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Applications of improved SVM framework in modeling in mechanics

100%

Pagalthivarthi K. V. , Mittal A. , Goyal P. , Gupta G. R.

Computer Assisted Mechanics and Engineering Sciences

2007

tom Vol. 14, No. 3

509-523

The problem of empirical data modeling is pertinent to several mechanics domains. Empirical data modeling involves a process of induction to build up a model of the system from which responses of the system can be deduced for unobserved data. Machine learning tools can model underlying non-linear function given training data without imposing prior restriction on the type of function. In this paper, we show how Support Vector Machines (SVM) can be employed to solve design problems involving optimizations over parametric space and parameter prediction problems that are recurrent in engineering domain. The problem considered is diffuser design where the optimal value of pressure recovery parameter can be obtained very efficiently by SVM based algorithm even in a large search space. In addition, locating the position of points on a string vibrating in a damped medium serves as an appropriate prediction problem. A grid-searching algorithm is proposed for automatically choosing the best parameters of SVM, thus resulting in a generic framework. The results obtained by SVM are shown to be theoretically sound and a comparison with other approaches such as spline interpolation and Neural Networks shows the superiority of our framework.

A kidney bean trypsin inhibitor with an insecticidal potential against Helicoverpa armigera and Spodoptera litura

88%

Mittal A. , Kansal R. , Kalia V. , Tripathi M. , Gupta V. K.

Acta Physiologiae Plantarum

2014

tom 36

nr 02

In the present study, trypsin inhibitor extracts of ten kidney bean seed (Phaseolus vulgaris) varieties exhibiting trypsin and gut trypsin-like protease inhibitor activity were tested on Helicoverpa armigera and Spodoptera litura. Trypsin inhibitor protein was isolated and purified using multi-step strategy with a recovery of ~15 % and purification fold by ~39.4. SDS-PAGE revealed a single band corresponding to molecular mass of ~15 kDa and inhibitory activity was confirmed by reverse zymogram analyses. The inhibitor retained its inhibitory activity over a broad range of pH (3–11), temperature (40–60°C) and thermostability was promoted by casein, CaCl₂, BSA and sucrose. The purified inhibitor inhibited bovine trypsin in 1:1 molar ratio. Kinetic studies showed that the protein is a competitive inhibitor with an equilibrium dissociation constant of 1.85 μM. The purified trypsin inhibitor protein was further incorporated in the artificial diet and fed to second instar larvae. A maximum of 91.7 % inhibition was obtained in H. armigera, while it was moderate in S. litura (29 %) with slight varietal differences. The insect bioassay showed 40 and 22 % decrease in larval growth followed by 3 and 2 days delay in pupation of H. armigera and S. litura, respectively. Some of the adults emerged were deformed and not fully formed. Trypsin inhibitor protein was more effective against H. armigera as it showed 46.7 % mortality during larval growth period compared to S. litura (13.3 %).