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Objective: Evaluate the antioxidant, phytochemicals and antibacterial screening of leaves extracts of Peristrophe bicalyculata.Methods: The antibacterial activity against the respiratory tract pathogens i.e., Staphylococcus aureus (MTCC 1144), Streptococcus pneumoniae (MTCC 655), Streptococcus pyogenes (MTCC 442), Pseudomonas aeruginosa (MTCC 2474) and Klebsiella pneumoniae (MTCC 4030) was examined by the method agar well diffusion and the minimum inhibitory concentration (MICs) was determined by the method of twofold serial dilution. Broad spectrum antibiotic erythromycin was used as positive control and dimethyl sulphoxide (DMSO) used as negative control. The qualitative method was adapted for phytoconstituents screening and antioxidant activity of plant extract was examined by DPPH free radical scavenging method.Results: The results showed that the chloroform (CHF) extract has a higher degree of antibacterial potency then the other extract. The zone of inhibition showed by chloroform extract against tested bacteria ranged between 9.3±0.59 mm to 26.6±0.66 mm, respectively. MICs values were recorded between 6.25 mg/ml to 25 mg/ml against all the test organisms. Phytoconstituents analysis of P. bicalyculata extract exposed the presence of alkaloids, flavonoids, glycosides, steroids, saponins and tannins. The methanolic extract of P. bicalyculata % inhibition of DPPH radical is up to 86.33%. The P. bicalyculata (Methanolic extract) gives best antioxidant activity than another extract.Conclusion: This investigation ropes a good answer to the use of P. bicalyculata as a natural antioxidant and in herbal medicine as a support for the development of new drugs and phytomedicine in the foundation for its use in remedial of respiratory infectious diseases.

The one-dimensional cutting stock problem is a linear optimization problem that is categorized as NP-Hard. This problem has a large number of applications in a number of different industries. Though a number of traditional methods have been applied to solve this problem, these methods are not as effective as advanced optimization techniques to find the global optimum of NP-Hard problems. In this paper, a combination of three such advanced methods has been used to solve the Cutting Stock Problem: the firefly algorithm (FFA), the bat algorithm (BA) and the teaching-learning based optimization (TLBO). The results of provided by these algorithms are compared on the basis of the optimality of the solution and for three individual case studies as well as by the convergence of the algorithms. It was found that the teaching-learning based optimization technique performed well in both the optimality as well as the convergence.

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