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This work seeks to enhance the tensile strength of polylactic acid (PLA) by incorporating a novel neem fiber (NF) and bamboo fiber (BF) as reinforcements. Four samples were examined at weight percentages of 90/10, 85/15, 80/20, and 75/25 of PLA to novel neem and PLA to BF, respectively, using a hand lay-up technique in the fabrication process. Tensile testing was conducted independently on each sample, with an experimental analysis using G-power set at 0.8, an alpha of 0.05, beta of 0.2, and a 95% confidence interval. Ten samples each of novel NF and regular BF were tested. The results revealed that the sample with 20% fiber loading exhibited the highest tensile strength. Specifically, the tensile strength of PLA combined with novel NF was measured at 70 MPa, whereas the tensile strength of PLA combined with BF was 59 MPa. Statistical analysis showed a significant difference between the two groups, with a p-value of 0.001 (p<0.05). These findings suggest that the incorporation of novel NF into PLA can substantially enhance the composite material's tensile strength. This improvement makes the material more suitable for various applications requiring high strength and toughness. [ABSTRACT FROM AUTHOR]

The main objective of this research is to compare MRR during CNC machining of creative epoxy composites supported by Sustainable natural fiber (Areca) (10%) and nanocarbon particles made of tamarind seeds (5%) to that of plain epoxy. The hand-layup technique was used to create the samples for both groups. The samples were prepared in accordance with the requirements, and the drilling operation was completed on a CNC (vertical) machine. Each group underwent a total of 20 experiments with a pretest of Gpower 80%. The MRR (material removal rate) of the sample was evaluated and contrasted. Using the statistical software SPSS, T-independent tests were run on the material removal rate. The average material removal rate for Group 1 was 0.38540mm3/sec, compared to 0.11860mm3/sec for Group 2. Group 1 consisted of 10% Sustainable fibre, 5% nanocarbon particles, and 85% other materials (plain epoxy). The results of the independent T- test statistical analysis show that there is a difference in the mean variance of MRR between Groups 1 and 2 (significant at p=0.00, or P<0.05). There is a significant difference between the two groups under study. Within the parameters of this study, it is evident that adding reinforcements like areca fibre and nanocarbon particles significantly improves MRR. [ABSTRACT FROM AUTHOR]

Analyse the surface roughness of Glass/Luffa fiber composite with and without the addition of Shorea robusta filler. For this comparison investigation, 20 samples from each group were used. Epoxy composite with glass/luffa reinforcement in Group 1. Group 2's glass/luffa reinforced epoxy composite with shorea robusta filler. There will be a total of 40 samples, with 20 samples in each group. The intended effect size, alpha level, and power were taken into account while calculating the right sample size for statistical significance using the G-power software. To guarantee acceptable statistical power, a power level of 0.8, alpha of 0.05, and beta of 0.2 will be used. The mean surface roughness of the Group 1 composite was found to be9.1380 µm, while Group 2 showed a significantly less surface roughness of 7.954 µm. A statistical examination indicated a p-value of 0.001, which is below the predetermined significance 0.05(p<0.05). This implies that there is a statistically significant disparity in surface roughness between the two groups. The findings provide Glass/Luffa epoxy composites with 20 % filler reinforcement showed less surface roughness than composite without the addition of filler. [ABSTRACT FROM AUTHOR]

This study was carried out to determine the Flexural Strength of (ABS) Acyroonitrile Butadiene Styrene and the Flexural Strength of (ABS) composites with silica nanoparticles. There were 20 specimens for each group of sample that served as an experimental group that had ABS with filler and a control group comprised of ABS without filler. The sample size was decided at the 80% power, at 0.05 alpha per set. The base materials incorporated were Acrylonitrile Butadiene Styrene with the Silica nanoparticles filler added. This is testified by the fact that the Flexural Strength of ABS Composite in contrast with ABS Composite with 2% volume fraction of novel silica nanoparticles filler was higher. The P value was determined as 0.00 (p<0.05), which is statistically significant and denoting a difference between the two groups. Statistically, the value of ABS with 2% filler was also seen as an observation variable. Hence, the conclusion being that the superior flexural strength of ABS with Novel Silica nanoparticles fillers composite was reached without the introduction of Novel Silica nanoparticles fillers. Indeed the findings are restricted by the small sample size and laboratorial conditions. [ABSTRACT FROM AUTHOR]

