Gangadhar Meher University, Amruta Vihar, formerly Sambalpur College and Gangadhar Meher College, is a state university situated in Sambalpur, Odisha, India. It is named after the Odia poet, Gangadhar Meher. N Nagaraju is the current Vice-Chancellor, while Smt. Jugaleswari Dash is the Registrar and Uma Charan Pati is the Deputy Registrar.
In the present work, using the so-called Mohand integral transform, we obtain the solutions of strongly nonlinear fractional differential equations such as modified Kawahara and Gardner equations via the Caputo fractional derivative. Further, Adomian polynomials are being used to decompose the nonlinear terms involved in the fractional-order models. The effectiveness of this approach is illustrated by taking two practical examples. The results indicate its robustness and efficiency across different derivatives of fractional order.
Let T_ℓ ,k(n) represent the number of k-tuple ℓ -regular partitions of n. In this study, we investigate the arithmetic behavior of T_5^2k-1,6(n) . To analyze this, we first derive congruences for T_5,6(n) modulo powers of 5, then construct generating functions for T_5^2k-1,6(n) along certain arithmetic progressions. Through this approach, we derive infinite families of Ramanujan-type congruences that hold modulo powers of 5.
This study examines the hydrogeochemical behavior and salinization processes affecting groundwater in the Kujang area, Eastern Odisha, India. Groundwater was very hard during both pre-monsoon (PRM) and post-monsoon (POM) periods due to elevated total hardness (TH). The chemical composition displayed high concentrations of Na⁺, Cl⁻, Mg2⁺, electrical conductivity (EC), total dissolved solids (TDS), and TH, reflecting saltwater–freshwater mixing within the coastal aquifer. The ionic dominance sequence Mg2⁺ > Na⁺ > Ca2⁺ > K⁺ = Cl⁻ > HCO3⁻ > SO42⁻ indicates saline influence. Multivariate statistical analyses (PCA and correlation), combined with the Base Exchange Index (BEX), Seawater Mixing Index (SMI), and ionic ratios, suggested the effects of seawater intrusion. Additional factors contributing to groundwater salinity include over-pumping, agricultural return flow, and wastewater infiltration. Simpson’s ratio [Cl⁻/(HCO3⁻ + CO32⁻)] suggests that most samples fall within moderate to injurious contamination categories. The Hydrochemical Facies Evolution (HFE) diagram revealed that 86
ABSTRACT Climate change is a huge concern with far‐reaching negative consequences for many industries, including agricultural ecosystems. Having a broad understanding of the evolving landscape of research relating to climate change and its impact on agriculture is very important for stakeholders as well as policymakers. The present study explores the intellectual structure and bibliometric analysis of the available literature on climate change and agriculture. Using data from the Scopus database, we examine publication trends, citation networks, thematic evolution, geographic distribution, and co‐authorship networks to provide a holistic view. We conducted a bibliometric analysis of a total of 1426 papers ranging from the period from 1989 to 2023 (January). Our results will help in understanding the knowledge gaps and emerging areas within climate change and agriculture research. By uncovering these knowledge gaps, this study aims to guide future research directions and facilitate evidence‐based policymaking. The findings provide important resources for researchers, practitioners, policymakers and decision‐makers striving to develop sustainable solutions and adaptive strategies that address the impacts of climate change on agriculture, ensuring long‐term food security and environmental resilience.
Perovskite microwave dielectric ceramics have been identified as a class of promising nanomaterials due to their exceptional dielectric, electronic, and optical properties, making them a potential candidate for next-generation sustainable energy applications. The dielectric constant of perovskite microwave dielectric ceramics is a crucial factor in designing supercapacitors, solar cells, and energy-harvesting systems. Hence, accurately predicting the dielectric constant is essential in designing materials for a specific purpose. However, the conventional methods, such as classical theory and density functional theory, used for the prediction of the dielectric constant are time-consuming and expensive. Prediction using machine learning algorithms allows high accuracy, fast screening, and reduced trial and error. This research work focuses on exploring and analyzing the performance of various machine learning algorithms to estimate the dielectric constant of perovskite structures by leveraging material descriptors and structural features. The predictive framework developed in this work can accelerate the design and suitable choice of features of perovskite microwave dielectric ceramics. The computational techniques presented in this study are assessed by performance metrics like root mean squared error, mean squared error, mean absolute error, and coefficient of determination. Experimental results show that both the extreme gradient boosting and random forest algorithms offer superior outcomes compared with other counterparts. The mean squared error of both algorithms is found to be 9%. This research work exhibits the potential of integrating computational material science with data-driven models for advancing nanotechnology solutions in sustainable energy systems and environmental management.
