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In this paper, we apply the powerful sine-Gordon expansion method (SGEM), along with a computational program, to construct some new traveling wave soliton solutions for two models, including the higher-order nonlinear Boussinesq dynamical wave equation, which is a well-known nonlinear evolution model in mathematical physics, and the (1+1)-dimensional framework of the Van der Waals gas system. This study presents some new complex traveling wave solutions, as well as logarithmic and complex function properties. The 3D and 2D graphical representations of all obtained solutions, unveiling new properties of the considered model are simulated. Additionally, several simulations, including contour surfaces of the results, are performed, and we discuss their physical implications. A comprehensive conclusion is provided at the end of this paper. [ABSTRACT FROM AUTHOR]

Dried grapes (or Raisins) are among the most frequently grown and consumed cereal crops worldwide. They are also an important source of nutrition and nourishment in a variety of countries including Türkiye, the United States, Greece, etc. In addition to that, raisins consist of 15% water, 79% carbs (including 4% fiber), 3% protein, and very little fat. In our study, there were a total of 900 raisin grains used, with 450 pieces from each type: Kecimen and Besni raisin. Seven morphological features were taken from these images after going through several steps of pre-processing. Since machine learning algorithms can analyze large datasets quickly, automatic classification is made possible. With enough training and testing, machine learning models can attain a high degree of precision in classifying raisin grains. They are able to detect variations in size, shape, color, and texture that would be difficult for humans to detect consistently. Eleven machine learning and five different types of artificial intelligence have been used to classify these features. As part of this study, we look into different machine learning and deep learning methods: GaussianNB, Decision Tree, K-Nearest Neighbor, Random Forest, Support vector machine (SVM), XGBoost, LightGBM, and AdaBoost, Logistic Regression, Artificial Neural Network and Deep Learning Network. Study efficacy is evaluated using standard metrics as F1 score and ROC area under the curve (AUC). Using the caret, H2O, neuralnet, and keras packages, AdaBoost and LightGBM, two of the fourteen models, achieve an accuracy of 90.30% and 98.40%, respectively, and a ROC curve score of around 90%. [ABSTRACT FROM AUTHOR]

Background: Infectious diseases are a leading cause of death and disability worldwide, so it is crucial to plan for their potential impact in order to implement an efficient response. Examining the seasonality and distribution of influenza viruses in Ghana, as well as susceptible demographic groups and circulating strains of the virus, were the objectives of this study. Methods: We worked with a modified version of the Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIR-V) transmission model to forecast the possible outcomes of the influenza pandemic in Ghana. Using the fourth-order Runge-Kutta method, we were able to get numerical simulations for changing the model parameters. We analyzed forecasts for the illness transmission rate β, vaccination rate ρ, and recovery rate γ on a daily and cumulative basis. The average fundamental reproduction number for the parameters β and γ was also rendered graphically. Results: We effectively forecasted the trajectory of influenza-related morbidity using our model, which paves the way for future approaches of controlling and monitoring the flu in our study area. In order to restrict the seasonal influenza, we have provided visual evidence that vaccinated patients and a quarantine in Ghana for at least the next 10 days are needed. It has been noted that the recovery rates of non-vaccinated patients and the vaccination rate work together to reduce the contagious disease. Conclusion: Using precise parameter approximations, theoretical epidemic analysis has proven to be an effective method for predicting and managing the spread of pandemics such as seasonal influenza virus. This model has been transformed into an epidemic model by adding the hospitalized-vaccination compartment for patients with confirmed infections to the SEIR-V model. [ABSTRACT FROM AUTHOR]

This article describes an approach known as the conjugate Gram-Schmidt method for estimating gradients and Hessian using function evaluations and difference quotients, and uses the Gram-Schmidt conjugate direction algorithm to minimize functions and compares it to other techniques for solving ∇f = 0. Comparable minimization algorithms are also used to demonstrate convergence rates using quotient and root convergence factors, as described by Ortega and Rheinbolt to determine the optimal minimization technique to obtain results similar to the Newton method, between the Gram-Schmidt approach and other minimizing approaches. A survey of the existing literature in order to compare Hestenes' Gram-Schmidt conjugate direction approach without derivative to other minimization methods is conducted and further analytical and computational details are provided. [ABSTRACT FROM AUTHOR]

