Insights on biorational potential of Ocimum gratissimum essential oil and its binary combination with monoterpene phenol for control of rice weevil (Sitophilus oryzae) and aflatoxigenic fungi.

Food grain storage is a difficult task due to insect infestation and subsequent mycotoxin contaminations which adversely affects the nutritional quality of grains and leading to economic loss. Current research focuses on contact and fumigant toxicity effects of essential oils (EO) and aroma compounds against Sitophilus oryzae and growth inhibition of aflatoxigenic fungi. The EO of Ocimum gratissimum comprised of thymol (46.8%), γ-terpinene (14.04%) along with o-cymene (11.76%). Also, Cymbopogon flexuosus rich in citral (76.3%) and geraniol (84.6%) and Cymbopogon nardus having geraniol (49.24%) and geranyl acetate (20.9%) were all evaluated using a dose of 25 μL (Conc∼833.3 μL/L air) against S. oryzae . All the compounds showed significant mortality (>95%) at 24 h of exposure. The insecticidal property of O. gratissimum oil chemotype showed a strong contact and fumigant toxicity against S. oryzae at a highest dose of 25 μL (Conc∼833.3 μL/L air) within 24 h. It has been further evaluated on three aflatoxigenic fungal strains which showed reduction in growth and aflatoxin content (Aflatoxin B and G), which was markedly reduced upon the treatment. The binary mixture interaction of O. gratissimum oil with monoterpene phenol (carvacrol) was assessed and the specific binary mixture of 80:20 ratio (having additive property) acts as a contact insecticide with 100% mortality. Hence, essential oil of less explored Ocimum species (O. gratissimum ) and its binary mixture could be deployed as potential biorational for control of rice weevil (Sitophilus oryzae ) and aflatoxigenic Aspergillus spp.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.