non-rational. However, by replacing determinism with bistable states, interdependence at the social level mirrors entanglement at the quantum level, suggesting the applicability of quantum tools for social science. We report how our quantum-like models capture some of the essential aspects of interdependence, a tool for the metrics of hybrid teams; as an example, we find additional support for our model of the solution to the open problem of team size. We also report on progress with the theory of computational emotion for hybrid teams, linking it qualitatively to the second law of thermodynamics. We conclude that the science of interdependence

Prediction of the elastic modulus of wood flour/kenaf fibre/polypropylene hybrid composites

Treesearch

Jamal Mirbagheri; Mehdi Tajvidi; Ismaeil Ghasemi; John C. Hermanson

2007-01-01

The prediction of the elastic modulus of short natural fibre hybrid composites has been investigated by using the properties of the pure composites through the rule of hybrid mixtures (RoHM) equation. In this equation, a hybrid natural fibre composite assumed as a system consisting of two separate single systems, namely particle/polymer and short-fibre/polymer systems...

49 CFR 572.31 - General description.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., titled “Sign Convention for Vehicle Crash Testing”, dated 1994-12. (6) Exterior dimensions of the Hybrid... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) ANTHROPOMORPHIC TEST DEVICES Hybrid III Test Dummy § 572.31 General description. (a) The Hybrid III 50th percentile size dummy consists of components and...

Effectiveness of Simulation in a Hybrid and Online Networking Course.

ERIC Educational Resources Information Center

Cameron, Brian H.

2003-01-01

Reports on a study that compares the performance of students enrolled in two sections of a Web-based computer networking course: one utilizing a simulation package and the second utilizing a static, graphical software package. Analysis shows statistically significant improvements in performance in the simulation group compared to the…

A Case Study: To Internet or Not To Internet.

ERIC Educational Resources Information Center

Carman, Jared; Boynton, Doug

1997-01-01

Interactive multimedia training can be delivered via CD-ROM, hard drive, local area networks (LAN), wide area networks (WAN), Intranet, Internet and hybrid systems. This article presents a case study of how two companies (Los Angeles Times and Allen Communication) evaluated alternative delivery systems, chose one, and implemented multimedia…

Home and School Technology: Wired versus Wireless.

ERIC Educational Resources Information Center

Van Horn, Royal

2001-01-01

Presents results of informal research on smart homes and appliances, structured home wiring, whole-house audio/video distribution, hybrid cable, and wireless networks. Computer network wiring is tricky to install unless all-in-one jacketed cable is used. Wireless phones help installers avoid pre-wiring problems in homes and schools. (MLH)

Development of a space-systems network testbed

NASA Technical Reports Server (NTRS)

Lala, Jaynarayan; Alger, Linda; Adams, Stuart; Burkhardt, Laura; Nagle, Gail; Murray, Nicholas

1988-01-01

This paper describes a communications network testbed which has been designed to allow the development of architectures and algorithms that meet the functional requirements of future NASA communication systems. The central hardware components of the Network Testbed are programmable circuit switching communication nodes which can be adapted by software or firmware changes to customize the testbed to particular architectures and algorithms. Fault detection, isolation, and reconfiguration has been implemented in the Network with a hybrid approach which utilizes features of both centralized and distributed techniques to provide efficient handling of faults within the Network.

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced State-Awareness of Resilient Hybrid Energy System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ondrej Linda; Dumidu Wijayasekara; Milos Manic

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a Fuzzy-Neural Data Fusion Engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFEmore » is composed of a three-layered system consisting of: 1) traditional threshold based alarms, 2) anomalous behavior detector using self-organizing fuzzy logic system, and 3) artificial neural network based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory’s (INL) hybrid energy system facility know as HYTEST. Experimental results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold based alarm systems.« less

New evidence for hybrid zones of forest and savanna elephants in Central and West Africa.