Reproductive coercion has been the primary focus of research on intimate partner violence against women in regard to reproductive health. While studies have taken a look at whether Intimate Partner Violence makes women more or less inclined to use contraception, not much research has been able to provide a comprehensive analysis of the connection between Intimate Partner Violence and reproductive coercion. This particular direction of research has concentrated its attention on both of these aspects when discussing reproductive coercion. It is significant to analyze these things together because it is important to fully understand the condition of reproductive coercion, reproductive choices, and the consequences that modern women are confronting. As a consequence of the negative effects of reproductive coercion on survivors' mental, physical, and emotional well-being, it is imperative that social workers be able to recognize the signs of Reproductive Coercion and provide effective interventions and advocacy on their behalf. The use of contraception in patriarchal power dynamic societies, the relationship between intimate partner violence and reproductive coercion, and the health outcome for women are all issues that could potentially be explained with the use of feminist theory and the constructionist theory that we proposed. [ABSTRACT FROM AUTHOR]

When it comes to making assessments about public health, the mortality rate is a very important factor. The COVID-19 pandemic has exacerbated well-known biases that affect the measurement of mortality, which varies with time and place. The COVID-19 pandemic took the world off surveillance, and since the outbreak, it has caused damage that many would have thought unthinkable in the present era. By estimating excess mortality for 2020 and 2021, we provide a thorough and consistent evaluation of the COVID-19 pandemic's effects. Excess mortality is a term used in epidemiology and public health to describe the number of fatalities from all causes during a crisis that exceeds what would be expected under 'normal' circumstances. Excess mortality has been used for thousands of years to estimate health emergencies and pandemics like the 1918 "Spanish Flu"6. Positive excess mortality occurs when actual deaths exceed previous data or recognized patterns. It could demonstrate how a pandemic affects the mortality rate. The estimates of positive excess mortality presented in this research are generated using the procedure, data, and methods described in detail in the Methods section and briefly summarized in this study. We explored different regression models in order to find the most effective factor for our estimates. We predict the pandemic period all-cause deaths in locations lacking complete reported data using the Poisson, Negative Binomial count framework. By overdispersion test, we checked the assumption of the Poisson model, and then we chose the negative binomial as a good fitting model for this analysis through Akaike Information Criteria (AIC) and Standardized residual plots, after that checking the P-value<0.05; we found some significant predictors from our choosing model Negative binomial model, and the coefficient of all predictors gave the information that some factors have a positive effect, and some has a negative effect at positive excess mortality at COVID-19 (2020-2021). [ABSTRACT FROM AUTHOR]

In this work, we studied convergence rates using quotient convergence factors and root convergence factors, as described by Ortega and Rheinboldt, for Hestenes' Gram–Schmidt conjugate direction method without derivatives. We performed computations in order to make a comparison between this conjugate direction method, for minimizing a nonquadratic function f, and Newton's method, for solving ∇ f = 0 . Our primary purpose was to implement Hestenes' CGS method with no derivatives and determine convergence rates. [ABSTRACT FROM AUTHOR]

Breast cancer is the most lethal form of cancer that can strike women anywhere in the world. The most complex and tough undertaking in order to lower the death rate is the process of predicting a patient's likelihood of survival following breast cancer surgery. Due to the fact that this survival prediction is linked to the life of a woman, effective algorithms are required for the purpose of making the prognosis. It is of the utmost importance to accurately predict the survival status of patients who will have breast cancer surgery since this shows whether or not doing surgery is the actual approach for the specific medical scenario. Given the gravity of the situation, it is impossible to overstate how important it is to investigate new and improved methods of prediction in order to guarantee an accurate assessment of the patient's chances of survival. In this paper, we collect data and examine some models based on the survival of patients who underwent breast cancer surgery. The goal of this research is to evaluate the forecasting performance of various classification models, including the Linear regression model, logistic regression analysis, LDA, QDA, KNN, ANN, and Decision Tree. The results of the experiment on this dataset demonstrate the better performance of the came up with ANN approach, with an accuracy of 82.98 percent. [ABSTRACT FROM AUTHOR]