Journal of Mathematics and Statistics Studies

The importance of using scientific and quantitative methods in addressing contemporary problems, including the (Covid-19) pandemic, as these challenges and problems require everyone, especially those working in educational institutions and researchers, to support international and local efforts to reduce the impact of this pandemic by achieving optimal use of medical resources, for the supply chain of medical resources that includes (therapeutic protocol and medical supplies (; Thus, providing solutions, alternatives and logistical support that would absorb the significant increases in the number of injuries in light of the limited resources in the face of this pandemic. Hence, this research came to contribute to the local and international efforts to address this problem by presenting a package of ideas and solutions for how to achieve the optimal utilization of medical resources. In light of the inaccuracy and discrepancy in the available data by distributing those resources in a quantitative and thoughtful manner to achieve the goal for which it was set, as well as evaluating alternatives on the ground and ways to improve them with an indication of the future prospects for this problem is by applying one of the artificial intelligence techniques called the fuzzy inference system (FIS).

The British Pound Sterling (GBP) to Nigerian Naira (NGN) exchange rate has been grossly affected by the Coronavirus 2019 (Covid-19) pandemic. It has become pertinent to identify robust models that will help to cope with the variability associated with the pandemic. Many original studies found the ARIMA method to be highly useful in modeling and forecasting exchange rates. However, not much work has been done on modeling the GBP and NGN exchange rate during the covid-19 pandemic using machine learning models. This study focuses on modeling the exchange rate between the GPB and NGN during the period of the Covid-19 pandemic by adopting the process of model comparison using the Artificial Neural Network (ANN), Autoregressive Integrated Moving Average (ARIMA), and Random Forest models to obtain an optimal model and forecasts from the model. Secondary data of the GBP to NGN exchange rate within the period of the Covid-19 pandemic from exchangerate.org.uk were used. The two machine learning models (ANN and random forest) performed better than the ARIMA model. The RF, though performed well in the training set, was outperformed in the test set by the ANN model. The ANN model was chosen to model and forecast the GBP and NGN exchange rate during the Covid-19 pandemic. The predicted fall in the GBP to NGN exchange rate to 570 by December 2021 and 575 by September 2022 using the ANN model will have a huge effect on the economy of the country as the country depends largely on imported goods. The Government and policymakers must put in place structural measures that will avoid the looming crisis.

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).

Let be an absolute valued algebra containing a nonzero central algebraic element. Then is a pre-Hilbert algebra and is finite dimensional in the following cases: 1) A satisfies (x, x, x)=0. 2) A satisfies (x2, x2 , x2 )=0. 3) A satisfies (x, x2, x)=0. In these cases is isomorphic to or . It may be conjectured that every absolute valued algebra containing a nonzero central element is pre-Hilbert algebra.

In [8], we have proven that if is an absolute valued algebra containing a nonzero central algebraic element, then is a pre-Hilbert algebra. Here we show that is finite dimensional in the following cases: 1) A satisfies (x2, x, x) = 0 or (x, x, x2) = 0, 2) A satisfies (x2, x2, x) = 0 or (x, x2, x2) = 0, . In these cases A is isomorphic to R, C, H or O.

This study examined the factors affecting the academic achievement of undergraduate students at the faculty of Arts and Science Kufrah -Benghazi University as a case study. This study seeks to identify and analyze some determining factors that influence students' academic achievement in the study area. Four factors namely: psychological, family, learning facilities, and economic; were considered. The sample was randomly selected from the study population. A questionnaire was administered to 240 (90 males,150 females) students as a sample of this study. The responses of the students were analyzed to meet the objectives of the study using (SPSS) and displayed in forms and tables. After collecting the required data on the variables of the study, they were encoded to be entered into the computer to extract the statistical results. Statistical methods within the Statistical Package for Social Sciences (SPSS) were used to process data obtained by the field study of the sample. To analyze the data mean difference test is used. Results of arithmetic means of the psychological, family, learning facilities, and economic factors were medium. Furthermore, there were no statistically significant differences in the factors affecting academic achievement among the participants due to some demographic factors such as gender and marital status. following recommendations were made; provide proper learning facilities to the students and also improve the environment of the faculty. Furthermore, the students would perform well if they are properly guided by both their parents and teachers.

This study aims to determine the relationship of Self Efficacy as a mediator between Motivation and Engagement and Academic Performance in Mathematics. In order to determine the students’ assessment of their self-efficacy, motivation, and engagement, the researcher used the survey method. The researcher utilized The Revised Study Process Questionnaire developed by Biggs et al. (2001), and the grades the students got from their most recent Mathematics course were used. The data were analyzed using SPSS 20.0 software program using Andrew Haye’s Model 4. Based on the results, it is revealed that: 1) motivation influences students’ academic performance, 2) self-efficacy also influences students’ academic performance, and 3) self–efficacy is not a mediating factor between motivation and academic performance.

In this research, we will address the importance of geographical location in relation to the supply chain and the process of choosing a location, relying on basic factors, and building a mathematical model using linear programming to choose the most important cities that have a high degree of connectivity (Accessibility) in Iraq from being economical or service distribution centers, either subsidiary or It has a high response speed and contributes to building a logistical transportation model in Iraq. The sports model, after the solution, has reached the selection of the best cities in terms of logistics.

