Knockout of Auxin Response Factor SlARF4 Improves Tomato Resistance to Water Deficit

Auxin response factors (ARFs) play important roles in various plant physiological processes; however, knowledge of the exact role of ARFs in plant responses to water deficit is limited. In this study, SlARF4, a member of the ARF family, was functionally characterized under water deficit. Real-time fluorescence quantitative polymerase chain reaction (PCR) and β-glucuronidase (GUS) staining showed that water deficit and abscisic acid (ABA) treatment reduced the expression of SlARF4. SlARF4 was expressed in the vascular bundles and guard cells of tomato stomata. Loss of function of SlARF4 (arf4) by using Clustered Regularly Interspaced Short Palindromic Repeats/Cas 9 (CRISPR/Cas 9) technology enhanced plant resistance to water stress and rehydration ability. The arf4 mutant plants exhibited curly leaves and a thick stem. Malondialdehyde content was significantly lower in arf4 mutants than in wildtype plants under water stress; furthermore, arf4 mutants showed higher content of antioxidant substances, superoxide dismutase, actual photochemical efficiency of photosystem II (PSII), and catalase activities. Stomatal and vascular bundle morphology was changed in arf4 mutants. We identified 628 differentially expressed genes specifically expressed under water deficit in arf4 mutants; six of these genes, including ABA signaling pathway-related genes, were differentially expressed between the wildtype and arf4 mutants under water deficit and unlimited water supply. Auxin responsive element (AuxRE) elements were found in these genes’ promoters indicating that SlARF4 participates in ABA signaling pathways by regulating the expression of SlABI5/ABF and SCL3, thereby influencing stomatal morphology and vascular bundle development and ultimately improving plant resistance to water deficit.     

High gain observer for a class of nonlinear systems with coupled structure and sampled output measurements: application to a quadrotor

This paper proposes a new high gain observer for a class of non-uniformly observable nonlinear systems with coupled structure driven by sampled outputs. The considered class of systems is particularly constituted by several subsystems where each subsystem is associated to a subset of the output variables. The observer design is carried out through two steps. First, a high-gain observer is proposed in the continuous-time output case under the assumption that an adequate persistent excitation condition is satisfied by each subsystem. Then, the proposed observer is redesigned to handle the case of sampled outputs leading thereby to a continuous-discrete time observer. The latter property is achieved thanks to the approach pursued along the convergence analysis. The effectiveness of the proposed observer is emphasised in a realistic simulation framework involving a mathematical model of a quadrotor which is diffeomorphic to the proposed class of considered systems.     

Adaptive fuzzy controller for non-affine systems with zero dynamics

In this article, the direct adaptive fuzzy control problem is investigated for a class of general non-linear systems with zero dynamics. The direct adaptive fuzzy controller is developed based on a unified observer which is used to estimate the time derivatives of the output. The corrective term of the proposed observer involves a well-defined design function which is shown to be satisfied by the commonly used high-gain-based observers, namely for the usual high-gain observers and the sliding-mode observers together with their implementable versions. By using a general error function, and without resorting to the famous strictly positive real condition or the filtering of the observation error, a general proportional–integral (PI) law for updating the fuzzy parameters is proposed. Ultimately boundedness of the error signals is shown through Lyapunov's direct method. Theoretical results are illustrated through two simulation examples.     

Advanced classification approach for neuronal phoneme recognition system based on efficient constructive training algorithm

This paper introduces a neural network optimization procedure allowing the generation of multilayer perceptron (MLP) network topologies with few connections, low complexity and high classification performance for phoneme’s recognition. An efficient constructive algorithm with incremental training using a new proposed Frame by Frame Neural Networks (FFNN) classification approach for automatic phoneme recognition is thus proposed. It is based on a novel recruiting hidden neuron’s procedure for a single hidden-layer. After an initializing phase started with initial small number of hidden neurons, this algorithm allows the Neural Networks (NNs) to adjust automatically its parameters during the training phase. The modular FFNN classification method is then constructed and tested to recognize 5 broad phonetic classes extracted from the TIMIT database. In order to take into account the speech variability related to the coarticulation effect, a Context Window of Three Successive Frame’s (CWTSF) analysis is applied. Although, an important reduction of the computational training time is observed, this technique penalized the overall Phone Recognition Rate (PRR) and increased the complexity of the recognition system. To alleviate these limitations, two feature dimensionality reduction techniques respectively based on Principal Component Analysis (PCA) and Self Organizing Maps (SOM) are investigated. It is observed an important improvement in the performance of the recognition system when the PCA technique is applied. Optimal neuronal phone recognition architecture is finally derived according to the following criteria: best PRR, minimum computational training time and complexity of the BPNN architecture.     

