Trend analysis on CO emissions and their implications: a comparative study between India and China.

This research paper aims to provide a comparative trend analysis of CO emissions from the two largest emitters, India and China. The analysis focuses on the main sources of CO emissions-coal, oil, cement, and gas and their annual data and global share percentages from 1960 to 2019. The study uses non-parametric trend analysis methods, which do not rely on assumptions of normality, outliers, or data length.

The zero-and-plus/minus-one inflated extended-Poisson distribution.

In this paper, we introduce a new distribution defined on , called the distribution, which can be viewed as a natural extension of the zero-and-one-inflated Poisson ( ) distribution. It is designed to fit the count data with potentially excess zeros and/or ones, and/or minus ones. We explore its various properties and investigate the estimation of the unknown parameters.

Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia.

Extensive research has been conducted on poverty in developing countries using conventional regression analysis, which has limited prediction capability. This study aims to address this gap by applying advanced machine learning (ML) methods to predict poverty in Somalia. Utilizing data from the first-ever 2020 Somalia Demographic and Health Survey (SDHS), a cross-sectional study design is considered.

A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves.

Recent innovations have focused on the creation of new families that extend well-known distributions while providing a huge amount of practical flexibility for data modeling. Weighted distributions offer an effective approach for addressing model building and data interpretation problems. The main objective of this work is to provide a novel family based on a weighted generator called the length-biased truncated Lomax-generated (LBTLo-G) family.

Risk factors identification of COVID-19 patients with chronic obstructive pulmonary disease: A retrospective study in Punjab-Pakistan.

Background: Accessibility to the immense collection of studies on noncommunicable diseases related to coronavirus disease of 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an immediate focus of researchers. However, there is a scarcity of information about chronic obstructed pulmonary disease (COPD), which is associated with a high rate of infection in COVID-19 patients. Moreover, by combining the effects of the SARS-CoV-2 on COPD patients, we may be able to overcome formidable obstacles factors, and diagnosis influencers.