The study introduced a special case of the Poisson-Generalized Gamma empirical Bayes model to survey states in Nigeria with a higher risk of fatal accidents. Monte Carlo error and stationary dynamic trace plots were used to validate model convergence and accuracy of the posterior estimates. The main results included the disease mappings that revealed Ebonyi had the highest risk of road vehicular fatal accidents in Nigeria with a relative risk estimate of 1.4120 while Abuja had the lowest risk with a relative risk estimate 0.5711. In terms of geopolitical region, the risk of road vehicular fatal accident is highest in South-South region with a relative risk estimate of 1.1850 while North-Central had the lowest risk with a relative risk estimate of 0.7846. The study is to aid planned government programs to ameliorate vehicular road carnage in Nigeria.

This study aims to alleviate the uncertainties faced by prospective students during the application process by developing a predictive model for admission probabilities based on CGPA and GRE scores. The research investigates the significance of these predictor variables about the response variable, "Chance of Admit." Employing linear regression analysis, the model is thoroughly examined to evaluate its adequacy, predictive accuracy, and the need for interaction terms. The findings indicate that both CGPA and GRE scores play a crucial role in forecasting admission chances, with an adjusted R2 value of 0.0835, suggesting an 80% reduction in variance around the regression compared to the main line. The diagnostic plot of the model confirms its precision, revealing minimal deviations from linearity and normality in residuals. Furthermore, the study addresses concerns about multicollinearity using the Variable Inflation Factor (VIF) and finds no significant correlation between GRE Scores and CGPA. In summary, this research presents a robust predictive model for student admission probabilities, offering valuable insights for both prospective applicants and educational institutions.

Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown their profound impact in solving the high-scale and non-differentiable engineering problems. This paper presents a comparative study of the most widely used nature-inspired optimization algorithms for solving engineering classical design problems, which are considered challenging. The teen metaheuristic algorithms employed in this study are, namely, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization Algorithm (BBO), Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Cuckoo Search algorithm (CS), Differential Evolution (DE), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). The efficiency of these algorithms is evaluated on teen popular engineering classical design problems using the solution quality and convergence analysis, which verify the applicability of these algorithms to engineering classical constrained design problems. The experimental results demonstrated that all the algorithms provide a competitive solution.

Kilmer and Zheng (2021) recently introduced a generalized version of the alternating harmonic series. In this paper, we introduce a new generalization of the alternating harmonic series. A special case of our generalization converges to the Kilmer-Zheng series. Then we investigate several interesting and useful properties of this generalized, such as a summation formula related to the Hurwitz -Lerch Zeta function, a duplication formula, an integral representation, derivatives, and the recurrence relationship. Some important special cases of the main results are also discussed.

This paper suggests a new technique for finding the analytical approximate solutions to two-dimensional kinetically reduced local Navier-Stokes equations. This new scheme depends combines the q-Homotopy analysis method (q-HAM) , Laplace transform, and Padé approximant method. The power of the new methodology is confirmed by applying it to the flow problem of the lid-driven square cavity. The numerical results obtained by using the proposed method showed that the new technique has good convergence, high accuracy, and efficiency compared with the earlier studies. Moreover, the graphs and tables demonstrate the new approach’s validity.

This work explores Kink soliton solution, periodic soliton solution, and rational function solutions for the fractional generalized anti-cubic (FGAC) nonlinearity in fiber Bragg gratings (BGs). The rational fractional ((D_ζ^α G)/G)-expansion method is employed in conjunction with the idea of a conformable fractional derivative. Due to its nature, the soliton solution looks to have some restrictions.

Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach.

The estimation of a suitable probability model depends mainly on the features of available temperature data at a particular place. As a result, existing probability distributions must be evaluated to establish an appropriate probability model that can deliver precise temperature estimation. The study intended to estimate the best-fitted probability model for the monthly maximum temperature at the Sylhet station in Bangladesh from January 2002 to December 2012 using several statistical analyses. Ten continuous probability distributions such as Exponential, Gamma, Log-Gamma, Beta, Normal, Log-Normal, Erlang, Power Function, Rayleigh, and Weibull distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the temperature data, several goodness-of-fit tests were applied, including the Kolmogorov-Smirnov test, Anderson-Darling test, and Chi-square test. The Beta distribution is found to be the best-fitted probability distribution based on the largest overall score derived from three specified goodness-of-fit tests for the monthly maximum temperature data at the Sylhet station.

This study's primary goal is to examine the characteristics of public university canteen food service. A saying goes, "Health is riches." Therefore, it not only helps them to clear their minds but also enables them to focus on their studies, families, and careers. A model was created from the information that was provided and tested using information from a survey that was carried out at a college in northwest Pennsylvania. The findings imply that staff behavior, food quality, and price are the three key factors that affect student satisfaction. Cleanliness, responsiveness, and environment are further important factors. Considering these factors (food quality, food variety, price justice, ambiance, etc.) could help people in charge of food services provide more value and satisfaction to improve students' entire educational experience.