High gain observer for a class of non-triangular systems

This paper presents a high gain observer for a class of MIMO nonlinear systems involving some uncertainties. The latter is particularly composed of cascade subsystems where each subsystem is associated with a subset of the output variables, and assumes a triangular dependence on its own state variables and may depend on the state variables of all other subsystems. The main contribution consists in extending the available results to allow more interconnections between the subsystems. Of fundamental interest, it is shown that the underlying observation error exponentially converges to zero in the absence of uncertainties. Moreover, the observation error can be made as small as desired by properly specifying the observer design parameter in the case where uncertainties are considered.     

A high gain observer with updated gain for a class of MIMO non-triangular systems

A high gain like observer with updated gain is proposed for a class of cascade nonlinear and non triangular systems that are observable for any input. The objective of the gain adaptation is to perform an admissible tradeoff between state reconstruction dynamics on the one hand versus noise amplification on the other hand. To this end, the gain of the proposed observer is tuned through a single scalar time-varying parameter governed by an adequate differential Riccati equation. The involved adaptation process is mainly driven by the power of the output observation error norm computed on a moving horizon window. Simulation results are given to show the effectiveness of the proposed observer, namely its exponential convergence and its insensitivity with respect to noise measurements.     

High gain observer with updated gain for a class of MIMO nonlinear systems

A high gain like observer with an updated gain is proposed for a class of MIMO nonlinear systems that are observable for any inputs. The main contribution of this article lies in the nature of the observer gain that involves a scalar time-varying design parameter governed by some scalar Riccati equation. This time-varying design parameter, chosen constant in standard high observer, allows to get an acceptable tradeoff between state reconstruction speed on the one hand versus amplification of noise on the other. Simulation results are given in order to highlight the performances of the proposed observer, namely its exponential convergence and its good behaviour in dealing with noise measurements.     

High gain observer based on-line rotor and stator resistances estimation for IMs

One of the main challenging issues in induction motor drives is the lack of knowledge about the actual values of some critical parameters, such as rotor and stator resistances which are subject to large variations during operation. Such problem is difficult to resolve due to the strong interconnection between states and parameters in the nonlinear motor model, besides the unavailability of both rotor flux and load torque. In the spirit to accurately follow the on-line machine variables, this paper focuses on the simultaneous estimation of internal states and time varying parameters. Especially, a new identification scheme for rotor resistance and/or stator resistance is introduced. In the aim of decoupling the unknown electrical parameters, we adopt a mild change of coordinates that allows to easily design a two-stage of high gain observer. The simplicity of presented procedures and the efficiency for real time computation constitute both main features of the proposed approach. Moreover, possible exploitation of our algorithm in the fault detection issue is discussed through a simulation of an abrupt rotor short-circuit.     

CAMBIO: software for modelling and simulation of bioprocesses

CAMBIO, a software package devoted to bioprocess modelling, which runs on Apollo computers, is described. This software enables bioengineers to easily and interactively design appropriate mathematical models directly from their perception of the process. CAMBIO provides the user with a set of design symbols and mnemonic icons in order to interactively design a functional diagram. This diagram has to exhibit the most relevant components with their related interactions through biological and physico-chemical reactions. Then, CAMBIO automatically generates the dynamical material balance equations of the process in the form of an algebraic-differential system by taking advantage of the knowledge involved in the functional diagram. The model may be used for control design purpose or completed by kinetics expressions with a view to simulation. CAMBIO offers facilities to generate a simulation model (for coding of kinetics, introducing auxiliary variables, etc.). This model is automatically interfaced with a specialized simulation software which allows an immediate visualization of the process dynamical behaviour under various operational conditions (possibly involving feedback control strategies). An example of an application dealing with yeast fermentation is given.