The implementation of two optimization methodologies, Biharmonic and Thin Plate Spline, aims to improve the cut-off frequency of InAlGaN HEMT on SiC substrate. This is achieved by altering the aspect ratio of the gate length/width to 0.15nm. The study consists of two groups: Group 1 using Biharmonic optimization (BHO) and Group 2 using Thin Plate Spline optimization (TPSO). Each group has 7 samples, resulting in a total of 14 samples. The statistical analysis shows that there is enough statistical power (G power=0.84) to draw meaningful conclusions. The objective is to enhance the cut-off frequency by utilizing AI-driven optimization to adjust the gate length in HEMT. The SPSS analysis of the independent sample t-test shows a result that is not statistically significant (p>0.05) with a value of 0.216. The Biharmonic optimization obtains a cut-off frequency of 242 GHz, while the Thin Plate Spline optimization achieves 209 GHz within the gate length/width range of 0-35 nm. The highest cutoff frequency is observed at a temperature of 35nm, reaching a value of 242.24 GHz. This study highlights the improved performance of Biharmonic optimization (242 GHz) compared to Thin Plate Spline optimization (209 GHz) in improving the cut-off frequency of InAlGaN HEMT for various gate lengths, underlining its potential for power electronics devices. [ABSTRACT FROM AUTHOR]

The purpose of this project is to analyze and improve performance of automated vehicle theft detection to enhance the security system of vehicles parked in public places using two different machine learning techniques and compare the overall performance. For this research, neural networks namely Novel Resnet -50 and Stochastic Gradient Descent classifiers are chosen. The dataset was divided into 80% for training and 20% for testing. Next, the recommended classifier model was trained on the selected dataset, followed by testing of the model on the remaining 20% of the dataset, with sample size 10 for each group. The Novel Resnet-50 classifier was found to have an accuracy of 90% in improving security to avoid vehicle theft by facial recognition, while the Stochastic Gradient Descent classifier achieved an accuracy of 85%. Further analysis revealed that the Novel Resnet-50 classifier significantly outperformed the SGD classifier, with a significance value of 0.001 (p<0.05) that was obtained by performing an independent sample test. To evaluate the performance of the classifiers, the outcome was measured in terms of Accuracy, specificity and sensitivity. Overall, the study demonstrated that the Novel Resnet-50 classifier has potential for improving security and preventing vehicle theft through facial recognition with a high degree of accuracy. The proposed system can alert the owner if anyone is trying to access or drive the car. Results indicated that the Novel Resenet-50 performed well and offered an acceptable level of functionality, when compared to Stochastic Gradient Descent. [ABSTRACT FROM AUTHOR]

To use the Trickle Approach rather than the Lift and Shift approach in order to address the low efficiency issue that arose during the database migration and guarantee that the entire database is successfully moved in a shorter length of time. For in-depth testing, the Lift and Shift and Trickle Migration approaches are employed. This experiment is conducted using a sample size of 10. The IBM Employee Dataset, which has 1,059 rows of data, is used as an example dataset. In this experimental investigation, the trickle migration technique outperforms the lift and shift approach in terms of cost, time, and data quality. With an efficiency of 77.63% for the Lift and Shift strategy and 86.20% for the Trickle migration approach, the experiment shows the efficacy of these strategies. According to the data reported in this research, the Lift and Shift strategy is less efficient than the Trickle Migration approach. [ABSTRACT FROM AUTHOR]

This research proposes an effective method for improving accuracy in identifying temperament history and text messages and comparing the performance of algorithms. Iterations were conducted during the research for a total sample size of 3062. The csv file repositories on the Kaggle website provided the data set for the study. Which is carried out by setting the G-power as 0.80, with the alpha computation as 0.05 and beta computation as 0.2, with the Confidence range of 95.0%. An accuracy of 93.40% and a loss of 6.59% were achieved with the implementation of the GRU model, which outperformed the Random Forest model with its accuracy of 81.52% and loss of 13.33%. The analysis uncovered a substantial difference among the 2 groups, as evidenced by a calculated p-value of 0.001.GRU algorithm with 93.40% outperforms the RF algorithm with 81.52% in terms of accuracy. [ABSTRACT FROM AUTHOR]