ABSTRACT Nanocomposites are the combination of fibers, resin and nanofillers, and it is indeed the promising materials for many industrial applications. In the present study, the reinforcement Anogeissus latifolia (AL) gum powder of 40% is mixed with polyester (P) of 60% to produce unique hybrid polyester (ALP) resin matrix, the same is feeded with three different volume fractions of 1%, 3% and 5% Fly Ash Nano Powder (FANP) as a binding agent. These compositions applied on Abaca (AB), hemp (HE) and kenaf (KE) mats to produce the individual three layers composite fibers. The findings of dynamic mechanical analysis (DMA) shows that natural fiber materials with three different volume fractions (1%, 2% and 3%) mixing in each composition impregnated ALP resin have improved the stiffness compared to hybrid ALP resin. Damping factor (Tan δ) is observed to be least in AB/HE/KE mats for three different volume fractions nano powder feeding in each composition impregnated ALP resin as compared to hybrid ALP resin. The tensile strength, hardness and impact strength of raw AB fiber mats with 3% nano powder mixing in HR composites has reached the maximum value of 49.2 MPa, 17.4 BHN and 0.83 J. The improvements were 25%, 19% and 28% for the tensile, hardness and impact strengths, respectively. fibers nanofillers applications study AL (AL 40 P (P 60 (ALP matrix 1 1% 3 5 FANP (FANP agent AB, , (AB) HE (HE KE (KE DMA (DMA (1% 2 Tan δ ABHEKE 492 49 49. MPa 174 17 4 17. 083 0 83 0.8 J 25 25% 19 28 strengths respectively 6 (AB (1 08 8 0. (

Abstract Classification of viruses is an essential step before suitable treatment of a disease. In this work, a novel approach for the classification of virus texture from TEM images is proposed. Chebyshev moments are used to classify virus textures with the Pre-trained convolution neural networks. Resnet50 is a pre-trained deep learning model used to classify images into object categories. Chebyshev moments and the pruned features extracted from the last global pooling layer of Resnet50 are fused to improve the classification accuracy. The fused feature vector along with corresponding labels is used to train a multiclass Error Correcting Output Code (ECOC) classifier to give the output. The proposed approach is tested on a standard benchmark virus texture dataset. Peak, mean, and median classification accuracies are calculated and compared with the state of the art approaches. In addition, various other classification metrics i.e., sensitivity, specificity, Mathews Correlation Coefficient, and kappa are given to justify the validity of the proposed method. 80% of the image dataset is chosen for training and the remaining 20% for testing. A peak classification accuracy of 90.33%, mean accuracy of 86.99% and median accuracy of 86.66% is achieved. Superiority of the proposed method is justified with simulations.

The present study dealt with the hydro distillation of Nigella sativa seeds and GC-MS analysis. The total composition of the oil was 86.7%. The seed volatile oil was tested against 19 microbes (Gram positive, Gram negative and fungi), which showed that Gram negative bacteria Haemophilus influenza, Klebsiella pnemoniae, and Proteus vulgaris were highly sensitive against the antimicrobial agent, whereas the fungi such as Trichoderma vibriae,. Pencillium rubrum; and Dermatophyte: Trichophyton mentagrophytes; had no response at the 20µl concentration.