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The fuel known as biodiesel is produced by combining diesel, ethanol, and jatropha oil. Through the utilisation of an outstanding and one-of-a-kind mixing technique known as lorentz-lorentz, the purpose of this study is to determine its density. Following that, a comparison was made between the viscosities of diesel and biodiesel. The strategy and the methodology: The Lorentz-Lorentz mixing method is utilised in the process of preparing the biodiesel sample mixtures. The first group is comprised of biodiesel. We will begin by extracting diesel, ethanol, and jatropha oil from the biofuel mixtures. After that, we will fill the viscometer with a predetermined quantity of each of these substances, and then we will record our readings based on how the fuel blend flows. Within the second group, the diesel is put through its paces. This experiment is repeated using a variety of different fuel blending percentages. When analysing the various fuel blends, a two-sample t-test is carried out with G power at a statistical power of 80 percent and a confidence level of 95 percent. Each group consists of 20 samples, resulting in a total of 40 samples. In order to determine the significance of the viscosity values of diesel and biodiesel mixtures, the fuel blend mixing method was utilised to make the comparison. In comparison to normal diesel, which has a viscosity of 3.881mm2/s, biodiesel has been found to have a viscosity of 4.416mm2/s. The findings were obtained as a consequence of differences that were statistically significant between the research groups, with a p-value that was lower than 0.05. Furthermore, a p-value that is lower than 0.05 shows that biodiesel has a significant value of 0.003, which is a significant value. Both of these groups are very different from one another. To summarise, we used ethanol and jatropha oil to determine the viscosity of the biodiesel. It was proved by the findings that the viscosity value of biodiesel was higher than that of diesel. [ABSTRACT FROM AUTHOR]

This study intends to increase the accuracy of smart irrigation systems that are based on the Internet of Things (IoT) by employing a revolutionary algorithm known as K-nearest neighbour (KNN) rather than Random forest (RF). The Instruments and Methods Used in Research: The Kaggle database served as the source for the dataset used in this investigation. Twenty individuals from Group I and twenty individuals from Group II were needed to take part in the smart irrigation system that had the greatest rate of accuracy. In order to make an accurate prediction regarding the size of the sample, we maintained the G power at 80 percent, the confidence level at 95 percent, and the alpha value at 0.05. In order to develop a smart agricultural system that is based on the Internet of Things (IoT) and has a higher accuracy rate, a sample size of twenty is utilised in conjunction with Random Forest (RF) and Novel K-nearest neighbour (KNN). ThingSpeak, an Internet of Things cloud platform, is used to implement the recommended strategy, which demonstrates that it is more effective than the alternatives. A rate of 96.60 percent is achieved by the K-nearest neighbour (KNN) classifier, which is much higher than the Random Forest (RF) classifier, which has an accuracy rating of 91.55 percent. Both of the groups achieved a significant result with a p-value of 0.001. The two groups are unrelated to one another in terms of the statistical significance of their differences. When it comes to intelligent irrigation systems, the results indicate that the K-nearest neighbour (KNN) method is superior to the Random forest approach (RF). [ABSTRACT FROM AUTHOR]

This study's objective is to determine the refractive index value of biodiesel by utilising the Lorentz-Lorentz mixing method to compare the refractive index values of diesel and biodiesel that are produced by combining jatropha oil, ethanol, and diesel. Additionally, the research will seek to determine the refractive index value of biodiesel. Detailed instructions and materials: For the purpose of producing one-of-a-kind mixes of biodiesel samples, the Lorentz-Lorentz mixing technique is utilised. The first group that we will utilise for this experiment is going to be biodiesel, and we are going to use a refractometer to determine the concentrations of ethanol, diesel, and jatropha oil in a variety of fuel blends. The frequency with which this technique is repeated is determined by the various ratios of blending utilised. Twenty samples are included in each of the groups, for a total of forty samples. The two-sample t-test with confidence levels of 80 percent and 95 percent using G power is utilised in order to evaluate various fuel blends. Group II employed diesel as their control, and they compared the refractive index values of the biodiesel mixture and diesel according to the blending ratio in order to form conclusions regarding the significance of the difference. For the biodiesel mixture, the refractive index value was 1.4285, whereas for the petroleum diesel mixture based on jatropha oil, it was 1.4055. This was the result of the experimental procedure. During the process of experimenting with biodiesel and diesel fuel mixtures, these equations were utilised. There are statistically significant differences between the two groups in terms of fuel type, with a significance value of p<0.05. The biodiesel attained a significant value of 0.001, and the results demonstrated that there are significantly different fuel types between the two groups. According to the findings, diesel fuel and biodiesel blends can have their refractive indices computed with a high degree of precision if the refractive index of each component is taken into consideration. In order to make an accurate prediction regarding the refractive index of diesel and biodiesel mixtures, the formulae can be used as a guide. [ABSTRACT FROM AUTHOR]

