scholarly journals IN SILICO MODEL QSPR FOR PREDICTION OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES

2018 ◽  
Vol 127 (1A) ◽  
pp. 67
Author(s):  
Nguyen Minh Quang ◽  
Tran Xuan Mau ◽  
Pham Van Tat ◽  
Tran Nguyen Minh An ◽  
Vo Thanh Cong
Keyword(s):  
Neural Network ◽  
Stability Constants ◽  
In Silico ◽  
Metal Ion ◽  
Test Group ◽  
Structure And Property ◽  
The Neural Network ◽  
Complex Solutions ◽  
Artificial Neural Network Ann ◽  
The Stability

In the present work, the stability constants logb<sub>11</sub> and the concentration of metal ion and thiosemicarbazone in complex solutions were determined by using <em>in silico</em> models. The 2D, 3D, physicochemical and quantum descriptors of complexes were generated from the molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The quantitative structure and property relationships (QSPRs) were constructed by using the ordinary linear regression (OLR) and artificial neural network (ANN). The best linear model QSPR<sub>OLR</sub> (with <em>k</em> of 6) involved descriptors k0, core-core repulsion, xp5, xch5, valence, and SHHBd. The quality of model QSPR<sub>OLR</sub> had the statistical values: <em>R</em><sup>2</sup><sub>train</sub> = 0.898, <em>R</em><sup>2</sup><sub>adj</sub> = 0.889, <em>Q</em><sup>2</sup><em><sub>LOO</sub></em> = 0.846, MSE = 1.136, and <em>F<sub>stat</sub></em> = 91.348. The neural network model QSPR<sub>ANN</sub> with architecture I(6)-HL(6)-O(1) had the statistical values: <em>R</em><sup>2</sup><em><sub>train</sub></em> = 0.9768, and <em>Q</em><sup>2</sup><em><sub>LOO</sub></em> = 0.8687. The predictability of QSPR models for complexes of the test group turned out to be in good agreement with those from the experimental data in the literature.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Neural Networks Applied to Estimating Subglacial Topography and Glacier Volume

2009 ◽  
Vol 22 (8) ◽  
pp. 2146-2160 ◽  
Author(s):  
Garry K. C. Clarke ◽  
Etienne Berthier ◽  
Christian G. Schoof ◽  
Alexander H. Jarosch
Keyword(s):  
Neural Network ◽  
Regional Scale ◽  
Ice Thickness ◽  
Ice Dynamics ◽  
Ice Caps ◽  
Mountain Glaciers ◽  
The Neural Network ◽  
Glacier Ice ◽  
Subglacial Topography ◽  
Artificial Neural Network Ann

Abstract To predict the rate and consequences of shrinkage of the earth’s mountain glaciers and ice caps, it is necessary to have improved regional-scale models of mountain glaciation and better knowledge of the subglacial topography upon which these models must operate. The problem of estimating glacier ice thickness is addressed by developing an artificial neural network (ANN) approach that uses calculations performed on a digital elevation model (DEM) and on a mask of the present-day ice cover. Because suitable data from real glaciers are lacking, the ANN is trained by substituting the known topography of ice-denuded regions adjacent to the ice-covered regions of interest, and this known topography is hidden by imagining it to be ice-covered. For this training it is assumed that the topography is flooded to various levels by horizontal lake-like glaciers. The validity of this assumption and the estimation skill of the trained ANN is tested by predicting ice thickness for four 50 km × 50 km regions that are currently ice free but that have been partially glaciated using a numerical ice dynamics model. In this manner, predictions of ice thickness based on the neural network can be compared to the modeled ice thickness and the performance of the neural network can be evaluated and improved. From the results, thus far, it is found that ANN depth estimates can yield plausible subglacial topography with a representative rms elevation error of ±70 m and remarkably good estimates of ice volume.