PubMed

Mondol, Samrat; Moltke, Ida; Hart, John; Keigwin, Michael; Brown, Lisa; Stephens, Matthew; Wasser, Samuel K

2015-12-01

The African elephant consists of forest and savanna subspecies. Both subspecies are highly endangered due to severe poaching and habitat loss, and knowledge of their population structure is vital to their conservation. Previous studies have demonstrated marked genetic and morphological differences between forest and savanna elephants, and despite extensive sampling, genetic evidence of hybridization between them has been restricted largely to a few hybrids in the Garamba region of northeastern Democratic Republic of Congo (DRC). Here, we present new genetic data on hybridization from previously unsampled areas of Africa. Novel statistical methods applied to these data identify 46 hybrid samples--many more than have been previously identified--only two of which are from the Garamba region. The remaining 44 are from three other geographically distinct locations: a major hybrid zone along the border of the DRC and Uganda, a second potential hybrid zone in Central African Republic and a smaller fraction of hybrids in the Pendjari-Arli complex of West Africa. Most of the hybrids show evidence of interbreeding over more than one generation, demonstrating that hybrids are fertile. Mitochondrial and Y chromosome data demonstrate that the hybridization is bidirectional, involving males and females from both subspecies. We hypothesize that the hybrid zones may have been facilitated by poaching and habitat modification. The localized geography and rarity of hybrid zones, their possible facilitation from human pressures, and the high divergence and genetic distinctness of forest and savanna elephants throughout their ranges, are consistent with calls for separate species classification. © 2015 John Wiley & Sons Ltd.

Actor Diversity and Interactions in the Development of Banana Hybrid Varieties in Uganda: Implications for Technology Uptake

ERIC Educational Resources Information Center

Sanya, Losira Nasirumbi; Sseguya, Haroon; Kyazze, Florence Birungi; Baguma, Yona; Kibwika, Paul

2018-01-01

Purpose: We examine the nature of networks through which new hybrid banana varieties (HBVs) in Uganda are developed, and how different actors engage in the technology development process. Design/methodology/approach: We collected the data through 20 key informant interviews and 5 focus group discussions with actors involved in the process. We…

Binder-free flexible LiMn2O4/carbon nanotube network as high power cathode for rechargeable hybrid aqueous battery

NASA Astrophysics Data System (ADS)

Zhu, Xiao; Wu, Xianwen; Doan, The Nam Long; Tian, Ye; Zhao, Hongbin; Chen, P.

2016-09-01

Highly flexible LiMn2O4/carbon nanotube (CNT) electrodes are developed and used as a high power cathode for the Rechargeable Hybrid Aqueous Battery (ReHAB). LiMn2O4 particles are entangled into CNT networks, forming a self-supported free-standing hybrid films. Such hybrid films can be used as electrodes of ARLB without using any additional binders. The binder-free LiMn2O4/CNT electrode exhibits good mechanical properties, high conductivity, and effective charge transport. High-rate capability and high cycling stability are obtained. Typically, the LiMn2O4/CNT electrode achieves a discharge capacity of 72 mAh g-1 at the large-current of 20 C (1 C = 120 mAh g-1), and exhibits good cycling performance and high reversibility: Coulombic efficiency of almost 100% over 300 charge-discharge cycles at 4 C. By reducing the weight, and improving the large-current rate capability simultaneously, the LiMn2O4/CNT electrode can highly enhance the energy/power density of ARLB and hold potential to be used in ultrathin, lightweight electronic devices.

System and Method for Network Bandwidth, Buffers and Timing Management Using Hybrid Scheduling of Traffic with Different Priorities and Guarantees

NASA Technical Reports Server (NTRS)

Bonk, Ted (Inventor); Hall, Brendan (Inventor); Smithgall, William Todd (Inventor); Varadarajan, Srivatsan (Inventor); DeLay, Benjamin F. (Inventor)

2017-01-01

Systems and methods for network bandwidth, buffers and timing management using hybrid scheduling of traffic with different priorities and guarantees are provided. In certain embodiments, a method of managing network scheduling and configuration comprises, for each transmitting end station, reserving one exclusive buffer for each virtual link to be transmitted from the transmitting end station; for each receiving end station, reserving exclusive buffers for each virtual link to be received at the receiving end station; and for each switch, reserving a exclusive buffer for each virtual link to be received at an input port of the switch. The method further comprises determining if each respective transmitting end station, receiving end station, and switch has sufficient capability to support the reserved buffers; and reporting buffer infeasibility if each respective transmitting end station, receiving end station, and switch does not have sufficient capability to support the reserved buffers.

