Effects of phytase enzyme supplementation on growth performance, intestinal morphology and metabolism in Nile tilapia (Oreochromis niloticus).

Phytase is crucial in enhancing the bioavailability and release of phosphorus and other nutrients bound to phytic acid, making them more bioavailable for animal absorption. This study was carried out to inspect the effect of supplementing low phosphorus (P) diet with di-calcium phosphate (DCP) and liquid phytase enzyme (LP), which contains 1500 FTU/kg, on growth performance, intestinal morphometry, proximate body chemical composition, blood profile, immunity status, liver mitochondrial enzyme activities, the expression response and economic returns of Nile tilapia (Oreochromis niloticus). Three triplicate groups of fish (initial weight 5.405 ± 0.045 g, N = 90) were fed on three different diets for 90 days. The first was a control diet with zero DCP; the second was a control diet supplemented with 0.71% DCP; the third was a control diet supplemented with 0.03% LP. The groups were designated as CG, DCP and LP, respectively. Results showed that LP induced considerable improvements (p < 0.05) in FBW, body weight gain, weight gain rate, specific growth rate, HIS, viscero-somatic index, spleen-somatic index, feed conversion ratio, blood parameters and the histomorphometry assessment of intestinal villi absorptive capacity, compared with the other groups. Also, whole-body protein and lipid contents pointedly (p < 0.05) increased by LP, compared with the DCP group. A positive response (p < 0.05) to the phytase enzyme was noted in complexes I, III and IV of the mitochondrial liver complex enzyme activity. Likewise, the relative gene expression levels of (GHr-1, IGF-1, FAS and LPL) were notably (p < 0.05) upregulated by phytase enzyme, associated with DCP and control groups. Further, phytase recorded the highest total return and profit percentage. It can be concluded that Nile tilapia benefits from using phytase enzyme 1500 FTU/kg at 0.03% without adding DCP in terms of good performance and profits.

Adaptive neural PD controllers for mobile manipulator trajectory tracking.

Artificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD controller and a multilayer neural PD controller for position tracking of a mobile manipulator. Both controllers are trained by an extended Kalman filter (EKF) algorithm. Neural networks trained with the EKF algorithm show faster learning speeds and convergence times than the training based on backpropagation. The integrative term in PID controllers eliminates the steady-state error, but it provokes oscillations and overshoot. Moreover, the cumulative error in the integral action may produce windup effects such as high settling time, poor performance, and instability. The proposed neural PD controllers adjust their gains dynamically, which eliminates the steady-state error. Then, the integrative term is not required, and oscillations and overshot are highly reduced. Removing the integral part also eliminates the need for anti-windup methodologies to deal with the windup effects. Mobile manipulators are popular due to their mobile capability combined with a dexterous manipulation capability, which gives them the potential for many industrial applications. Applicability of the proposed adaptive neural controllers is presented by simulating experimental results on a KUKA Youbot mobile manipulator, presenting different tests and comparisons with the conventional PID controller and an existing adaptive neuron PID controller.

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution.

This article presents an approach to solve the inverse kinematics of cooperative mobile manipulators for coordinate manipulation tasks. A self-adaptive differential evolution algorithm is used to solve the inverse kinematics as a global constrained optimization problem. A kinematics model of the cooperative mobile manipulators system is proposed, considering a system with two omnidirectional platform manipulators with n DOF. An objective function is formulated based on the forward kinematics equations. Consequently, the proposed approach does not suffer from singularities because it does not require the inversion of any Jacobian matrix. The design of the objective function also contains penalty functions to handle the joint limits constraints. Simulation experiments are performed to test the proposed approach for solving coordinate path tracking tasks. The solutions of the inverse kinematics show precise and accurate results. The experimental setup considers two mobile manipulators based on the KUKA Youbot system to demonstrate the applicability of the proposed approach.

Emotional system in complex cognitive activities of working memory: A literature review of its role.

Cognitive processing is needed to elicit emotional responses. At the same time, emotional responses modulate and guide cognition to enable adaptive responses to the environment. However, most empirical studies and theoretical models of cognitive functions have been investigated without taking into account emotion, which is considered interference that is counterproductive to the correct functioning of the cognitive system. To understand how complex behaviors are carried out in the brain, an understanding of the interactions between emotion and cognition may be indispensable. Given the enormous scope of this topic for both cognition and emotion, these concepts will not be further defined here; instead, this review will be relatively narrow in scope and will emphasize several brain systems involved in the interactions between emotion and working memory because an important dimension of cognition involves working memory function. In attempting to understand the relationship between emotion and working memory, we will describe the projections of a set of brain structures that support our emotional life and the neuromodulator dopamine (which is also involved in emotion processing and incentive motivational behavior) in the prefrontal cortex. According to the literature, working memory engages the cortical regions. Thus, the prefrontal cortex, particularly the dorsolateral prefrontal cortex (DLPFC), although commonly viewed as a purely cognitive area, provides a test for the hypothesis that working memory and emotion are strongly integrated in the brain. In this review, we provide an overview of neuropsychological, neuroanatomical and molecular evidence, with the aim of establishing the extent to which working memory and emotion are related.

Hybrid Geoid Model: Theory and Application in Brazil.

Determination of the ellipsoidal height by Global Navigation Satellite Systems (GNSS) is becoming better known and used for purposes of leveling with the aid of geoid models. However, the disadvantage of this method is the quality of the geoid models, which degrade heights and limit the application of the method. In order to provide better quality in transforming height using GNSS leveling, this research aims to develop a hybridization methodology of gravimetric geoid models EGM08, MAPGEO2015 and GEOIDSP2014 for the State of São Paulo, providing more consistent models with GNSS technology. Radial Basis Function (RBF) neural networks were used to obtain the corrector surface, based on differences between geoid model undulations and the undulations obtained by GNSS tracking in benchmarks. The experiments showed that the most suitable interpolation for correction modeling is the linear RBF. Checkpoints indicate that the geoid hybrid models feature root mean square deviation ± 0.107, ± 0.104 and ± 0.098 m, respectively. The results shows an improvement of 30 to 40% in consistencies compared with the gravimetric geoids, providing users with better quality in transformation of geometric to orthometric heights.

Hybrid Geoid Model: Theory and Application in Brazil

Determination of the ellipsoidal height by Global Navigation Satellite Systems (GNSS) is becoming better known and used for purposes of leveling with the aid of geoid models. However, the disadvantage of this method is the quality of the geoid models, which degrade heights and limit the application of the method. In order to provide better quality in transforming height using GNSS leveling, this research aims to develop a hybridization methodology of gravimetric geoid models EGM08, MAPGEO2015 and GEOIDSP2014 for the State of São Paulo, providing more consistent models with GNSS technology. Radial Basis Function (RBF) neural networks were used to obtain the corrector surface, based on differences between geoid model undulations and the undulations obtained by GNSS tracking in benchmarks. The experiments showed that the most suitable interpolation for correction modeling is the linear RBF. Checkpoints indicate that the geoid hybrid models feature root mean square deviation ± 0.107, ± 0.104 and ± 0.098 m, respectively. The results shows an improvement of 30 to 40% in consistencies compared with the gravimetric geoids, providing users with better quality in transformation of geometric to orthometric heights.

Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller.

In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Clifford support vector machines for classification, regression, and recurrence.

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.