Artificial Neural Networks and Learning Techniques

2016 ◽  
pp. 89-112
Author(s):  
Pushpendu Kar ◽  
Anusua Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Work ◽  
The Neural Network ◽  
Learning Techniques ◽  
Learning Procedure ◽  
Processing Component ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

Transferrin binding of Al3+ and Fe3+.

1987 ◽  
Vol 33 (3) ◽  
pp. 405-407 ◽  
Author(s):  
R B Martin ◽  
J Savory ◽  
S Brown ◽  
R L Bertholf ◽  
M R Wills
Keyword(s):  
Stability Constant ◽  
Blood Plasma ◽  
Stability Constants ◽  
Metal Ion ◽  
Equilibrium Dialysis ◽  
Ion Binding ◽  
Metal Ion Binding ◽  
Quantitative Studies ◽  
Intensity Changes ◽  
The Stability

Abstract An understanding of Al3+-induced diseases requires identification of the blood carrier of Al3+ to the tissues where Al3+ exerts a toxic action. Quantitative studies demonstrate that the protein transferrin (iron-free) is the strongest Al3+ binder in blood plasma. Under plasma conditions of pH 7.4 and [HCO3-]27 mmol/L, the successive stability constant values for Al3+ binding to transferrin are log K1 = 12.9 and log K2 = 12.3. When the concentration of total Al3+ in plasma is 1 mumol/L, the free Al3+ concentration permitted by transferrin is 10(-14.6) mol/L, less than that allowed by insoluble Al(OH)3, by Al(OH)2H2PO4, or by complexing with citrate. Thus transferrin is the ultimate carrier of Al3+ in the blood. We also used intensity changes produced by metal ion binding to determine the stability constants for Fe3+ binding to transferrin: log K1 = 22.7 and log K2 = 22.1. These constants agree closely with a revision of the reported values obtained by equilibrium dialysis. By comparison with Fe3+ binding, the Al3+ stability constants are weaker than expected; this suggests that the significantly smaller Al3+ ions cannot coordinate to all the transferrin donor atoms available to Fe3+.

ANN-Based Wheat Chlorophyll Density Estimation Using Canopy Hyperspectral Vegetation Indices

2012 ◽  
Vol 500 ◽  
pp. 243-249
Author(s):  
Da Cheng Wang ◽  
Luo Rui Sen ◽  
Ji Hua Wang ◽  
Cun Jun Li ◽  
Dong Yan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Statistical Method ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Leaf Chlorophyll ◽  
The Neural Network ◽  
Spectral Vegetation Indices ◽  
Artificial Neural Network Ann ◽  
Traditional Statistical Method

Canopy leaf Chlorophyll Density is a key index for evaluating crop potential photosynthetic efficiency and nutritional stress. Leaf Chlorophyll Density estimate using canopy hyperspectral vegetation indices provides a rapid and non-destructive method to evaluate yield predictions. A systematic comparison of two approaches to estimate Chlorophyll Density using 6 spectral vegetation indices (VIs) was presented in this study. In this study, the traditional statistical method based on power regression analyses was compared to the emerging computationally powerful techniques based on artificial neural network (ANN). The regression models of TCARI 、SAVI 、MSAVI and RDVIgreen were found to be more suitable for predicting Chlorophyll Density when only traditional statistical method was used especially TCARI and RDVI. ANN method was more appropriate to develop prediction models. The comparisons between these two methods were based on analysis of the statistic parameters. Results obtained using Root Mean Square Error (RMSE) for ANNs were significantly lower than the traditional method. From this analysis it is concluded that the neural network is more robust to train and estimate crop Chlorophyll Density from remote sensing data.

