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The scope of the research is to compare conventional concrete and the 10% Novel E-waste replaced concrete material of M30 grade concrete. The composition of various items present in concrete mix are segregated with Novel E-waste 20mm, superplasticizer, cement (53 grade PPC), fine aggregate (sieve size 4.75mm), coarse aggregate (sieve size 20mm), and water. In this research the main set of events are grouped in two categories. Group-1 (control group/conventional concrete) and group-2 (experimental group/Novel E-waste concrete). In this project first group has 18 samples and the Second group has 18 samples, Total samples of N=36 The analysis was performed with a G-power of 0.8, alpha and beta values set at 0.05 and 0.2 respectively, and a 95% confidence interval. The independent sample T-Test yielded a value of (p>0.05), specifically (p=0.256). The One-way ANOVA conducted using SPSS confirmed differences in the mechanical properties between conventional concrete and Novel E-waste Concrete. The average compressive strength for conventional concrete was found to be (31.12 N/mm2), while for Novel E-waste concrete it was (34.15 N/mm2). A significant difference was noted with a significance value of (p<0.05), precisely (p=0.000), and standard deviations of 0.65 and 0.79, respectively. It was determined that Novel E-waste concrete, when mixed with a superplasticizer, exhibited a lower compressive strength percentage compared to conventional concrete. [ABSTRACT FROM AUTHOR]

The scope of this research is about the removal of turbidity by comparing the Novel Oleifera moringa Seed and Strychnos Potatorum seed as a natural coagulant for cheyur lake water.: The novel Oleifera moringa Seed and Strychnos Potatorum seed are used as coagulants to remove turbidity of lake water. Two groups are considered. Group 1 seed is novel Oleifera moringa and group 2 seed is Strychnos Potatorum. Each group has 16 samples. Total sample of N=32. The pretest g power is determined with 80% and with alpha value of 0.05 and confidence intervals are 95% respectively. The lake water having turbidity level is 33.8 NTU before coagulation-flocculation process was done. When the process is done, turbidity removal percentage of novel Oleifera moringa seed is 81.83% and Strychnos Potatorum seed is 75.05% respectively. The SPSS carried out has a significance of p=0.000 (p<0.05). This shows that there is a statistical significant difference between the two methods considered in this study. The standard deviation values are 2.71105and 2.30924 then, standard error is 0.67776 and 0.57731 respectively. The novel Oleifera moringa seed would perform better turbidity removal than Strychnos Potatorum seed. [ABSTRACT FROM AUTHOR]

To enhance the mechanical characteristics for engineering applications, this work aims to produce a unique bio composite that incorporates natural fiber. At 0, 10, 20, and 30% weight fractions, kenaf fiber (KF) is used to strengthen biopolymer poly-butylene succinate (PBS). An injection molding machine was used to create the polymer composite by adding chopped KFs to the molten PBS. A comparison is made between the prepared composite and plain PBS in terms of their machining characteristics, namely drilling. A control group consisting of unreinforced PBS and an experimental group consisting of PBS with KFs added. There were four groups given consideration. Using the G-Power calculator, three samples were analyzed for each group, each with a probability of 0.05 and a 95% confidence interval. Along with increasing their stiffness and hardness, chopped KFs added to PBS also increase their mechanical strength. Measurements are made of the amount of fiber delamination when a solid carbide tool is drilled in a vertical machining centre. A substantial difference between the groups under consideration is found in the analysis, with a significance level of 0.000 (p<0.05). This indicates that the degree of delamination rises when more KFs are included. Though its mechanical strength is greater, adding KFs to the PBS matrix, under the parameters of this investigation, results in increased delamination because the fibers pull out of the matrix during drilling. [ABSTRACT FROM AUTHOR]

The purpose of the study is to evaluate how well two different edge detection techniques differentiate between different guava leaf disease kinds. More specifically, a sample size of 20 is used for each approach to compare the performance of the Laplacian Edge Detection algorithms (LED) and Roberts Edge Detection algorithms (RED). In order to guarantee a 95% confidence interval and a scientific significance level of 0.05, this sample size was calculated. The assessment utilises the Guava Disease dataset, which consists of 2300 photos of guava leaves obtained from a Kaggle repository and includes four different diseases that impact the leaves. The analysis shows that the accuracy of the Laplacian edge identification algorithm is 90.83%, whereas the accuracy of the Robert edge detection approach is 89.6%. Based on statistical analysis, there is a significant difference between the two procedures (independent collection t-test p < 0.05) with an associated p-value of 0.003. Notably, when it comes to guava leaf disease classification, the Laplacian detection of edges technique performs noticeably better than the Roberts method. [ABSTRACT FROM AUTHOR]

This research aims to assess and compare the efficacy of two distinct image segmentation methodologies: the Novel Watershed algorithm and the Adaptive Thresholding approach. The objective is to enhance the precision of white blood cell (WBC) segmentation within microscopic images. The segmentation process employs the Novel Watershed algorithm, designated as group 1, encompassing a sample size of N=10. Simultaneously, the Adaptive Thresholding technique constitutes group 2, also with N=10 samples. The study maintains a pre-test power of 80%, alongside alpha and beta values of 0.05 and 0.2, and a 95% confidence interval. The entire process is executed using MATLAB software. The segmentation accuracy achieved by the Novel Watershed algorithm is 86%, mirroring the accuracy of the Adaptive Thresholding approach, which also yields an 86% accuracy rate. The disparity between these segmentation results is statistically significant, denoted by a significance value of p=0.002 (p<0.05), indicating an error-free outcome. The Novel Watershed algorithm distinctly outperforms the Adaptive Thresholding algorithm in augmenting the accuracy of white blood cell segmentation. [ABSTRACT FROM AUTHOR]