In order to find out how well augmented recurrent neural networks operate for detecting when fruit is ripe in comparison to ridge regression, which has lower accuracy. Approach and methodology: Mango samples were collected, stored at room temperature, and checked for ripening, patches, and colour on a regular basis. While taking pictures of the samples, four fluorescent lamps were used, each with a color-rendering index (Ra) of over 95%. the MATLAB 6.5 algorithms for colour analysis, brown spot measurement, image texture analysis, and full-image preprocessing Group 1 (N=20 samples) was given an Enhanced Recurrent Neural Network (ERNN), while group I (N=20 samples) was given ridge regression. The parameters that were determined to define efficient fruit ripening detection were applied to 40 samples, 20 samples each group. Here, Group I serves as the control group, and Group II as the experimental group. We estimated the sample size using the following parameters: G power=80%, confidence level=95%, and alpha=0.05. Findings: It seems feasible to online anticipate the stages of mango ripening using computer vision. In terms of statistical significance, the two groups are distinct from one another. Statgraphics were used to quickly produce classification results. According to the results of the significance test, ERNN and Ridge regression were significantly different from one another (p=0.002). As a result, the produced significant gap is 0.002 (p<0.05). Conclusion: Using the csv (common separated values) and the hunterlab colorimeter, we were able to collect L*, a*, and b* measurements from 160 mango samples from the initial trial with an accuracy of 95.70. [ABSTRACT FROM AUTHOR]

Using the Gladstone-Dale mixing method, the objective of this study is to evaluate and contrast the viscosity of diesel fuel, biodiesel, and a combination consisting of diesel, butanol, and sunflower oil that has been mixed together. Materials and Methods of Procedure: The samples of the biodiesel blend were created through the application of the Gladstone Dale mixing procedure. Obtaining the biodiesel, which is a mixture of butanol, diesel, and sunflower oil, is accomplished by first putting the mixture into the beaker and then dipping it into the hydrometer using the hydrometer. This variety is included in the first group. In Group II, diesel is classified. This technique is repeated multiple times, each time with a different temperature and a different proportion of mixing. The two groups each received twenty out of the forty samples that were collected. Setting the G power to 80 percent and the confidence level to 95 percent were the two decisions that were made in order to calculate the sample size. We utilised SPSS version 26 to analyse the data and assess the significance of the results. This allowed us to calculate the viscosity value by comparing diesel and biodiesel. Findings: Although conventional diesel has a viscosity of 3.9545 mm2/s, biodiesel has a viscosity of 4.2645 mm2/s, which is significantly greater. There was a substantial difference between the two groups, with the most significant values for biodiesel being 0.004 (p<0.05). The two groups were determined to be considerably different from each other. Blend ratios demonstrate that the fuels typically act in a manner that is comparable, even when the temperatures are low. In conclusion, the findings demonstrated that the viscosity of diesel and biodiesel produced from sunflower oil and butanol was identical. When compared to diesel, it was found that biodiesel had a viscosity value that was significantly higher. Biodiesel has the ability to reduce the negative impact that transportation has on the environment and to reduce the amount of reliance that is placed on crude oil that is imported. [ABSTRACT FROM AUTHOR]