Synchronization of hybrid coupled reaction-diffusion neural networks with time delays via generalized intermittent control with spacial sampled-data.

PubMed

Lu, Binglong; Jiang, Haijun; Hu, Cheng; Abdurahman, Abdujelil

2018-05-04

The exponential synchronization of hybrid coupled reaction-diffusion neural networks with time delays is discussed in this article. At first, a generalized intermittent control with spacial sampled-data is introduced, which is intermittent in time and data sampling in space. This type of control strategy not only can unify the traditional periodic intermittent control and the aperiodic case, but also can lower the update rate of the controller in both temporal and spatial domains. Next, based on the designed control protocol and the Lyapunov-Krasovskii functional approach, some novel and readily verified criteria are established to guarantee the exponential synchronization of the considered networks. These criteria depend on the diffusion coefficients, coupled strengths, time delays as well as control parameters. Finally, the effectiveness of the proposed control strategy is shown by a numerical example. Copyright © 2018 Elsevier Ltd. All rights reserved.

Copula Entropy coupled with Wavelet Neural Network Model for Hydrological Prediction

NASA Astrophysics Data System (ADS)

Wang, Yin; Yue, JiGuang; Liu, ShuGuang; Wang, Li

2018-02-01

Artificial Neural network(ANN) has been widely used in hydrological forecasting. in this paper an attempt has been made to find an alternative method for hydrological prediction by combining Copula Entropy(CE) with Wavelet Neural Network(WNN), CE theory permits to calculate mutual information(MI) to select Input variables which avoids the limitations of the traditional linear correlation(LCC) analysis. Wavelet analysis can provide the exact locality of any changes in the dynamical patterns of the sequence Coupled with ANN Strong non-linear fitting ability. WNN model was able to provide a good fit with the hydrological data. finally, the hybrid model(CE+WNN) have been applied to daily water level of Taihu Lake Basin, and compared with CE ANN, LCC WNN and LCC ANN. Results showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models.

Novel WRM-based architecture of hybrid PON featuring online access and full-fiber-fault protection for smart grid

NASA Astrophysics Data System (ADS)

Li, Xingfeng; Gan, Chaoqin; Liu, Zongkang; Yan, Yuqi; Qiao, HuBao

2018-01-01

In this paper, a novel architecture of hybrid PON for smart grid is proposed by introducing a wavelength-routing module (WRM). By using conventional optical passive components, a WRM with M ports is designed. The symmetry and passivity of the WRM makes it be easily integrated and very cheap in practice. Via the WRM, two types of network based on different ONU-interconnected manner can realize online access. Depending on optical switches and interconnecting fibers, full-fiber-fault protection and dynamic bandwidth allocation are realized in these networks. With the help of amplitude modulation, DPSK modulation and RSOA technology, wavelength triple-reuse is achieved. By means of injecting signals into left and right branches in access ring simultaneously, the transmission delay is decreased. Finally, the performance analysis and simulation of the network verifies the feasibility of the proposed architecture.

Networked Operations of Hybrid Radio Optical Communications Satellites

NASA Technical Reports Server (NTRS)

Hylton, Alan; Raible, Daniel

2014-01-01

In order to address the increasing communications needs of modern equipment in space, and to address the increasing number of objects in space, NASA is demonstrating the potential capability of optical communications for both deep space and near-Earth applications. The Integrated Radio Optical Communications (iROC) is a hybrid communications system that capitalizes on the best of both the optical and RF domains while using each technology to compensate for the other's shortcomings. Specifically, the data rates of the optical links can be higher than their RF counterparts, whereas the RF links have greater link availability. The focus of this paper is twofold: to consider the operations of one or more iROC nodes from a networking point of view, and to suggest specific areas of research to further the field. We consider the utility of Disruption Tolerant Networking (DTN) and the Virtual Mission Operation Center (VMOC) model.

Recognition of Handwritten Arabic words using a neuro-fuzzy network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boukharouba, Abdelhak; Bennia, Abdelhak

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descentmore » learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.« less

Highly sensitive piezo-resistive graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone composites with improved conductive network construction.

