National Institute of Technology Manipur

Freshwater ecosystems play a crucial role in maintaining ecological balance and supporting biodiversity. Assessing water quality and identifying pollution factors affected by seasonal changes is of utmost importance. The traditional approach for identifying pollution sources and seasonal water quality assessment is often labor-intensive, time-consuming, and complex. In response, our study introduces a hybrid receptor, multivariate statistical and machine learning (ML) based framework as an alternative. Positive matrix factorization (PMF) and receptor model were used to identify key pollutant sources, while Principal Component Analysis (PCA) was used to corroborate pollutant sources. Additionally, various supervised ML classification algorithms are used to predict and identify pollution sources on full physicochemical datasets and reduced in situ parameter scenarios. ML model stability and generalization capacity were evaluated using learning curves, cross-validation method. The findings indicate significant seasonal variability in water quality index, ranging from 38.5 (poor) to 78.6 (good). PMF and PCA revealed three dominant pollution factors, such as biogeochemical processes, sediment resuspension/nutrient influx, and anthropogenic pollution, affecting the seasonal water quality by 15-30%, 25-45%, and 40-70%, respectively. Model performance indicators using confusion matrices, ROC-AUC, and other evaluation metrics, for full dataset identified XGBoost as the best model, achieving the highest accuracy of 96.3%, followed by CatBoosting (93.6%), Random Forest (93.6%), Support Vector Classifier (91.8%), K-Nearest Neighbors (87.2%) and Decision Tree (84.3%). However, with the in situ data the ML models show stable and consistent classification accuracy in the range of approximately 70-81%. Overall, the modelling framework provides an effective approach for capturing seasonal variability and pollution source attribution, contributing to improved ecological understanding and predictive management of freshwater system.

A remote sensing index is often used to identify meteorological and agricultural droughts. Google Earth Engine analyzes CHIRPS data from 2015 to 2024 and Landsat-8/Sentinel-2 data from 2020 to 2024. The Vegetation Condition Index (VCI), Temperature Condition Index (TCI), composite Vegetation Health Index (VHI), and Standardized Precipitation Index (SPI) were calculated for four seasons using NDVI, EVI, LST, and CHIRPS precipitation data to explain specific spatiotemporal trends. Meteorological and agricultural droughts include precipitation deficits and vegetation stress. From the study, pre-monsoon analysis reveals significant intra-seasonal correlations between VCI and VHI (0.84) and TCI and VHI (0.75), indicating that moisture reserves and thermal stress influence vegetation health during arid periods. The VCI-VHI correlation (0.91) predominates during the monsoon season, indicating plant growth amidst substantial precipitation. As the season nears peak aridity, the correlations between post-monsoon and winter TCI-VHI increase (0.81 and 0.83), signifying thermal stress. A weak correlation (≤ 0.50) between SPI and vegetation indices across the seasons indicates that current precipitation does not succeed in reliably predicting vegetation stress, since vegetation depends on accumulated soil moisture rather than instantaneous rainfall. Vegetation indices exhibit substantial temporal persistence: Pre-monsoon VCI conditions are strong predictors of winter VCI (0.98), VHI forecasts winter VHI (0.92), and TCI predicts winter TCI (0.87), thereby enabling nine-month drought forecasting. The findings demonstrate that vegetation indices serve as drought indicators for seasonal water resource planning and agricultural vulnerability assessment in monsoon-affected nations.

Growing concerns over resource depletion and rampant material pollution have sparked enormous interest in the use of natural, renewable fillers for the formulation of polymer composites. In this paper, Pistachio Shell Powder (PSP), an easily available agro-processing waste product, is comprehensively investigated as a biorenewable natural filler for the development of green and sustainable PLA biocomposites. Experimental design and analysis were performed using a statistical approach, Response Surface Methodology - Box-Behnken Design (RSM-BBD). This methodology was then applied for the optimization of the material properties by systematically altering the main input parameters, which are represented by the filler loading, melting temperature, pressure, and speed. The material characterization data obtained were optimized by using RSM for multifactor analysis, and the validity of the statistical design was carefully checked by the use of ANOVA. The key results obtained from this work clearly pinpoint PSP as a promising, high-potential, and eco-friendly reinforcement for biodegradable PLA biocomposites.

In this paper, we consider new extremal problems in the uniform norm between a univariate complex polynomial and its associated reciprocal polynomial involving a generalized B ‐operator. Our first result deals with inequality for the upper bound of a polynomial having s ‐fold zero at the origin governed by generalized B ‐operator, and as applications of this result, we are able to prove two inequalities for upper bounds of polynomials having s ‐fold zero at the origin considering the placement of the other zeros of the polynomials in | ζ | ≥ k , k ≤ 1 under the condition that both comparing circles of radii r , R lie inside the circle of radius k or that one circle lies inside and the other outside. Further, we obtain an interesting upper bound for another class of polynomials having all its zeros in | ζ | ≤ k , k > 0 under similar above conditions on the two comparing circles. Besides, a similar result for a self‐inversive polynomial is also obtained. Moreover, each of the obtained results implicates some existing known estimates and their analogues for the extremum modulus of a polynomial.