Issue 9

Recently, the environmental pollution is becoming a challenging issue due to the lacking of awareness about the importance of the environment and the growth of transportation facility and the various factories. The Internet of Things (IoT) technology is useful today for collecting and updating the data dynamically from anywhere anytime in less expense. Air quality of different cities is predicted by considering and maintaining the above listed technique which is useful for identifying and predicting the air quality and measure the air pollution level as well. This paper proposes a new IoT aware air quality prediction system that incorporates the newly proposed classifier with fuzzy temporal correlation and the auto-encoded Bidirectional Long Short-Term Memory (Bi-LSTM) to predict the diseases such as heart, cancer and diabetes. Moreover, we propose a new optimization algorithm called Spotted Hyena and Personalized best Cuckoo Search Optimization Algorithm (SHPCSA) for selecting and optimizing the most contributed features that are helpful for enhancing the prediction accuracy with short span of time. The air quality data is collected from various cities like Chennai, Bong Kong, etc. to conduct experiments. The proposed model is proved better than standard classifier with respect to precision, recall, F1-score and accuracy by conducting experiments

The research work's main objective is to correctly examine and find out the proper hydro-geochemistry of groundwater during the dry and wet seasons and hence evaluate it’s quality in India, Tamil Nadu state's Thirukkazhukundram Block. Data from two seasons-the "Pre-monsoon" in August 2016 and the "Post- monsoon" in February 2017-have just been used to do subjective assessments of the chemical composition of the underground water. The results of a thorough geochemical investigation of the subsurface zone are given. Gibbs emphasizes that the majority of samples in both seasons are dominated by evaporation and weathering mechanism sector. The majority of the sample's mixed water type is interpreted hydro-chemically in accordance with Piper's classification. According to the Wilcox diagram, the majority of PRM and POM samples fall into the group of "very good to good and good to permissible." The USSL diagram shows that just 23% of the samples for the C1-S1 type were collected during POM. 23% samples align under C2-S1 during PRM and 57% fall under C2-S1 during POM for. It was noted that 67% and 20% of the samples accounted under C3-S1type respectively, for POM and PRM. The C4-S1type is in PRM only, as of 13% of the samples. The irrigation quality of the groundwater samples showed that they were basically suitable for the very same purpose. Fluoride contamination in groundwater has nowadays become a slowly creeping issue in the study area. Steps need to be taken promptly to avert the resulting human health hazard on account of this pollution, in due course of time. Using ArcGIS 10.0, the groundwater quality chart was produced. Using inverse distance weighting, the regional distribution of groundwater quality measurements was interpolated. The findings of this study highlight the significance of creating techniques for managing aquifer systems.

The goal of this investigation was to focus on the viability of synchronous aging of natural product waste with Aspergillus Niger TISTR 3063 co-societies producing ethanol. Additionally noted was how the aging temperature affected the yield of ethanol. Peels from pineapple and banana were used as substrates, and they were prepared by being slashed into little rectangular pieces. A 250 ml Erlenmeyer jar containing glucose was used to mature several bundles of natural product waste as a control. Analysis of the factors involved in the production of organic waste included sugar, pH, TS, vs, debris, moisture, COD, and TKN. In my review, bio ethanol was produced from banana strips and pineapple strips using pretreatment techniques, more specifically, soluble and acidic pretreatment. The process of aging interaction is then used to produce bio-ethanol. However, the production of bioethanol from food crops needs to be regulated to ensure food security and requires line-by-line analysis to move potential financial and environmental risks. Utilizing Plan Master Programming version 13, determine whether the nature of the bioethanol has changed after synthesis.Using plan master programming, it is possible to see how the item ethanol is being streamlined. Plot the chart for the item's actual and predicted values, one component diagram, and 3D surface. The fuel qualities of the bioethanol that was given were thought to be comparable to those of diesel fuel, and they also complied with ASTM regulations.

The purpose of this research was to comprehensively evaluate the performance and emission characteristics of a diesel engine using a novel fuel blend comprising a hybrid nano-emulsified JME-Diesel blend (B20). The fuel blend consisted of a mixture of 50 ppm of ferric oxide (Fe2O3) and Silicon Dioxide (SiO2) nanoparticles. The engine's performance and emissions were investigated under different load conditions ranging from 0 to 100%. This study involved conducting a series of experiments on an engine to evaluate the engine's efficiency (BTE) and fuel consumption (BSFC). Additionally, emissions in exhaust including NOx, CO, HC, and excess O2 were measured to assess the environmental impact of the hybrid nano-emulsified JME-Diesel blend (B20). The results obtained from the experimental investigations revealed that the adding of nano Fe2O3 and SiO2 to the JME-Diesel blend improved the engine's performance and reduced emissions. An increase in the proportion of nanoparticles 50 ppm led to a significant improvement in BTE, while simultaneously reducing BSFC. Regarding emissions, the hybrid nano-emulsified JME-Diesel blend (B20) demonstrated a considerable reduction in CO2, CO, and HC emissions than neat diesel . Notably, the emission reductions were more pronounced as the nanoparticle proportion increased. This indicates the potential of the hybrid nano-emulsified fuel blend to contribute towards environmental sustainability and mitigate the adverse effects of diesel engine emissions. In insinuation, the results demonstrate the promising characteristics of an engine fueled with a hybrid nano-emulsified JME-Diesel blend (B20). The findings suggest that the inclusion of nano Fe2O3 and SiO2 in the fuel blend can enhance engine efficiency while reducing harmful exhaust emissions.