In this year there is the 40th anniversary of the first publication of plant nitric oxide (NO) emission by Lowell Klepper. In the decades since then numerous milestone discoveries have revealed that ...NO is a multifunctional molecule in plant cells regulating both plant development and stress responses. Apropos of the anniversary, these authors aim to review and discuss the developments of past concepts in plant NO research related to NO metabolism, NO signaling, NO's action in plant growth and in stress responses and NO's interactions with other reactive compounds. Despite the long-lasting research efforts and the accumulating experimental evidences numerous questions are still needed to be answered, thus future challenges and research directions have also been drawn up.
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•A review of the milestones from the last forty years of NO research in plants.•An overview of the generation and metabolism of NO in plants.•An overview of how NO controls plant reproduction, development and stress responses.•A review of the roles of NO in plant cell signaling.•A discussion of the future challenges and research directions of plant NO research.
Single-Atom Spin-Flip Spectroscopy Heinrich, A. J.; Gupta, J. A.; Lutz, C. P. ...
Science (American Association for the Advancement of Science),
10/2004, Letnik:
306, Številka:
5695
Journal Article
Recenzirano
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We demonstrate the ability to measure the energy required to flip the spin of single adsorbed atoms. A low-temperature, high-magnetic field scanning tunneling microscope was used to measure the spin ...excitation spectra of individual manganese atoms adsorbed on Al2O3islands on a NiAl surface. We find pronounced variations of the spin-flip spectra for manganese atoms in different local environments.
Fresh water goes global Vörösmarty, C. J.; Hoekstra, A. Y.; Bunn, S. E. ...
Science (American Association for the Advancement of Science),
07/2015, Letnik:
349, Številka:
6247
Journal Article
Cytoplasmic male sterility and its fertility restoration via nuclear genes offer the possibility to understand the role of mitochondria during microsporogenesis. In most cases rearrangements in the ...mitochondrial DNA involving known mitochondrial genes as well as unknown sequences result in the creation of new chimeric open reading frames, which encode proteins containing transmembrane domains. So far, most of the CMS systems have been characterized via restriction fragment polymorphisms followed by transcript analysis. However, whole mitochondrial genome sequence analyses comparing male sterile and fertile cytoplasm open options for deeper insights into mitochondrial genome rearrangements. We more and more start to unravel how mitochondria are involved in triggering death of the male reproductive organs. Reduced levels of ATP accompanied by increased concentrations of reactive oxygen species, which are produced more under conditions of mitochondrial dysfunction, seem to play a major role in the fate of pollen production. Nuclear genes, so called restorer-of-fertility are able to restore the male fertility. Fertility restoration can occur via pentatricopeptide repeat (PPR) proteins or via different mechanisms involving non-PPR proteins.
Local manipulation of electric fields at the atomic scale may enable new methods for quantum transport and creates new opportunities for field control of ferromagnetism and spin-based quantum ...information processing in semiconductors. We used a scanning tunneling microscope to position charged arsenic (As) vacancies in the gallium arsenide 110 GaAs(110) surface with atomic precision, thereby tuning the local electrostatic field experienced by single manganese (Mn) acceptors. The effects of this field are quantified by measuring the shift of an acceptor state within the band gap of GaAs. Experiments with varying tip-induced band-bending conditions suggest a large binding energy for surface-layer Mn, which is reduced by direct Coulomb repulsion when the As vacancy is moved nearby.
This paper proposes a diagonal recurrent neural network (DRNN) based identification model for approximating the unknown dynamics of the nonlinear plants. The proposed model offers deeper memory and a ...simpler structure. Thereafter, we have developed a dynamic back-propagation learning algorithm for tuning the parameters of DRNN. Further, to guarantee the faster convergence and stability of the overall system, dynamic (adaptive) learning rates are developed in the sense of Lyapunov stability method. The proposed scheme is also compared with multi-layer feed forward neural network (MLFFNN) and radial basis function network (RBFN) based identification models. Numerical experiments reveal that DRNN has performed much better in approximating the dynamics of the plant and have also shown more robustness toward system uncertainties.
In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural ...network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller.
