The current experiment aimed to identify the characteristics of composite materials enhanced with aloe vera, bamboo, palm, and kevlar fibres. Three different types of combinational fabrication—Type I (a blend of aloe vera and bamboo), Type II (a combination of bamboo and palm), and Type III—were carried out from all the other them (blend of palm and aloe vera). Analysis was done on the mechanical and dynamic-mechanical evaluation of biocomposites made spontaneously. Natural fibres used to produce hybrid composites were alkaline and treated in a 2.5 ml NaOH solution for 6 h at room temperature to get acceptable characteristics, then dried to remove the wax and oils on the natural fibre’s exterior surface. The effect of different stacking sequences on the mechanical and dynamic properties of manufactured composites has been investigated experimentally through ASTM standards. Impact, inter-delamination and double-shear tests are used to evaluate the mechanical properties; the failure mechanisms of the fabricated hybrid composites with various stacking sequences and testing conditions were investigated through the fractographs of SEM analysis. Type I S1 samples were found to display significant impact energy (10 Joules) as compared to other samples, and the break load of composite specimens was higher at 4.5 KN in S2 samples of type-III as compared to type-I and II, revealed Type-I samples with significant peak area of 0.492 delivered at 102.01 °C as compared to two types, Type-3 (Palmyra Palm + Aloe Vera) composite gave the best mechanical, dynamic properties.

Artificial intelligence and Deep learning techniques propose and provide effective mechanism for classification among several commodities like gender classification on basis of ridge count, spam mail classification and detection etc. Here we have proposed a hybrid module on basis of available AI and ML techniques by which we can achieve more than 90% accuracy for any provided test dataset. We have included over 50423 samples, out of which we use 66 percentage of data for training purpose of our model and 34% remaining sample we use as test dataset, with 8 fold we have achieve approximately 96% accuracy. For collection of dataset and processing of dataset we gone through several phases which include extraction of feature (EoF) using feature extraction technique, for cleaning of dataset we have use dimension reduction technique factor analysis(FA). In next phase for classification of flowers we have used classification techniques in which we found the accuracy score of j48 classifier is higher than Naïve Bayes and Jrip algorithm. Hence the choice of j48 classifier for classification is right.

This paper develops a novel sustainable production inventory model for deteriorating items under a dual-warehouse framework, where the demand is jointly influenced by greening efforts and marketing investment. Unlike traditional models, this study introduces a unique demand pattern that is simultaneously sensitive to both the firm’s level of environmental responsibility and its marketing intensity, reflecting growing consumer awareness and market competition. The system consists of an own warehouse (OW) with limited capacity and a rented warehouse (RW) with higher holding cost. The deterioration of items occurs at a constant rate, and production is continuous during a finite replenishment cycle. To enhance practical relevance, the model incorporates a greening cost function and a marketing investment function, both impacting demand and overall system profit. Analytical expressions are derived for inventory levels, total cost, and profit functions. Furthermore, to efficiently solve the resulting nonlinear optimization problem, this study implements the Sine-Cosine Algorithm (SCA), a robust metaheuristic technique that optimizes key decision variables—including selling price, greening level, marketing investment, and cycle length—to maximize total profit. The SCA’s oscillatory search mechanism effectively balances exploration and exploitation, enabling convergence to near-optimal solutions under complex operational constraints. A sensitivity analysis is conducted to explore the impact of key parameters including greening level, marketing investment, and warehouse costs. The study highlights that joint optimization of environmental and promotional strategies, facilitated by computational algorithms such as SCA, can significantly enhance economic and ecological performance. The proposed model offers valuable insights for decision-makers aiming to integrate sustainability into inventory planning..

This paper develops a novel sustainable production inventory model for deteriorating items under a dual-warehouse framework, where the demand is jointly influenced by greening efforts and marketing investment. Unlike traditional models, this study introduces a unique demand pattern that is simultaneously sensitive to both the firm’s level of environmental responsibility and its marketing intensity, reflecting growing consumer awareness and market competition. The system consists of an own warehouse (OW) with limited capacity and a rented warehouse (RW) with higher holding cost. The deterioration of items occurs at a constant rate, and production is continuous during a finite replenishment cycle. To enhance practical relevance, the model incorporates a greening cost function and a marketing investment function, both impacting demand and overall system profit. Analytical expressions are derived for inventory levels, total cost, and profit functions. A sensitivity analysis is conducted to explore the impact of key parameters including greening level, marketing investment, and warehouse costs. The study highlights that joint optimization of environmental and promotional strategies can significantly enhance economic and ecological performance. The proposed model offers valuable insights for decision-makers aiming to integrate sustainability into inventory planning.