Vikram University

This paper is based on the results of petrological and geochemical studies carried out on coal samples taken from Lower Gondwana coal-bearing horizons (Barakar Formation) of Sohagpur coalfield of Burhar-Amalai sub-basin of Madhya Pradesh (India). The study also shows that the coals of this basin are striated in nature and are dominated by ‘striated, striated dim’ and ‘striated bright’ components. Moreover, vitrinite (mean 33.1 vol

This paper proposes two innovative and efficient classes of estimators for estimating the population mean utilizing auxiliary information in simple random sampling. The bias and mean squared error of the proposed estimators are derived up to the first order of approximation under the simple random sampling without replacement scheme. The optimal conditions for minimizing the mean squared error of the newly developed estimators are determined. Efficiency conditions are derived by comparing the mean squared error of the proposed and existing estimators. To validate the study, an empirical analysis is conducted using four real population datasets, and a simulation study is conducted with 80,000 iterations. Based on the results from both the empirical and simulation studies, recommendations are made in favor of the suggested estimators.

This study reports the facile in-situ fabrication of a chitosan-supported Fe/Co/Ni trimetallic nanocomposite designed as a high-performance catalyst for environmental remediation. Structural and surface analysis, including XRD, XPS, FESEM and EDS, confirmed the formation of a highly crystalline trimetallic spinel oxide phase uniformly anchored within the biocompatible chitosan scaffold. Spectroscopic investigations (FTIR and XPS) elucidated a synergistic mixed-valence electronic environment involving Fe2+/Fe3+, Co2+and Ni2+ cations. These metallic centers are stabilized through coordination with the nitrogen and oxygen lone pairs of the chitosan backbone, and ensuring long-term structural integrity. The catalytic efficiency was evaluated through a comparative kinetic study focusing on the reductive degradation of Methylene Blue (MB), Rhodamine B (RhB), and the inorganic probe potassium ferricyanide (K3[Fe(CN)6]). All reduction processes obeyed pseudo-first-order kinetics, revealing the catalyst’s superior electron-transfer capabilities. The nanocomposite demonstrated multifunctional utility by facilitating the oxidative photo-Fenton mineralization of organic dyes under visible light via hydroxyl radical (⋅OH) generation. The catalyst exhibited excellent reusability and magnetic recoverability, retaining > 91

In many real-world situations, the elements of a population exhibit considerable variation in size. For instance, in surveys related to tuberculosis cases, hospitals or clinics may serve vastly different numbers of patients. Similarly, in evaluating the effects of government subsidy programs, the populations of villages can vary significantly. In such contexts, Probability Proportional to Size (PPS) sampling emerges as a more suitable and effective method. This study introduces a new class of estimators aimed at enhancing the estimation of the population mean within the PPS sampling framework. The bias and mean squared error (MSE) of the proposed estimators are obtained using first-order approximations. To assess their efficiency, a simulation study and empirical analysis using real-world data were carried out. The findings demonstrate that the proposed estimators outperform existing methods, as indicated by their lower MSE values and higher percent relative efficiency (PRE).

Given the increasing pressures on global food production caused by a rapidly rising population and climate change, precise crop yield prediction has emerged as an important component of sustainable agriculture. This research delves into identifying and evaluating multiple crop yield prediction methodologies, focusing mainly on application with the Artificial Neural Networks (ANNs) model. The study highlights the problems with traditional statistical approaches like Linear Regression, and compare with them the capabilities of more modern machine learning methods especially ANN, highlighting their additional explanatory power and predictive accuracy. This research uses a dataset of 1,000,000 records with many features like region, soil type, crop type, and climatic variables to establish comprehensive basis for analysis. This study relies on mutual information gain to identify influential features. Our research takes advantage of rich dataset and adaptability of ANN model to reveal insights in optimizing agricultural output while enhancing food security. The proposed ANN model demonstrated superior performance with an R ^2 score of 0.9 and a Mean Squared Error (MSE) of 0.25, indicating high accuracy and minimal prediction error. In contrast, the Lasso Regression model achieved an R ^2 of 0.76 with an MSE of 0.39, while the Decision Tree Regressor performed better with an R ^2 of 0.82 but still had a higher MSE of 0.51, showcasing the ANN model’s overall efficiency. The work brings out this potential change that can be adopted by ANN in the prediction of agricultural yield to create better future prospects for precision farming. This work emphasises on ANN-based approaches that can be integrated into precision farming systems for predicting crop yield, offering scalable and efficient solutions for enhancing productivity.