PubMed

Zhao, Hang; Bai, Jinbo

2015-05-13

The constructions of internal conductive network are dependent on microstructures of conductive fillers, determining various electrical performances of composites. Here, we present the advanced graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone (GCHs/PDMS) composites with high piezo-resistive performance. GCH particles were synthesized by the catalyst chemical vapor deposition approach. The synthesized GCHs can be well dispersed in the matrix through the mechanical blending process. Due to the exfoliated GNP and aligned CNTs coupling structure, the flexible composite shows an ultralow percolation threshold (0.64 vol %) and high piezo-resistive sensitivity (gauge factor ∼ 10(3) and pressure sensitivity ∼ 0.6 kPa(-1)). Slight motions of finger can be detected and distinguished accurately using the composite film as a typical wearable sensor. These results indicate that designing the internal conductive network could be a reasonable strategy to improve the piezo-resistive performance of composites.

Systematic Hybrid Network Scheduling for Multiple Traffic Classes with Host Timing and Phase Constraints

NASA Technical Reports Server (NTRS)

Hall, Brendan (Inventor); Bonk, Ted (Inventor); Varadarajan, Srivatsan (Inventor); Smithgall, William Todd (Inventor); DeLay, Benjamin F. (Inventor)

2017-01-01

Systems and methods for systematic hybrid network scheduling for multiple traffic classes with host timing and phase constraints are provided. In certain embodiments, a method of scheduling communications in a network comprises scheduling transmission of virtual links pertaining to a first traffic class on a global schedule to coordinate transmission of the virtual links pertaining to the first traffic class across all transmitting end stations on the global schedule; and scheduling transmission of each virtual link pertaining to a second traffic class on a local schedule of the respective transmitting end station from which each respective virtual link pertaining to the second traffic class is transmitted such that transmission of each virtual link pertaining to the second traffic class is coordinated only at the respective end station from which each respective virtual link pertaining to the second traffic class is transmitted.

Hybrid expert system for decision supporting in the medical area: complexity and cognitive computing.

PubMed

Brasil, L M; de Azevedo, F M; Barreto, J M

2001-09-01

This paper proposes a hybrid expert system (HES) to minimise some complexity problems pervasive to the artificial intelligence such as: the knowledge elicitation process, known as the bottleneck of expert systems; the model choice for knowledge representation to code human reasoning; the number of neurons in the hidden layer and the topology used in the connectionist approach; the difficulty to obtain the explanation on how the network arrived to a conclusion. Two algorithms applied to developing of HES are also suggested. One of them is used to train the fuzzy neural network and the other to obtain explanations on how the fuzzy neural network attained a conclusion. To overcome these difficulties the cognitive computing was integrated to the developed system. A case study is presented (e.g. epileptic crisis) with the problem definition and simulations. Results are also discussed.

Regulatory divergence of X-linked genes and hybrid male sterility in mice.

PubMed

Oka, Ayako; Shiroishi, Toshihiko

2014-01-01

Postzygotic reproductive isolation is the reduction of fertility or viability in hybrids between genetically diverged populations. One example of reproductive isolation, hybrid male sterility, may be caused by genetic incompatibility between diverged genetic factors in two distinct populations. Genetic factors involved in hybrid male sterility are disproportionately located on the X chromosome. Recent studies showing the evolutionary divergence in gene regulatory networks or epigenetic effects suggest that the genetic incompatibilities occur at much broader levels than had previously been thought (e.g., incompatibility of protein-protein interactions). The latest studies suggest that evolutionary divergence of transcriptional regulation causes genetic incompatibilities in hybrid animals, and that such incompatibilities preferentially involve X-linked genes. In this review, we focus on recent progress in understanding hybrid sterility in mice, including our studies, and we discuss the evolutionary significance of regulatory divergence for speciation.

