This study aims to analyze the effect of the drill method on the mathematics learning outcomes of seventh-grade students at SMP Negeri 5 Jombang. The pre-test results were tested for normality using the Shapiro-Wilk test and for homogeneity using the Bartlett test. The pre-test results showed the data were normally distributed and homogeneous. The t-test indicated that the pre-test mean scores between the control and experimental classes were the same. However, the post-test data for the experimental class were not normally distributed, so homogeneity was tested using the Levene test. The Mann-Whitney test showed a significant difference, proving that the drill method affects students’ learning outcomes.

The main key objective of this paper is to address the nonresponse problems by adapting Hansen and Hurwitz’s technique (1946) and Saini et al.’s estimator (2022) to propose a novel estimator of population mean under sub-sampling technique using multiple auxiliary variables. A comparative analysis of the proposed novel estimator’s efficacy has been performed through theoretical and numerical studies. The results of this paper confirm that our estimator is more effective than others under the same situation.

The purpose of this paper is to present an analytically easy to use procedure for estimating of extreme quantiles of continuous random variables using the Peak Over Threshold approach, and a statistically sound approach to the problem of threshold selection that needs to be resolved in this context. A web link included in the text points to a ready-to-use implementation of the proposed method in the popular programming language Python.

Purpose: The purpose of the study is to investigate blue zones lifestyle on Indian diet management system through optimized diet plans. The study explores menu planning with plant-based, animal and dairy-based recipes promoting longevity and reduction of chronic diseases in India. Design/Methodology/Approach: The macro and micro nutrients data is collected for the regionally available food items in India. The study proposed linear programming problems to maximize the calories with 66 food items, satisfying the Required Nutrient Intake (RNI) for normal individuals living in rural and urban areas of India. Findings: Three optimization models such as Linear Programming Problem (LPP), Integer Linear Programming (ILP) and Stigler’s diet programming (SDP) were proposed for selecting menus with varying calorie ranges (1900-3100 kcal). The percentage of nutrients contained in the diet plans were close to blue zones food guidelines adoptable to Indian population. Originality/Value: The revised Stigler Diet Problem (SDP) have well optimized objective function with highest accommodation of recipes in optimal menus. This approach is helpful to nutritionists and dieticians for preparing the affordable diet plans for distinct income groups. Also, the study provides insights to policy makers working on improving the health conditions of people by adopting the blue zone diet.

In the present paper, the analysis of growth and instability in production, area and yield of wheat for some wheat growing states of India is carried out by computing compound growth rate (CGR) and Cuddy-Della Valle (CDV) instability index on utilizing secondary time series data of wheat pertaining to the period 2011-2020 in the concerned states. The percentage change in production, area and yield of wheat is examined by considering the base year as 2011. Moreover, the percentage share of production, area and yield of wheat are demonstrated graphically for the year 2020.

This study aims to determine the effect of resampling RACOG and RACOG-RUS data on Gradient Boosting and Naïve Bayes classification in predicting water quality with unbalanced data. The data used in this study were 720 data from January 2022 to December 2023. It was found that Gradient Boosting performed best when using RACOG-RUS resampling data and feature selection with a number of numIntances of 200. While Naïve Bayes has the best performance when using RACOG-RUS resampling data without feature selection with a number of numIntances of 300. It can be seen that resampling RACOG data does not outperform RACOG-RUS in both classification models because it is known that the data generated in RACOG does not make the dataset more balanced than RACOG-RUS. Hybrid sampling is necessary if RACOG samples are used as the training dataset.

We propose and develop the four-parameter Harris Extended Fréchet distribution. It is obtained by inserting the two-parameter Fréchet distribution as the baseline in the Harris family and may be a useful alternative method to model income distribution and could be applied to other areas. We demonstrate that the new distribution can have decreasing, increasing and upside-down-bathtub hazard functions and that its probability density function is an infinite linear combination of Fréchet densities. Some standard mathematical properties of the proposed distribution are derived, such as the quantile function, ordinary and incomplete moments, incomplete moments, Lorenz and Bonferroni curves, Gini index, Renyi and β-entropies, mean residual life and mean inactivity time, probability weighted moments, stress-strength reliability, and order statistics. We also obtain the maximum likelihood estimators of the model. The potentiality/flexibility of the new distribution is illustrated by means two applications to failure and waiting time data sets.

Diabetes Mellitus, a chronic metabolic disorder stemming from fluctuations in blood glucose and insulin levels, exerts profound impacts on every organ, significantly compromising overall health. While a permanent cure remains elusive, proactive management can control the disease’s extent. Early detection is pivotal in averting its onset. This research employs Exploratory Data Analysis (EDA), coupled with SMOTE analysis, to unveil patterns, correlation, characteristics, and data structures. For diabetes classification, Support Vector Machine (SVM), Extreme Gradient Boosting (XG Boost). Random Forest (RF), Logistic Regression (LR) and Decision Tree (DT) optimized by Bees Optimization, were employed. Metrics like the F1 Score, ROC curve, accuracy, precision, and recall are used to carefully evaluate the model’s performance. In order to determine the parameters that support classification, this model was tested using the PIMA Indian dataset and real-time datasets. For the real- time dataset with BOA, the SVM model scored an astounding 98.86% accuracy, but for the PIMA dataset, it only managed a 96% accuracy. As a result, this study proves that, in comparison to cutting-edge techniques, combining EDA with SMOTE and ML with BOA produces better outcome.

This study offers a comprehensive bibliometric analysis of investment efficiency (IE) research, focusing on the integration of advanced technologies and ESG (environmental, social, and governance) criteria in modern financial decision-making processes. Utilizing the Bibliometrix R package, the analysis reviews 950 peer-reviewed articles from 1970 to 2023. It employs advanced techniques such as factorial analysis, thematic evolution mapping, and collaboration network analysis to explore the intellectual structure and thematic progression of IE research. The data is extracted from the Web of Science Core Collection to ensure a high-quality dataset. The study identifies major contributions in areas such as corporate governance, capital management, and the integration of emerging technologies like artificial intelligence, machine learning, and blockchain. ESG factors are highlighted as a transformative theme reshaping investment strategies and the overall direction of the field. This study provides a framework for understanding IE’s evolution, offering insights for researchers on ESG integration and technology’s role. Practitioners can apply these findings to enhance strategies, while policymakers can use them to promote responsible investing and capital innovation.