The fuel known as biodiesel is produced by combining diesel, ethanol, and jatropha oil. Through the utilisation of an outstanding and one-of-a-kind mixing technique known as lorentz-lorentz, the purpose of this study is to determine its density. Following that, a comparison was made between the viscosities of diesel and biodiesel. The strategy and the methodology: The Lorentz-Lorentz mixing method is utilised in the process of preparing the biodiesel sample mixtures. The first group is comprised of biodiesel. We will begin by extracting diesel, ethanol, and jatropha oil from the biofuel mixtures. After that, we will fill the viscometer with a predetermined quantity of each of these substances, and then we will record our readings based on how the fuel blend flows. Within the second group, the diesel is put through its paces. This experiment is repeated using a variety of different fuel blending percentages. When analysing the various fuel blends, a two-sample t-test is carried out with G power at a statistical power of 80 percent and a confidence level of 95 percent. Each group consists of 20 samples, resulting in a total of 40 samples. In order to determine the significance of the viscosity values of diesel and biodiesel mixtures, the fuel blend mixing method was utilised to make the comparison. In comparison to normal diesel, which has a viscosity of 3.881mm2/s, biodiesel has been found to have a viscosity of 4.416mm2/s. The findings were obtained as a consequence of differences that were statistically significant between the research groups, with a p-value that was lower than 0.05. Furthermore, a p-value that is lower than 0.05 shows that biodiesel has a significant value of 0.003, which is a significant value. Both of these groups are very different from one another. To summarise, we used ethanol and jatropha oil to determine the viscosity of the biodiesel. It was proved by the findings that the viscosity value of biodiesel was higher than that of diesel. [ABSTRACT FROM AUTHOR]

This study intends to increase the accuracy of smart irrigation systems that are based on the Internet of Things (IoT) by employing a revolutionary algorithm known as K-nearest neighbour (KNN) rather than Random forest (RF). The Instruments and Methods Used in Research: The Kaggle database served as the source for the dataset used in this investigation. Twenty individuals from Group I and twenty individuals from Group II were needed to take part in the smart irrigation system that had the greatest rate of accuracy. In order to make an accurate prediction regarding the size of the sample, we maintained the G power at 80 percent, the confidence level at 95 percent, and the alpha value at 0.05. In order to develop a smart agricultural system that is based on the Internet of Things (IoT) and has a higher accuracy rate, a sample size of twenty is utilised in conjunction with Random Forest (RF) and Novel K-nearest neighbour (KNN). ThingSpeak, an Internet of Things cloud platform, is used to implement the recommended strategy, which demonstrates that it is more effective than the alternatives. A rate of 96.60 percent is achieved by the K-nearest neighbour (KNN) classifier, which is much higher than the Random Forest (RF) classifier, which has an accuracy rating of 91.55 percent. Both of the groups achieved a significant result with a p-value of 0.001. The two groups are unrelated to one another in terms of the statistical significance of their differences. When it comes to intelligent irrigation systems, the results indicate that the K-nearest neighbour (KNN) method is superior to the Random forest approach (RF). [ABSTRACT FROM AUTHOR]

This study's objective is to determine the refractive index value of biodiesel by utilising the Lorentz-Lorentz mixing method to compare the refractive index values of diesel and biodiesel that are produced by combining jatropha oil, ethanol, and diesel. Additionally, the research will seek to determine the refractive index value of biodiesel. Detailed instructions and materials: For the purpose of producing one-of-a-kind mixes of biodiesel samples, the Lorentz-Lorentz mixing technique is utilised. The first group that we will utilise for this experiment is going to be biodiesel, and we are going to use a refractometer to determine the concentrations of ethanol, diesel, and jatropha oil in a variety of fuel blends. The frequency with which this technique is repeated is determined by the various ratios of blending utilised. Twenty samples are included in each of the groups, for a total of forty samples. The two-sample t-test with confidence levels of 80 percent and 95 percent using G power is utilised in order to evaluate various fuel blends. Group II employed diesel as their control, and they compared the refractive index values of the biodiesel mixture and diesel according to the blending ratio in order to form conclusions regarding the significance of the difference. For the biodiesel mixture, the refractive index value was 1.4285, whereas for the petroleum diesel mixture based on jatropha oil, it was 1.4055. This was the result of the experimental procedure. During the process of experimenting with biodiesel and diesel fuel mixtures, these equations were utilised. There are statistically significant differences between the two groups in terms of fuel type, with a significance value of p<0.05. The biodiesel attained a significant value of 0.001, and the results demonstrated that there are significantly different fuel types between the two groups. According to the findings, diesel fuel and biodiesel blends can have their refractive indices computed with a high degree of precision if the refractive index of each component is taken into consideration. In order to make an accurate prediction regarding the refractive index of diesel and biodiesel mixtures, the formulae can be used as a guide. [ABSTRACT FROM AUTHOR]

