Global NEST Journal

In recent days, massive quantity of waste materials gets considerable increased with increasing population. Proper management of waste materials becomes essential to reduce environmental degradation and improve quality of life in smart cities. Waste management helps to collect and treat waste materials from society. Smart waste management is a new frontier for local authorities assisting in reducing municipal solid waste and improving community recycling rate. Appropriate classification of waste objects necessitates the design of automated waste classification models based on artificial intelligence (AI) and computer vision (CV) based approaches. With this motivation, in this study, an automated artificial intelligence with earth worm optimization assisted waste management and classification (AIEWO-WMC) model is proposed for smart city environment. The proposed technique intends to recognize and categorize waste objects using the DL techniques. The proposed model primarily derives a RetinaNet based object detection module to identify the existence of waste objects in the images. To improve the classification performance, Adagrad optimizer is applied. Moreover, earthworm optimization with stacked autoencoder (SAE) algorithm is applied for the classification of waste objects. To assuring the improvised results of the AIEWO-WMC technique, comprehensive experimentation is performed on standard dataset and the obtained values indicated the supremacy of AIEWO-WMC model over the other techniques with increased accuracy of 99.15%.

The microbial consortia incorporated in bio concrete should have traits such as high alkali resistance and the ability to form endospores to overcome the mechanical and chemical stresses produced during concrete preparation. The primary intent of our work is to segregate and identify the calcium carbonate precipitating microorganisms with a strong urease activity, compare their potential for the preparation of bio concrete, and evaluate its performance, durability, and effects on heavy metal toxicity. Bacteria, algae, protozoans, yeasts, and fungi are predominantly reported in the consortia of microbes in a construction sample, making it an excellent source of wild microbial species and strains which have adapted to the environment of the construction place which consists of a consortium of limestone, fine sand, and curing water. Consortia of organisms were isolated from fermented lime mortar and curing construction water, which was recognized using the 16S rRNA gene sequencing technique. Atomic Absorption Spectrometry[AAS] and Inductively Coupled Plasma Mass Spectrometry[ICP-MS] were carried out to analyze the deduction of heavy metal toxicity present in the produced bio concrete. The results of strength and toxicity tests were compared with bio-concrete produced from standard cultures of Bacillus megaterium (NDRI-067) and Bacillus licheniformis(NDRI-598) obtained from the National Dairy Research Institute. (NDRI), and also compared with conventional concrete.

Water pollution has been acknowledged as an issue of concern due to recalcitrant pollutants like textile dyes. Therefore, the eco-friendly synthesis of nano-particles (NPs) through green chemistry approach from sources and materials of organic origin has not only enhanced the properties of substance at nanoscale but has also reduced the toxicological impacts on human health and the environment. In this study a successful attempt was made to synthesize TiO2 NPs by Mangifera indica leaves extract as organic source. The structural morphology was determined by carrying out FTIR, SEM, EDX and XRD techniques. The removal of Methylene Blue (MB) dye was assessed by the application of the photocatalysis technique. The experiments were designed using the RSM (CCD) approach for selected parameters such as pH, TiO2 NPs, Dye concentration and Time. The maximal dye removal efficiency of 98% was achieved at optimum conditions of 4.7 pH, 0.04g of nano photocatalyst in 250ml of dye solution having 8ppm concentration, allowed to react for 45 min. The regression coefficient value of 79.37% was determined by ANOVA for MB dye. The results clearly indicate that the application of green nanoparticles for the removal of MB dye is a feasible option to overcome with the ever-increasing issue of water pollution and environmental degradation.

People are prompted to complain about air pollution by offensive odours. They can produce psychological consequences, such as nausea, headaches, lack of appetite, breathing difficulties, and various adverse reactions in some circumstances. Among various industries, the dairy industry emits the most noxious odours. The human nose is the only trustworthy sensor, and numerous laboratory and field methods for quantifying human observations have been devised. Most odours are difficult to quantify, as evidenced by the fact that contemporary technology has yet to provide a fully precise method for quantifying them. As a result, the current study examines the possibility of employing fuzzy logic to predict odour intensity from dairy effluent characterisation. In this work, an Adaptive Neuro Fuzzy Inference System (ANFIS) based prediction model using Sugeno controller is developed for finding the Odour Index of the dairy effluent. COD and BOD are considered as input parameters for the model with triangular membership function for five linguistic variables. The standard odour index estimated from the literatures is used for modelling. The predicted odour index obtained from the ANFIS model for synthetic dairy wastewater is validated against the standard odour index and the average error is found to be ± 0.16 %.