Was there voter fraud in the 2021 Peru Presidential Elections?

This paper performs a forensic study of the Peru’s presidential election on June 6th, 2021 between Pedro Castillo and Keiko Fujimori, where ex-candidate Keiko Fujimori claimed there had been irregularities. We calculate three p-values that help us determine if there was fraud. The consensus of the results indicates that there was no manipulation of the results.

Experimental Analysis PVP Coated Silver Nanofluid Properties for Application in Photovoltaic/Thermal (PVT) Collectors

Nowadays, supplying energy for the global population has turned into a prominent issue for countries engendering the consumption of huge amounts of fossil fuels which leads to some serious environmental problems. Among the renewable energy technologies, solar collectors can play major role to improve the efficiency, in air conditioning utility systems by minimum pollution. In photovoltaic/thermal (PVT) solar collectors, which are currently considered as the most advanced type to produce electricity and heat simultaneously, working fluid absorbs Energy from photovoltaic (PV) module engendering to decrease temperature of PV module and increase the electricity efficiency and also provide permissible amount of heat for other residential applications. Meanwhile, utilizing nanofluid as the working fluid in collector, regarding that the nanofluid has enhanced thermal properties relative to the base fluid, leads to a higher collector efficiency. In this research, PVP coated silver nanofluid was prepared in three volume concentration being 250, 500 and 1000 ppm by two-step method. To assess the stability of nanofluid the zeta potential is calculated which is obtained -41.6 V. Also, the prominent thermal properties of the nanofluid were analyzed regarding PVT solar collector applications. According to the results, thermal conductivity of the PVP coated silver nanofluid, improves the properties of base fluid, to the extent that thermal conductivity coefficient grows up 50% in some temperatures and increased from 0.594 for base fluid to 1.098 W/mK by escalation of concentration to 1000 ppm. Thus, PVP coated silver nanofluid can be deemed as the vital working fluid to improve the performance of PVT solar collectors.

Determining the Efficiency of Fuzzy Logic EOQ Inventory Model with Varying Demand in Comparison with Lagrangian and Kuhn-Tucker Method Through Sensitivity Analysis

This paper considers an EOQ inventory model with varying demand and holding costs. It suggests minimizing the total cost in a fuzzy related environment. The optimal policy for the nonlinear problem is determined by both Lagrangian and Kuhn-tucker methods and compared with varying price-dependent coefficient. All the input parameters related to inventory are fuzzified by using trapezoidal numbers. In the end, a numerical example discussed with sensitivity analysis is done to justify the solution procedure. This paper primarily focuses on the aspect of Economic Order Quantity (EOQ) for variable demand using Lagrangian, Kuhn-Tucker and fuzzy logic analysis. Comparative analysis of there methods are evaluated in this paper and the results showed the efficiency of fuzzy logic over the conventional methods. Here in this research trapezoidal fuzzy numbers are incorporated to study the price dependent coefficients with variable demand and unit purchase cost over variable demand. The results are very close to the crisp output. Sensitivity analysis also done to validate the model.

Genetic Algorithm Coupled with Neural Networks to Guesstimate the Subsurface Features of the Earth

Electrical resistivity method is often used to estimate the subsurface structure of the earth. Many inversion algorithms are available to estimate the subsurface features. However, predicting the exact parameter in the non-linear subsurface of the earth is difficult because of its complex composition. Soft computing tools can approximate the subsurface parameters more clearly. Each soft computing tool has certain advantages and disadvantages. A hybrid formation of algorithms will make the decision more appropriate than depending on a single tool. Here in our study the data obtained through Vertical Electrical Sounding has been used to determine the sub surface characteristics of earth viz., true resistivity and thickness. Artificial Neural Networks (ANN) requires certain optimizing procedures. Here in this paper, Genetic Algorithm (GA) is applied to optimize Artificial Neural Networks (ANN). This coupled approach is tested with the field data. Error percentage of algorithm nearly mimics the behavior of earth and is verified. The best performance result shows that this technique can be implemented to estimate the non-linear characteristics of the earth more noticeably.

Artificial Neural Network Model for Rainfall Data Analysis During 2004-2017 in Tamil Nadu, India – Prevailing Pattern Evaluation on Climate Change

This research paper focuses on rainfall variations in Tamil Nadu, India using Wavelet, Linear regression and Artificial Neural Networks model from 2004 to 2017. As the rainfall is the key factor in understanding climate change, the seasonal datasets from 2004-2017 of Tamil Nadu state has been taken for study. The salient feature of this study is the application of Neural Networks and wavelet analysis. It reveals that the rainfall variations are ambiguous that it does not maintain a constant pattern. Wavelet coefficients of multiresolution spectrogram reveals that the intensity of rainfall in each year. Linear regression model divulge the pattern of rainfall followed in every season and the results show that except winter season all other season suffers deficient rainfall. The deficiency of rainfall may be due to different parameters like ElNino or LaNina pattern or global warming. Results showed that all seasons except winter does not maintain consistency in the rainfall variability. Winter season provides the positive slope values of 4.7 and 0.6 for January and February respectively. Moreover Artificial Neural Networks training provides prominent results of Regression value 0.98 which is comparably high with other seasons taken for study.

Monte Carlo Approach To Genotype By Environment Interaction Models

Understanding the implication of Genotype-by-Environment (GXE) interaction structure is an important consideration in plant breeding programs. Traditional statistical analyses of yield trials provide little or no insight into the particular pattern or structure of the GXE interaction. In this study, efforts were made to solve these problems under different level of data occurrence. We employed the simulation process of Monte Carlo in generating since use of a real-life data may pose a serious difficulty. In this paper, we simulated for two data Types of Balance and Unbalance designs with different Levels of generations (3X3, 7X7, 10X10, and 3X7, 7X3, 7X10, 10X7 , , respectively). We therefore check the performance of GXE interaction on four different models (AMMI, FW, GGE and Mixed model), and also their stability and adaptability. The findings revealed that, when the assumption was maintained, AMMI outperformed Finlay-Wilkinson model, GGE Biplot model and Mixed model.