Copper(I)-Catalyzed Formal [4 + 2] Cyclocondensation of -Hydroxybenzyl Alcohol, Aromatic Terminal Alkynes, and Sulfonyl Azides: An Alternative Approach to 2-Sulfonyliminocoumarins.

In this paper, an alternative and efficient copper(I)-catalyzed synthesis of 2-sulfonyliminocoumarins is developed through a three-component reaction of -hydroxybenzyl alcohol, alkynes, and -toluenesulfonyl azide. The proposed route for access to the 2-iminocoumarin ring involves a [4 + 2] hetero-Diels-Alder reaction between -quinone methide and ketenimine intermediates generated in situ.

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era.

The COVID-19 pandemic has had a significant impact on students' academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits.

A NEW flexible exponent power family of distributions with biomedical data analysis.

Probability distributions are widely utilized in applied sciences, especially in the field of biomedical science. Biomedical data typically exhibit positive skewness, necessitating the use of flexible, skewed distributions to effectively model such phenomena. In this study, we introduce a novel approach to characterize new lifetime distributions, known as the New Flexible Exponent Power (NFEP) Family of distributions.

A robust cusum control chart for median absolute deviation based on trimming and winsorization.

Statistical quality control is concerned with the analysis of production and manufacturing processes. Control charts are process control techniques, commonly applied to observe and control deviations. Shewhart control charts are very sensitive and used for large shifts based on the basic assumption of normality.

Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction.

A novel flexible exponent power-X family of distributions with applications to COVID-19 mortality rate in Mexico and Canada.

This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short).

Monitoring the process mean under the Bayesian approach with application to hard bake process.

This study introduces the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart within the framework of measurement error, examining two separate loss functions: the squared error loss function and the linex loss function. We conduct an analysis of the posterior and posterior predictive distributions utilizing a conjugate prior. In the presence of measurement error (ME), we employ a linear covariate model to assess the control chart's effectiveness.

Performance of Bayesian EWMA control chart with measurement error under ranked set sampling schemes with application in industrial engineering.

The objective of this study is to investigate the behavior of the Bayesian exponentially weighted moving average (EWMA) control chart in the presence of measurement error (ME). It explores the impact of different ranked set sampling designs and loss functions on the performance of the control chart when ME is present. The analysis incorporates a covariate model, multiple measurement methods, and a conjugate prior to account for ME.

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques.

The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions, and precautionary measures related to the disease on social media platforms. Despite the considerable support on social media, a large number of fake propaganda and conspiracies are also circulated. People also reacted to COVID-19 vaccination on social media and expressed their opinions, perceptions, and conceptions.

Adaptive EWMA control chart using Bayesian approach under ranked set sampling schemes with application to Hard Bake process.

The memory-type control charts, such as cumulative sum (CUSUM) and exponentially weighted moving average control chart, are more desirable for detecting a small or moderate shift in the production process of a location parameter. In this article, a novel Bayesian adaptive EWMA (AEWMA) control chat utilizing ranked set sampling (RSS) designs is proposed under two different loss functions, i.e.

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model.

In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic.

Scaling up tobacco cessation within TB programmes: findings from a multi-country, mixed-methods implementation study.

Background: Brief behavioural support can effectively help tuberculosis (TB) patients quit smoking and improve their outcomes. In collaboration with TB programmes in Bangladesh, Nepal and Pakistan, we evaluated the implementation and scale-up of cessation support using four strategies: (1) brief tobacco cessation intervention, (2) integration of tobacco cessation within routine training, (3) inclusion of tobacco indicators in routine records and (4) embedding research within TB programmes.

Methods: We used mixed methods of observation, interviews, questionnaires and routine data.

Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan.

In this paper, we have focused on machine learning (ML) feature selection (FS) algorithms for identifying and diagnosing multidrug-resistant (MDR) tuberculosis (TB). MDR-TB is a universal public health problem, and its early detection has been one of the burning issues. The present study has been conducted in the Malakand Division of Khyber Pakhtunkhwa, Pakistan, to further add to the knowledge on the disease and to deal with the issues of identification and early detection of MDR-TB by ML algorithms.

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments.

In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD).

Regulatory genes identification within functional genomics experiments for tissue classification into binary classes via machine learning techniques.

Objective: The aim of this study is to filter out the most informative genes that mainly regulate the target tissue class, increase classification accuracy, reduce the curse of dimensionality, and discard redundant and irrelevant genes.

Method: This paper presented the idea of gene selection using bagging sub-forest (BSF). The proposed method provided genes importance grounded on the idea specified in the standard random forest algorithm.

Forecasting COVID-19 in Pakistan.

Objectives: Forecasting epidemics like COVID-19 is of crucial importance, it will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. In this study, the popular autoregressive integrated moving average (ARIMA) will be used to forecast the cumulative number of confirmed, recovered cases, and the number of deaths in Pakistan from COVID-19 spanning June 25, 2020 to July 04, 2020 (10 days ahead forecast).

Methods: To meet the desire objectives, data for this study have been taken from the Ministry of National Health Service of Pakistan's website from February 27, 2020 to June 24, 2020.

The analysis of risk factors associated with tonsillitis in district Mardan, Pakistan.

Objective: To assess the risk factors associated with tonsillitis.

Methods: The cross-sectional study was conducted at Mardan Medical Complex and District Headquarter Hospital, Mardan, Pakistan, from January to June 2018, and comprised tonsillitis patients. Data was collected using a questionnaire which included different risk factors like age 1-10 years, gender, residential area, dietary habit etc.

A New Extended- Family of Distributions: Properties and Applications.

During the past couple of years, statistical distributions have been widely used in applied areas such as reliability engineering, medical, and financial sciences. In this context, we come across a diverse range of statistical distributions for modeling heavy tailed data sets. Well-known distributions are log-normal, log-, various versions of Pareto, log-logistic, Weibull, gamma, exponential, Rayleigh and its variants, and generalized beta of the second kind distributions, among others.

A Flexible Reduced Logarithmic- Family of Distributions with Biomedical Analysis.

Statistical distributions play a prominent role in applied sciences, particularly in biomedical sciences. The medical data sets are generally skewed to the right, and skewed distributions can be used quite effectively to model such data sets. In the present study, therefore, we propose a new family of distributions to model right skewed medical data sets.

Risk factors associated with childhood asthma in District Mardan, Pakistan.

Objective: To estimate the prevalence of asthma in children aged <10 years, and to identify important risk factors for asthma..

Methods: The case-control study was conducted at Mardan Medical Complex and District Head Quarters Hospital, Mardan, Pakistan, from June to September 2017.

Sample size issues in multilevel logistic regression models.

Educational researchers, psychologists, social, epidemiological and medical scientists are often dealing with multilevel data. Sometimes, the response variable in multilevel data is categorical in nature and needs to be analyzed through Multilevel Logistic Regression Models. The main theme of this paper is to provide guidelines for the analysts to select an appropriate sample size while fitting multilevel logistic regression models for different threshold parameters and different estimation methods.

The frequency of various diseases among patients attending Tehsil Headquarter Hospital Khwazakela, Swat.

This hospital-based study was conducted in THQ (Tehsil Headquarter) Hospital Khwazakhela, district Swat in April 2018, to determine the incidence of various diseases among patients in general and the cases attended in the OPD (out patients department) in particular. One year of data was taken from April 2017 to March 2018, of all the patients who attended the THQ Hospital to check the frequency of individual diseases, month wise, gender wise, age wise as well as, case wise. Information on patients attending OPD with respiratory, gastro intestinal, urinary tract diseases and other communicable diseases were compiled.