Faculty Experiences in Higher Education Institutions Teaching Hybrid Courses

ERIC Educational Resources Information Center

Calderon, Blanca I. Rodriguez

2013-01-01

This qualitative phenomenological study investigated how professors perceive the effectiveness of hybrid courses at the university level. The study gathered data related to professor's experiences that could give insight about the factors encouraging the development of hybrid instruction in higher education. The targeted population consisted of…

Network motifs that stabilize the hybrid epithelial/mesenchymal phenotype

NASA Astrophysics Data System (ADS)

Jolly, Mohit Kumar; Jia, Dongya; Tripathi, Satyendra; Hanash, Samir; Mani, Sendurai; Ben-Jacob, Eshel; Levine, Herbert

Epithelial to Mesenchymal Transition (EMT) and its reverse - MET - are hallmarks of cancer metastasis. While transitioning between E and M phenotypes, cells can also attain a hybrid epithelial/mesenchymal (E/M) phenotype that enables collective cell migration as a cluster of Circulating Tumor Cells (CTCs). These clusters can form 50-times more tumors than individually migrating CTCs, underlining their importance in metastasis. However, this hybrid E/M phenotype has been hypothesized to be only a transient one that is attained en route EMT. Here, via mathematically modeling, we identify certain `phenotypic stability factors' that couple with the core three-way decision-making circuit (miR-200/ZEB) and can maintain or stabilize the hybrid E/M phenotype. Further, we show experimentally that this phenotype can be maintained stably at a single-cell level, and knockdown of these factors impairs collective cell migration. We also show that these factors enable the association of hybrid E/M with high stemness or tumor-initiating potential. Finally, based on these factors, we deduce specific network motifs that can maintain the E/M phenotype. Our framework can be used to elucidate the effect of other players in regulating cellular plasticity during metastasis. This work was supported by NSF PHY-1427654 (Center for Theoretical Biological Physics) and the CPRIT Scholar in Cancer Research of the State of Texas at Rice University.

Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles.

PubMed

Tóth-Nagy, Csaba; Conley, John J; Jarrett, Ronald P; Clark, Nigel N

2006-07-01

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.

Multiscale Hy3S: hybrid stochastic simulation for supercomputers.

PubMed

Salis, Howard; Sotiropoulos, Vassilios; Kaznessis, Yiannis N

2006-02-24

Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users create biological systems and analyze data. We demonstrate the accuracy and efficiency of Hy3S with examples, including a large-scale system benchmark and a complex bistable biochemical network with positive feedback. The software itself is open-sourced under the GPL license and is modular, allowing users to modify it for their own purposes. Hy3S is a powerful suite of simulation programs for simulating the stochastic dynamics of networks of biochemical reactions. Its first public version enables computational biologists to more efficiently investigate the dynamics of realistic biological systems.

Application of Two-Dimensional AWE Algorithm in Training Multi-Dimensional Neural Network Model

DTIC Science & Technology

2003-07-01

hybrid scheme . the general neural network method (Table 3.1). The training process of the software- ACKNOWLEDGMENT "Neuralmodeler" is shown in Fig. 3.2...engineering. Artificial neural networks (ANNs) have emerged Training a neural network model is the key of as a powerful technique for modeling general neural...coefficients am, the derivatives method of moments (MoM). The variables in the of matrix I have to be generated . A closed form model are frequency

Genetic algorithm for neural networks optimization

NASA Astrophysics Data System (ADS)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

Proliferation of Observables and Measurement in Quantum-Classical Hybrids

NASA Astrophysics Data System (ADS)

Elze, Hans-Thomas

2012-01-01

Following a review of quantum-classical hybrid dynamics, we discuss the ensuing proliferation of observables and relate it to measurements of (would-be) quantum mechanical degrees of freedom performed by (would-be) classical ones (if they were separable). Hybrids consist in coupled classical (CL) and quantum mechanical (QM) objects. Numerous consistency requirements for their description have been discussed and are fulfilled here. We summarize a representation of quantum mechanics in terms of classical analytical mechanics which is naturally extended to QM-CL hybrids. This framework allows for superposition, separable, and entangled states originating in the QM sector, admits experimenter's "Free Will", and is local and nonsignaling. Presently, we study the set of hybrid observables, which is larger than the Cartesian product of QM and CL observables of its components; yet it is smaller than a corresponding product of all-classical observables. Thus, quantumness and classicality infect each other.