Explosive Ordnance Risk Education Messages (EORE) is a multi-criteria decision-making problem (MCDM) based on three steps, namely, the identification of distinct evolution criteria, the significance criteria, and the variation of data. Because it makes use of a more sophisticated classification technique, the group decision method (GDM) based on weighted arithmetic mean (AM) to prioritize (EORE) messages is the proper approach. In contrast to GDM, which explicitly weights each criterion, GDM implicitly weights each alternative's criterion values. With the help of the new hybrid method weighting technique, we can overcome this theoretical difficulty by providing explicit weights for criteria generated with zero inconsistencies and combined with the new distance-based weighting method. SFS (spherical fuzzy set) is used in hybrid methods, although it can only be used to solve the ambiguity associated with the theoretical concerns outlined above.

The primary purpose of this work was to prove the boundedness of the Fractional Maximal Operator in Grand Herz Spaces on the Hyperplane. Here, We defined Grand Herz Space in a continuous Case. For Simplicity, We divided our Problem into two theorems by taking two subsets of Hyperplane( ) as ( ) and its complement . We proved the boundedness of the Fractional Maximal Operator in Grand Herz Space on these two subsets of Hyperplane. We also defined the continuous Case of Grand Herz Space. We proved some results to use in our proof. We represented other terms this paper uses, i.e. the Hyperplane and Fractional Maximal operator. Our proof method relied on one of the corollaries we gave in this paper. We proved the condition to apply that corollary, and then by referring to this, we confirmed both of our theorems. This paper is helpful in Harmonic analysis and delivers ways to analyse the solutions of partial differential equations. The Problem of our discussion provides methods to study the properties of very complex functions obtained from different problems from Physics, Engineering and other branches of science. Solutions of nonlinear Partial Differential equations often resulted in such functions which required deep analysis. Our work helps check the boundedness of such types of functions.

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.

Three major tools are required to investigate the controllability of control systems, namely, determining matrices, index of control systems and controllability Grammian. Determining matrices are the preferred choice for autonomous control systems due to the fact that they are devoid of integral operators in their computations. This article developed the structure of certain parameter-ordered determining matrices of generic double time-delay linear autonomous functional differential control systems, with a view to obtaining the controllability matrix associated with the rank condition for Euclidean controllability of the system. Expressions for the relevant determining matrices were formulated and it was established that the determining matrices for double time-delay linear autonomous functional differential control systems do not exist if one of the time-delays is not an integer multiple of the other paving the way for the investigation of the Euclidean controllability of generic double time-delay control systems.

Managing the blood supply network is crucially important. The lack of blood might result in patient problems and even death. Blood loss, on the other hand, results in hefty expenses. To reduce the levels of shortage and wastage, the blood product supply chain must make the best decisions possible. Numerous writers have researched this field because of the intricacy and significance of the blood supply chain. This essay aims to provide an overview of research on the blood supply chain. Studies that were published from 2015 to 2022 were therefore examined and categorized. This survey's main contribution is to update the body of research on the blood supply chain with a new classification and critically evaluate the state of the art in this field. Environments for making decisions, problems with the blood supply chain's design, working methods, decision-making, modeling, problem-solving methods, and data features are among the suggested categories. In addition, the shortcomings and inadequacies in the existing literature are emphasized, and potential study approaches are presented.

This paper developed and established unprecedented global results on the structure of determining matrices of generic double time-delay linear autonomous functional differential control systems, with a view to obtaining the controllability matrix associated with the rank condition for the Euclidean controllability of the system. The computational process and implementation of the controllability matrix were demonstrated on the MATLAB platform to determine the controllability disposition of a small-problem instance. Finally, the work examined the computing complexity of the determining matrices.

Landau’s conjecture and Shanks’ conjecture state that there are infinitely many prime numbers of the forms x2+1 and x4+1 for some nonzero integer , respectively. In this paper, we present a technique for studying whether or not there are infinitely many prime numbers of the form x2+1 or x4+1 derived from some Lucas sequences of the first kind {Un(P,Q)} (or simply, {Un}) or the second kind {Vn(P,Q)} (or simply, {Vn}) , where P greater or equal to 1 and Q= 1 or -1. Furthermore, as applications we represent the procedure of this technique in case of x is either an integer or a Lucas number of the first or the second kind with x greater or equal to 1 and 1 less or equal to P less or equal to 20.

With the growing competition among firms in the globalized corporate environment and considering the complexity of demand forecasting approaches, there has been a large literature on retail demand forecasting utilizing various approaches. However, the current literature largely relies on micro variables as inputs, thereby ignoring the influence of macroeconomic conditions on households’ demand for retail products. In this study, I incorporate external macroeconomic variables such as Consumer Price Index (CPI), Consumer Sentiment Index (ICS), and unemployment rate along with time series data of retail products’ sales to train a Long Short-Term Memory (LSTM) model for predicting future demand. The inclusion of macroeconomic conditions in the predictive model provides greater explanatory power. As anticipated, the developed model, including this external macroeconomic information, outperforms the model developed without this macroeconomic information, thereby demonstrating strong potential for industry application with improved forecasting capability.