Application of a Multilayer Perceptron Artificial Neural Network for the Prediction and Optimization of the Andrographolide Content in Andrographis paniculata

Andrographolide, the principal secondary metabolite of Andrographis paniculata, displays a wide spectrum of medicinal activities. The content of andrographolide varies significantly in the species collected from different geographical regions. Therefore, this study aims at investigating the role of different abiotic factors and selecting suitable sites for the cultivation of A. paniculata with high andrographolide content using a multilayer perceptron artificial neural network (MLP-ANN) approach. A total of 150 accessions of A. paniculata collected from different regions of Odisha and West Bengal in eastern India showed a variation in andrographolide content in the range of 0.28–5.45% on a dry weight basis. The MLP-ANN was trained using climatic factors and soil nutrients as the input layer and the andrographolide content as the output layer. The best topological ANN architecture, consisting of 14 input neurons, 12 hidden neurons, and 1 output neuron, could predict the andrographolide content with 90% accuracy. The developed ANN model showed good predictive performance with a correlation coefficient (R²) of 0.9716 and a root-mean-square error (RMSE) of 0.18. The global sensitivity analysis revealed nitrogen followed by phosphorus and potassium as the predominant input variables influencing the andrographolide content. The andrographolide content could be increased from 3.38% to 4.90% by optimizing these sensitive factors. The result showed that the ANN approach is reliable for the prediction of suitable sites for the optimum andrographolide yield in A. paniculata.     

Tunnelling Dynamics of Kicked Systems: A Route to Tunnelling Control

The effects of discontinuously time-varying perturbations on the dynamics of a particle moving in harmonic, symmetric double well and symmetric triple well potentials, are investigated both classically and quantum mechanically. The quantum dynamics is followed using the time-dependent Fourier grid Hamiltonian (TDFGH) method while the classical dynamics is analyzed within the framework of classical Hamiltonian mechanics. Depending on the spatial symmetry of the perturbation and the characteristic features of the reversal time , different types of ‘phase space’ structures are observed in each of the potentials. For symmetric double and triple well potentials, quantum dynamics reveals that complete destruction of tunnelling (CDT) can be achieved in the presence of a time-dependent spatially asymmetric perturbing field that is continuous in time. Any discontinuity in time-variation of the perturbation may induce over the barrier transition. The relevance of these results in the context of (i) tunnelling control and (ii) quantum computing with 3-state or 2-state quantum registers is briefly discussed.     

Vanadium(IV)-oxo Corrole Catalyzed Selective Oxidative Cleavage of Alkenes to Aldehydes

A practical and efficient protocol for oxidative cleavage of olefinic bonds especially in arylated olefins has been demonstrated. Herein, an oxo[5,10,15-tris(4-nitrophenyl)corrolato]vanadium (IV) complex (cat.), has been successfully synthesized and the existence of two tautomeric forms of this complex in solution has been established. Oxo[5,10,15-tris(4-nitrophenyl)corrolato]vanadium (IV) (cat.) in the presence of H₂O₂ cleaves olefinic bonds to yield the corresponding aldehyde compounds. In general, a high valent, oxo-(porphyrinoid)-metal complex catalyzes the epoxide formation reactions, however, in the present case, we have observed the exclusive formation of aldehydes. The reaction offered aryl aldehydes with good yields and excellent selectivity. A mechanism was also proposed for these catalysis reactions.