Global NEST Journal

In concrete industry, the use of concrete solid wastes is becoming further common since it can minimize associated costs and environmental implications. The current research looks at how concrete performs when polypropylene (PP) plastic, produced from pollution garbage items, is used as a partially replacing for coarse aggregate (CA). The proportion of total PP (PPA) (0, 5, 10, 15, and 20%) as well as the water-cement ratio 0.45 of recycled household waste water, are the most important variables. Flow ability, hard porosity, maximum compressive strengths are all factors to consider, and economic feasibility are all discussed. The slump value grew when the proportion of PPA was raised, according to the findings. Compressive and cracking tensile strengths were increased in concrete containing 15% PPA. As a result, suggested to utilize more than 10% PPA in structural concrete, with either crushed stone or PP aggregate. The results showed that 15% is the best alternative to coarse gravel during terms of strength and durability, and hence PPA can be utilized as a partial replacement for coarse aggregate in concrete. To conduct studies to enhance the impact resistance of concrete by utilizing recycled water. Reducing the consumption of nonrenewable resources reducing greenhouse gas emissions and improving the strength and load capacity of industrial materials used in building research. Finally, employing non-biodegradable waste plastic resources to make green concrete will offer up new possibilities.

In recent years, the need for the rapid development of rural tourism in China has resulted in a large consumption of ecological resources, which has seriously damaged the ecological balance of travel destinations. Rural tourism in many parts of China is increasingly lacking distinctive local characteristics. It is difficult to adapt to tourists’ requirements for a deeper experience of rural travel. Some measures have been proposed to address these issues. Firstly, the connotation of “green” is reinterpreted based on relevant theoretical knowledge starting from solving the problem of rural tourism community development in China. The questionnaire method is used to screen the indicator system. An empirical analysis is carried out for actual cases. The pictures uploaded by tourists to social media are the main data source. A relatively complete and effective method system for the image research of rural tourism destinations is constructed combined with the visual analysis technology based on deep learning. A dense CNN model producing 10 output parameter combined with various factors is formed to assist the promotion by comparing the differences between the advertising pictures of the tourist destination and the pictures taken by tourists. The results show obvious consistency in the construction scene of the rural travel perception environment. Each tourist destination has its own visual characteristics, as well as differences in various sensory modality types. This research method adopts a combination of subjective and objective and comprehensively examines the scene conspicuousness, distinction, artistic quality, design quality, and time of the pictures. The formed marketing picture research method is more scientific and feasible.

Water waste management is one of the major hassles which is faced by the majority of industries. The industries let the wastewater into flowing streams or rivers near them, polluting the water resources in that locality. To prevent and safeguard the water resources, the government has given much regulation on the quality of the water before it is drained into the water sources. One of the primary problems is the amount of pH which is present in the wastewater. The pH is a highly non-linear quality of the liquids, as a single drop may change the quality to either acidic or alkaline. In this paper, the different proposed methods were used for maintaining the pH of wastewater by using various intelligent controllers. The Nonlinear Autoregressive with External Controller (NARX) and Nonlinear Autoregressive (NAR) under deep neural network is proposed where the NARX model is synthesized based on two different algorithms which involve the Levenberg Marquardt (LM) and Scaled Conjugate Gradient (SCG) for maintaining pH. The controllers are implemented into the process tank of the pilot plant for the determination and neutralization of the pH. The pilot plant is interfaced MATLAB R2019b with the computer as a controller where the controller action for the neutralization is processed. The proposed NAR model did produce better results than the other models, whose settling times were 8.6 seconds and 0.5% overshoot. The NAR model has better results when compared with the other two neural models.

For the past few decades, owing to human activities, urbanization, and industrialization, air pollution is becoming severe across several countries. Deep Learning (DL) and Machine Learning (ML) techniques had great contribution to the development of methods in various aspects of prediction, planning, and uncertainty analysis of smart cities and urban advancement in the current scenario. Many of the cities which are developed suffered from severe air quality (AQ) because of the rapid growth in industrialization and population. In this paper, we introduce a deep learning based air pollution prediction model for smart environment design planning (DLAPP-SEDP). The presented DLAPP-SEDP technique majorly intends to predict the level of air pollution in the smart environment. It follows a three-stage process namely data pre-processing, air pollution prediction, and hyperparameter tuning. At the initial stage, the presented DLAPP-SEDP technique performs various levels of data pre-processing such as missing value replacement, categorical value encoding, normalization, and feature selection. In the next stage, the DLAPP-SEDP technique employs graph convolutional network (GCN) model. Finally, the DLAPP-SEDP technique utilizes atomic orbital search optimization (AOSO) algorithm for optimal hyperparameter tuning process, showing the novelty of the work. To demonstrate the enhanced predictive efficiency of the DLAPP-SEDP method, a wide-ranging experimental analysis can be carried out. The experimental values assured the enhancements of the DLAPP-SEDP method over other recent techniques.