A generalized model via random walks for information filtering

NASA Astrophysics Data System (ADS)

Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng

2016-08-01

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation.

Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

PubMed

Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

2015-09-01

In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

The autophagy interaction network of the aging model Podospora anserina.

PubMed

Philipp, Oliver; Hamann, Andrea; Osiewacz, Heinz D; Koch, Ina

2017-03-27

Autophagy is a conserved molecular pathway involved in the degradation and recycling of cellular components. It is active either as response to starvation or molecular damage. Evidence is emerging that autophagy plays a key role in the degradation of damaged cellular components and thereby affects aging and lifespan control. In earlier studies, it was found that autophagy in the aging model Podospora anserina acts as a longevity assurance mechanism. However, only little is known about the individual components controlling autophagy in this aging model. Here, we report a biochemical and bioinformatics study to detect the protein-protein interaction (PPI) network of P. anserina combining experimental and theoretical methods. We constructed the PPI network of autophagy in P. anserina based on the corresponding networks of yeast and human. We integrated PaATG8 interaction partners identified in an own yeast two-hybrid analysis using ATG8 of P. anserina as bait. Additionally, we included age-dependent transcriptome data. The resulting network consists of 89 proteins involved in 186 interactions. We applied bioinformatics approaches to analyze the network topology and to prove that the network is not random, but exhibits biologically meaningful properties. We identified hub proteins which play an essential role in the network as well as seven putative sub-pathways, and interactions which are likely to be evolutionary conserved amongst species. We confirmed that autophagy-associated genes are significantly often up-regulated and co-expressed during aging of P. anserina. With the present study, we provide a comprehensive biological network of the autophagy pathway in P. anserina comprising PPI and gene expression data. It is based on computational prediction as well as experimental data. We identified sub-pathways, important hub proteins, and evolutionary conserved interactions. The network clearly illustrates the relation of autophagy to aging processes and enables further specific studies to understand autophagy and aging in P. anserina as well as in other systems.

Hybrid inversions of CO2 fluxes at regional scale applied to network design

NASA Astrophysics Data System (ADS)

Kountouris, Panagiotis; Gerbig, Christoph; -Thomas Koch, Frank

2013-04-01

Long term observations of atmospheric greenhouse gas measuring stations, located at representative regions over the continent, improve our understanding of greenhouse gas sources and sinks. These mixing ratio measurements can be linked to surface fluxes by atmospheric transport inversions. Within the upcoming years new stations are to be deployed, which requires decision making tools with respect to the location and the density of the network. We are developing a method to assess potential greenhouse gas observing networks in terms of their ability to recover specific target quantities. As target quantities we use CO2 fluxes aggregated to specific spatial and temporal scales. We introduce a high resolution inverse modeling framework, which attempts to combine advantages from pixel based inversions with those of a carbon cycle data assimilation system (CCDAS). The hybrid inversion system consists of the Lagrangian transport model STILT, the diagnostic biosphere model VPRM and a Bayesian inversion scheme. We aim to retrieve the spatiotemporal distribution of net ecosystem exchange (NEE) at a high spatial resolution (10 km x 10 km) by inverting for spatially and temporally varying scaling factors for gross ecosystem exchange (GEE) and respiration (R) rather than solving for the fluxes themselves. Thus the state space includes parameters for controlling photosynthesis and respiration, but unlike in a CCDAS it allows for spatial and temporal variations, which can be expressed as NEE(x,y,t) = λG(x,y,t) GEE(x,y,t) + λR(x,y,t) R(x,y,t) . We apply spatially and temporally correlated uncertainties by using error covariance matrices with non-zero off-diagonal elements. Synthetic experiments will test our system and select the optimal a priori error covariance by using different spatial and temporal correlation lengths on the error statistics of the a priori covariance and comparing the optimized fluxes against the 'known truth'. As 'known truth' we use independent fluxes generated from a different biosphere model (BIOME-BGC). Initially we perform single-station inversions for Ochsenkopf tall tower located in Germany. Further expansion of the inversion framework to multiple stations and its application to network design will address the questions of how well a set of network stations can constrain a given target quantity, and whether there are objective criteria to select an optimal configuration for new stations that maximizes the uncertainty reduction.