In order to find out how well augmented recurrent neural networks operate for detecting when fruit is ripe in comparison to ridge regression, which has lower accuracy. Approach and methodology: Mango samples were collected, stored at room temperature, and checked for ripening, patches, and colour on a regular basis. While taking pictures of the samples, four fluorescent lamps were used, each with a color-rendering index (Ra) of over 95%. the MATLAB 6.5 algorithms for colour analysis, brown spot measurement, image texture analysis, and full-image preprocessing Group 1 (N=20 samples) was given an Enhanced Recurrent Neural Network (ERNN), while group I (N=20 samples) was given ridge regression. The parameters that were determined to define efficient fruit ripening detection were applied to 40 samples, 20 samples each group. Here, Group I serves as the control group, and Group II as the experimental group. We estimated the sample size using the following parameters: G power=80%, confidence level=95%, and alpha=0.05. Findings: It seems feasible to online anticipate the stages of mango ripening using computer vision. In terms of statistical significance, the two groups are distinct from one another. Statgraphics were used to quickly produce classification results. According to the results of the significance test, ERNN and Ridge regression were significantly different from one another (p=0.002). As a result, the produced significant gap is 0.002 (p<0.05). Conclusion: Using the csv (common separated values) and the hunterlab colorimeter, we were able to collect L*, a*, and b* measurements from 160 mango samples from the initial trial with an accuracy of 95.70. [ABSTRACT FROM AUTHOR]

Using the Gladstone-Dale mixing method, the objective of this study is to evaluate and contrast the viscosity of diesel fuel, biodiesel, and a combination consisting of diesel, butanol, and sunflower oil that has been mixed together. Materials and Methods of Procedure: The samples of the biodiesel blend were created through the application of the Gladstone Dale mixing procedure. Obtaining the biodiesel, which is a mixture of butanol, diesel, and sunflower oil, is accomplished by first putting the mixture into the beaker and then dipping it into the hydrometer using the hydrometer. This variety is included in the first group. In Group II, diesel is classified. This technique is repeated multiple times, each time with a different temperature and a different proportion of mixing. The two groups each received twenty out of the forty samples that were collected. Setting the G power to 80 percent and the confidence level to 95 percent were the two decisions that were made in order to calculate the sample size. We utilised SPSS version 26 to analyse the data and assess the significance of the results. This allowed us to calculate the viscosity value by comparing diesel and biodiesel. Findings: Although conventional diesel has a viscosity of 3.9545 mm2/s, biodiesel has a viscosity of 4.2645 mm2/s, which is significantly greater. There was a substantial difference between the two groups, with the most significant values for biodiesel being 0.004 (p<0.05). The two groups were determined to be considerably different from each other. Blend ratios demonstrate that the fuels typically act in a manner that is comparable, even when the temperatures are low. In conclusion, the findings demonstrated that the viscosity of diesel and biodiesel produced from sunflower oil and butanol was identical. When compared to diesel, it was found that biodiesel had a viscosity value that was significantly higher. Biodiesel has the ability to reduce the negative impact that transportation has on the environment and to reduce the amount of reliance that is placed on crude oil that is imported. [ABSTRACT FROM AUTHOR]