•DRNN is successfully applied to control non linear dynamical systems (both SISO and MIMO systems).•Lyapunov stability criterion is used to derive weight update rule.•Learning ability of DRNN is tested and compared with MLFFNN and FCRNN.•Robustness of DRNN, FCRNN and MLFFNN is tested and compared. Both parameter variations and disturbance signal impact are considered.•Structure of DRNN is compared with MLFFNN and FCRNN in terms of dynamical behavior and count of weights.
•Shape based clustering model is proposed for solar radiation prediction.•Effect of various combinations of meteorological parameters is analyzed.•Combination of models give better result in ...comparison to single model.•Sunshine duration is prime parameter for prediction.•Wind speed has least effect on prediction.
Estimation of solar radiation is of considerable importance because of the increasing requirement for the design, optimization and performance evaluation of the solar energy systems. This paper presents the development of pattern similarity based clustering algorithm and its application in solar radiation estimation. In the present work continuous density, Hidden Markov Model (HMM) with Pearson R model is utilized for the extraction of shape based clusters from the input meteorological parameters and it is then processed by the Generalized Fuzzy Model (GFM) to accurately estimate the solar radiation. Instead of using distance function as an index of similarity here shape/patterns of the data vectors are used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance based clustering approaches. The estimation method used here exploits the pattern identification prowess of the HMM for cluster selection and generalization and nonlinear modeling capabilities of GFM to predict the solar radiation. The data of solar radiation and various meteorological parameters (sun shine hour, ambient temperature, relative humidity, wind speed and atmospheric pressure) to carry out the present work is taken from the comprehensive weather monitoring station made at Solar Energy Centre, Gurgaon, India. To consider the effect of each meteorological parameter on the estimation of solar radiation the proposed model is applied on 15 different sets comprising of various combinations of input meteorological parameters. The meteorological data of three years from 2009 to 2011 (915days) is used to estimate the solar radiation. Out of these 915days data, the first 750days data is used for the training of the proposed paradigm and rest 165days data is used for validating the model. The results of estimation using all the sets of various combination of meteorological parameter are analyzed and it is found that the sunshine duration is the prime parameter for the estimation of solar radiation. The next important parameter, which influences the estimation of solar radiation, is temperature followed by relative humidity, atmospheric pressure and wind speed. It is interesting to note that worse results are obtained for the sets which are not using sunshine duration as an input. The best performance is achieved by the set which uses all the parameters except the wind speed. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation co-efficient (R-value) of the proposed paradigm for the best performing combination of meteorological parameter are 7.9124, 3.0083 and 0.9921 respectively which shows that the proposed model results are in good agreement with the actual measured solar radiation.
The present work is aimed at the design of Levenberg–Marquardt (LM) and adaptive linear network (ADALINE) based soft sensors and their application in inferential control of a multicomponent ...distillation process. Further the ADALINE sensor is trained online using past measurements, to adapt the changes in the inputs and is termed as dynamic ADALINE (D-ADALINE) sensor. The soft sensors are then used in the control loop to obtain LM based inferential controller (LMIC), ADALINE based inferential controller (ADIC) and D-ADALINE based inferential controller (DADIC) for the process. The performance of dynamic controller is also analyzed for different inputs and sampling intervals. The comparison of results shows the efficient and robust prediction capability of D-ADALINE sensor and hence DADIC proves to be the best controller.
► The LM neural network and adaptive linear network based soft sensors are designed. ► The ADALINE sensor is trained online to obtain the dynamic ADALINE soft sensor. ► The designed soft sensors are used to obtain inferential controllers, i.e., LMIC, ADIC and DADIC. ► The performance of DADIC is analyzed for appropriate inputs and sampling intervals.
In this paper, a comparative study is performed to test the approximation ability of different neural network structures. It involves three neural networks multilayer feedforward neural network ...(MLFFNN), diagonal recurrent neural network (DRNN), and nonlinear autoregressive with exogenous inputs (NARX) neural network. Their robustness is also tested and compared when the system is subjected to parameter variations and disturbance signals. Further, dynamic back-propagation algorithm is used to update the parameters associated with these neural networks. Four dynamical systems of different complexities including motor-driven robotic link are considered on which the comparative study is performed. The simulation results show the superior performance of DRNN identification model over NARX and MLFFNN identification models.
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