Fuzzy and neural control

NASA Technical Reports Server (NTRS)

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.

A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

PubMed Central

Wen, Hui; Xie, Weixin; Pei, Jihong

2016-01-01

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

Integrated RF/Optical Interplanetary Networking Preliminary Explorations and Empirical Results

NASA Technical Reports Server (NTRS)

Raible, Daniel E.; Hylton, Alan G.

2012-01-01

Over the last decade interplanetary telecommunication capabilities have been significantly expanded--specifically in support of the Mars exploration rover and lander missions. NASA is continuing to drive advances in new, high payoff optical communications technologies to enhance the network to Gbps performance from Mars, and the transition from technology demonstration to operational system is examined through a hybrid RF/optical approach. Such a system combines the best features of RF and optical communications considering availability and performance to realize a dual band trunk line operating within characteristic constraints. Disconnection due to planetary obscuration and solar conjunction, link delays, timing, ground terminal mission congestion and scheduling policy along with space and atmospheric weather disruptions all imply the need for network protocol solutions to ultimately manage the physical layer in a transparent manner to the end user. Delay Tolerant Networking (DTN) is an approach under evaluation which addresses these challenges. A multi-hop multi-path hybrid RF and optical test bed has been constructed to emulate the integrated deep space network and to support protocol and hardware refinement. Initial experimental results characterize several of these challenges and evaluate the effectiveness of DTN as a solution to mitigate them.

Target-based drug discovery for [Formula: see text]-globin disorders: drug target prediction using quantitative modeling with hybrid functional Petri nets.

PubMed

Mehraei, Mani; Bashirov, Rza; Tüzmen, Şükrü

2016-10-01

Recent molecular studies provide important clues into treatment of [Formula: see text]-thalassemia, sickle-cell anaemia and other [Formula: see text]-globin disorders revealing that increased production of fetal hemoglobin, that is normally suppressed in adulthood, can ameliorate the severity of these diseases. In this paper, we present a novel approach for drug prediction for [Formula: see text]-globin disorders. Our approach is centered upon quantitative modeling of interactions in human fetal-to-adult hemoglobin switch network using hybrid functional Petri nets. In accordance with the reverse pharmacology approach, we pose a hypothesis regarding modulation of specific protein targets that induce [Formula: see text]-globin and consequently fetal hemoglobin. Comparison of simulation results for the proposed strategy with the ones obtained for already existing drugs shows that our strategy is the optimal as it leads to highest level of [Formula: see text]-globin induction and thereby has potential beneficial therapeutic effects on [Formula: see text]-globin disorders. Simulation results enable verification of model coherence demonstrating that it is consistent with qPCR data available for known strategies and/or drugs.

Hybrid Texts: Fifth Graders, Rap Music, and Writing

ERIC Educational Resources Information Center

Christianakis, Mary

2011-01-01

Consistent with a sociocritical frame and the analytic tools of hybridity theory, this article explicates how urban fifth-grade children made language hybrids using rap and poetry to participate in classroom literacy. Ethnographic data from a yearlong study illustrate two key findings. First, standards-based and canon-driven writing models…

Accurate modeling of switched reluctance machine based on hybrid trained WNN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Shoujun, E-mail: sunnyway@nwpu.edu.cn; Ge, Lefei; Ma, Shaojie

2014-04-15

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, themore » nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.« less

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture.

PubMed

Chen, Yingyi; Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Daoliang

2018-01-01

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.

PubMed

Amudha, P; Karthik, S; Sivakumari, S

2015-01-01

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

PubMed Central

Amudha, P.; Karthik, S.; Sivakumari, S.

2015-01-01

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625

Manipulation of novel nano-prodrug composed of organic pigment-based hybrid network and its optical uses.

