Supaul College of Engineering

This work presents a comprehensive investigation of the structural, magnetic, optical, and dielectric properties of praseodymium-doped barium hexaferrite nanomaterials, BaPrxFe12−xO19 (x = 0.00 and 1), synthesized via a heat treatment method at 850 °C. Fourier transform infrared (FTIR) spectroscopy confirms the formation of the hexaferrite phase through characteristic metal–oxygen vibrational modes and enables the evaluation of force constants, bond lengths, and functional groups. X-ray diffraction (XRD) analysis reveals the formation of M-type hexaferrite with a hexagonal structure, accompanied by a minor BaFe2O4 secondary phase, while providing detailed crystallographic parameters influenced by Pr3+ substitution. Thermogravimetric analysis (TGA) demonstrates excellent thermal stability of the samples beyond 780 °C, indicating their suitability for high-temperature applications. Magnetic characterization using vibrating sample magnetometry (VSM) shows a notable enhancement in magnetic behavior with Pr incorporation, attributed to lattice distortion and modified superexchange interactions. Optical properties investigated via UV–Visible spectroscopy reveal a direct band gap, which increases systematically with Pr concentration, highlighting effective band gap tuning—one of the key novel outcomes of this study. Dielectric and ferroelectric measurements indicate that Pr3+ ion substitution suppresses electrical leakage by stabilizing the lattice and hindering charge carrier mobility, thereby enhancing remnant polarization and coercive polarization. Overall, the improved structural stability, enhanced magnetic performance, tunable optical band gap, and favorable dielectric characteristics establish Pr-doped BaFe12O19 nanomaterials as promising multifunctional candidates for advanced optical, magnetic, electronic, and potential biomedical applications.

Air pollution is a widespread global environmental hazard that poses significant threats to both human health and the earth’s ecosystems. Particulate matter (PM2.5) is one of the most perilous forms of air pollution if present in higher concentrations. This tiny material is incorporated into the air by both anthropogenic and natural activities and may further lead to poor ambient air quality. The size of these particles has been directly linked to their reactivity and potential impact on human health. Inhalable coarse particles 2.5 to 10 μm in diameter and fine particles < 2.5 μm in diameter are the primary concern due to their high surface-to-volume ratio. Owing to their diverse physicochemical characteristics, such as the heterogeneous mixture of particle sizes, small diameters, and chemical components, PM2.5 have been found to be associated with many respiratory and reproductive-related disorders, cardiovascular diseases, central nervous system dysfunctions, and cancer. In this review article, we have highlighted the sources, occurrence, and human toxicological effects of PM2.5, as well as their role in the progression of various human diseases. Various preclinical and epidemiological studies are also covered to reveal the harmful effects of PM2.5 on human health worldwide. This comprehensive analysis of PM2.5 may help policymakers and other stakeholders adopt more stringent measures to phase out PM2.5 levels and mitigate its negative impacts on human health and the ecosystem.

Drought is a natural hazard that cannot be entirely prevented but can be effectively monitored and mitigated through timely prediction and spatio-temporal analysis. This study assesses the efficacy of remote sensing and meteorological drought indices for monitoring agricultural droughts in western Rajasthan, India. Meteorological indices including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Self-Calibrated PDSI (scPDSI), and Rainfall Anomaly Index (RAI) are evaluated alongside the remote-sensing-based Standard Vegetation Index (SVI). A modified Mann–Kendall test is applied to detect significant trends, and the Degree of Dryness Index (DDI) is used to quantify drought severity over time. Results indicate a strong correlation of SVI with SPI (6, 9, and 12 months), SPEI (particularly 12-month), PDSI, and scPDSI. Notably, SVI, scPDSI, and 12-month SPEI reveal statistically significant increasing trends. Spatial analysis confirms the alignment between remote-sensed SVI and meteorological indices during both dry (2009) and wet (2011) years. SVI values also exhibit significant correlations with the yield of major rainfed crops—Deshi cotton (r = 0.742), maize (r = 0.67), bajra (r = 0.599), and groundnut (r = 0.59). These findings reinforce the potential of SVI as a reliable remote-sensing tool for agricultural drought assessment in arid environments.

Construction projects are inherently affected by uncertainties arising from site variability, resource availability, and subjective scheduling judgments. This study proposes a unified hybrid framework that integrates Fuzzy Logic, Opposition-Based Learning (OBL), and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) to address multi-objective trade-offs involving Time, Cost, Quality, and Safety (TCQS) under uncertainty. Triangular Fuzzy Numbers (TFNs) are used to model uncertain parameters such as activity durations, direct costs, quality indices, and safety risks. These are defuzzified using the centroid method to enable effective optimization. A real-world construction case study comprising 21 activities, each with five execution modes, is used to demonstrate the approach. The proposed Fuzzy-OBL-NSGA-III algorithm yields 25 high-quality Pareto-optimal solutions, balancing the conflicting TCQS objectives. Performance metrics and comparative analysis with existing models such as MOACO, MOTLBO, MODE, standard NSGA-III, and Fuzzy-MOPSO confirm the superior diversity, convergence, and computational efficiency of the proposed model. Trade-off plots and correlation analyses further validate its decision-support capabilities. The framework provides construction planners with a dynamic, scalable, and uncertainty-resilient scheduling tool, enabling strategic decision-making in complex, real-life project environments.

To achieve a smooth surface finish and minimum tolerances on cylindrical objects, cylindrical grinding is a popular practical method in industry. In this process, material is removed from the workpiece surface as small chips, and after this process, post-processing is not required. A grinding wheel is used in this process. Currently, there are various grinding processes, such as creep-feed grinding, surface grinding, centerless grinding, internal grinding, and cylindrical grinding. This process is used to grind external cylindrical parts such as spindles, connecting rods, cam shafts, engine shafts, axles, propeller shafts, etc. A cylindrical grinding machine is used to achieve a high machining rate and a high surface finish. The effect of the process parameters on the response parameters is the main goal of this work. Here, the most influential parameters are speed, feed rate, and the depth of cut. The response parameters are the metal removal rate and Ra. In the present work, machining is performed on EN24 alloy steel, and the objective is to achieve the specified material removal rate (MRR) and surface finish. The EN24 is a very high-strength steel alloy that is widely used in aircraft and heavy vehicle crankshafts, connecting rods, gearshafts, etc. To find the optimum set of process parameters, a Taguchi design of experiment with an L16 orthogonal array was used. Later, the experiment results are optimized by gray relational analysis (GRA). In the proposed work, the minimum surface roughness and the maximum MRR are obtained. The parameters that greatly influence obtaining the optimum result are the feed rate and the speed of machining.