The affinity of aldehydic compounds to complex formation. Nickel(II) and cobalt(II) complexes with 2-pyridinecarboxaldehyde

1977 ◽  
Vol 55 (14) ◽  
pp. 2613-2619 ◽  
Author(s):  
M. S. El-Ezaby ◽  
M. A. El-Dessouky ◽  
N. M. Shuaib
Keyword(s):  
Complex Formation ◽  
Aqueous Solutions ◽  
Stability Constants ◽  
Metal Ion ◽  
Experimental Conditions ◽  
Complex Species ◽  
Spectrophotometric Methods ◽  
Complex Equilibria ◽  
The Stability ◽  
Hydroxy Groups

The interactions of Ni(II) and Co(II) with 2-pyridinecarboxaldehyde have been investigated in aqueous solutions at μ = 0.10 M (KNO3) at 30 °C. The stability constants of different complex equilibria have been determined using potentiometric methods. Spectrophotometric methods were also used in the case of the nickel(II) – 2-pyridinecarboxaldehyde system. It was concluded that nickel(II) and cobalt(II), analogous to copper(II), enhance hyrdation of 2-pyridinecarboxaldehyde prior to deprotonation of one of the geminal hydroxy groups. Complex species of 1:1 as well as 1:2 metal ion to ligand composition exist under the experimental conditions used.

scholarly journals Interaction of Ampicillin and Amoxicillin with Mn2+: A Speciation Study in Aqueous Solution

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3110
Author(s):  
Claudia Foti ◽  
Ottavia Giuffrè
Keyword(s):  
Aqueous Solution ◽  
Stability Constants ◽  
Metal Ion ◽  
Formation Constants ◽  
Enthalpy Change ◽  
Ligand Concentration ◽  
Empirical Parameter ◽  
Speciation Study ◽  
Potentiometric Measurements ◽  
The Stability

A potentiometric and UV spectrophotometric investigation on Mn2+-ampicillin and Mn2+-amoxicillin systems in NaCl aqueous solution is reported. The potentiometric measurements were carried out under different conditions of temperature (15 ≤ t/°C ≤ 37). The obtained speciation pattern includes two species for both the investigated systems. More in detail, for system containing ampicillin MLH and ML species, for that containing amoxicillin, MLH2 and MLH ones. The spectrophotometric findings have fully confirmed the results obtained by potentiometry for both the systems, in terms of speciation models as well as the stability constants of the formed species. Enthalpy change values were calculated via the dependence of formation constants of the species on temperature. The sequestering ability of ampicillin and amoxicillin towards Mn2+ was also evaluated under different conditions of pH and temperature via pL0.5 empirical parameter (i.e., cologarithm of the ligand concentration required to sequester 50% of the metal ion present in traces).

scholarly journals Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jiazhi Li ◽  
Weicun Zhang ◽  
Quanmin Zhu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
External Disturbance ◽  
Lyapunov Stability Theory ◽  
Robotic Manipulators ◽  
Adaptive Observer ◽  
Multiple Model ◽  
Neural Network Controller ◽  
The Neural Network ◽  
The Stability

This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy.

Research of Intelligent Substation Condition Monitoring Based on the Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 692-696
Author(s):  
Da Peng Chai ◽  
Qiang Qiang Xue ◽  
Ling Mei Wang ◽  
Xing Yong Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Electric Power ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Power Equipment ◽  
The Neural Network ◽  
Condition Monitoring System ◽  
Artificial Neural Network Ann

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).

An Efficient Neural Network Model for Path Planning of Car-like Robots in Dynamic Environment

2000 ◽  
Vol 4 (3) ◽  
pp. 220-229 ◽  
Author(s):  
Simon X. Yang ◽  
◽  
Max Meng ◽  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Lyapunov Stability Theory ◽  
Network Size ◽  
Computationally Efficient ◽  
Neural Network Approach ◽  
The Neural Network ◽  
The Stability

In this paper, an effcient neural network approach to real-time path planning with obstacle avoidance of holonomic car-like robots in a dynamic environment is proposed. The dynamics of each neuron in this biologically inspired, topologically organized neural network is characterized by a shunting equation or an additive equation. The state space of the neural network is the configuration space of the robot. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The real-time collision-free path is planned through the dynamic neural activity landscape of the neural network without explicitly searching over neither the free workspace nor the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore it is computationally efficient. The stability of the neural network is proven by both qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency are demonstrated through simulation studies.

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