PubMed

Zhou, Zhan; Zheng, Yuhui; Cheng, Cheng Zhang; Wen, Jiajia; Wang, Qianming

2017-01-01

Here we developed the first case of pyropheophorbide-a-loaded PEGylated-hybrid carbon nanohorns (CNH-Pyro) to study tumor targeting therapy. During incubation with living cells, CNH-Pyro exhibited very intense red emissions. The intracellular imaging results were carried out by flow cytometry based on four different kinds of cell lines (including three adherent cell lines and one suspension cell line). Compared with free pyropheophorbide-a, CNH-Pyro demonstrated enhanced photodynamic tumor ablation efficiency during in vitro experiments due to improved biocompatibility of the hybrid nanomaterial and the photothermal therapy effect derived from carbon-network structure. Trypan blue staining experiments supported that the cell fate was dependent on the synergistic effects of both CNH-Pyro and laser irradiations. These results indicated that the chlorin-entrapped carbon nanohorns could provide powerful delivery vehicles for increasing photodynamic efficacy and possess early identification of the disease. Copyright © 2016 Elsevier B.V. All rights reserved.

A new method for 3D thinning of hybrid shaped porous media using artificial intelligence. Application to trabecular bone.

PubMed

Jennane, Rachid; Aufort, Gabriel; Benhamou, Claude Laurent; Ceylan, Murat; Ozbay, Yüksel; Ucan, Osman Nuri

2012-04-01

Curve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper presents an alternative to compute a hybrid shape-dependent skeleton and its application to porous media. The resulting skeleton combines 2D surfaces and 1D curves to represent respectively the plate-shaped and rod-shaped parts of the object. For this purpose, a new technique based on neural networks is proposed: cascade combinations of complex wavelet transform (CWT) and complex-valued artificial neural network (CVANN). The ability of the skeleton to characterize hybrid shaped porous media is demonstrated on a trabecular bone sample. Results show that the proposed method achieves high accuracy rates about 99.78%-99.97%. Especially, CWT (2nd level)-CVANN structure converges to optimum results as high accuracy rate-minimum time consumption.

Photon hopping and nanowire based hybrid plasmonic waveguide and ring-resonator

PubMed Central

Gu, Zhiyuan; Liu, Shuai; Sun, Shang; Wang, Kaiyang; Lyu, Quan; Xiao, Shumin; Song, Qinghai

2015-01-01

Nanowire based hybrid plasmonic structure plays an important role in achieving nanodevices, especially for the wide band-gap materials. However, the conventional schemes of nanowire based devices such as nano-resonators are usually isolated from the integrated nano-network and have extremely low quality (Q) factors. Here we demonstrate the transmission of waves across a gap in hybrid plasmonic waveguide, which is termed as “photon hopping”. Based on the photon hopping, we show that the emissions from nanodevices can be efficiently collected and conducted by additional nanowires. The collection ratio can be higher than 50% for a wide range of separation distance, transverse shift, and tilt. Moreover, we have also explored the possibility of improving performances of individual devices by nano-manipulating the nanowire to a pseudo-ring. Our calculations show that both Q factor and Purcell factor have been increased by more than an order of magnitude. We believe that our researches will be essential to forming nanolasers and the following nano-networks.

Construction of multifunctional MoSe2 hybrid towards the simultaneous improvements in fire safety and mechanical property of polymer.

PubMed

Wang, Junling; Ma, Chao; Mu, Xiaowei; Cai, Wei; Liu, Longxiang; Zhou, Xia; Hu, Weizhao; Hu, Yuan

2018-06-15

Organic modification of MoSe 2 sheets is firstly achieved by Atherton-Todd reaction, aiming at the acquisition of multifunctional MoSe 2 hybrid. Simultaneous enhancements in fire safety and mechanical property of thermalplastic polyurethane (TPU) are obtained with the presence of this hybrid. Strong interfacial interactions between the functionalized MoSe 2 sheets and TPU can be obtained, making more efficient load transfer from the weak polymer chains to the robust sheets. Besides, more coherent barrier network may be formed in polymer matrix, restraining the diffusion of decomposed fragments and reducing the supply for combustion fuel. Consequently, the decreases in heat release are observed for polymer composites. Notably, the releases of toxic gases, such as HCN and CO, are also suppressed by this barrier network, resulting in the reductions in fire toxicity. This work may open a new door for the functionalization of MoSe 2 sheets and evoke significant developments in its promising applications. Copyright © 2018. Published by Elsevier B.V.