The Chandigarh Group of Colleges (or CGC) are the educational institutions located in Sahibzada Ajit Singh Nagar district of Punjab in short distance from cities of Chandigarh and Mohali. Chandigarh Group of Colleges was established in 2001 at Landran initially and at Jhanjeri in 2012.
This study investigates the enhancement of mechanical performance in polylactic acid (PLA) components fabricated using fused deposition modeling (FDM) through the application of a zinc coating via the electric arc thermal spray process. Three major parameters of FDM processing such as infill density (60, 80 and 100 percent), printing speed (20, 40 and 60 mm/s) and the infill pattern (linear, triangular and hexagonal) were systematically studied on tensile and flexural strengths of the coated PLA specimens. Moreover, the experimental design was optimized by means of Taguchi L9 orthogonal array and mechanical properties (tensile and flexural strength) were determined with the help of a universal testing machine (UTM). The findings indicate that tensile and flexural behaviors are controlled by unique optimum combinations of the parameters. The highest tensile strength of 53.44 MPa was achieved at 100 percent infill density, a print speed of 40 mm/s and triangular infill pattern. However, the highest flexural strength of 146.6 MPa was achieved at 100 percent infill density, a print speed of 20 mm/s and hexagonal infill pattern. These results reveal that incorporating FDM with electric arc thermal spray coating could provide a promising path to boosting dramatically the mechanical performance of 3D-printed PLA components to expand their scope of use in high-end engineering disciplines.
Through the Tridosha system of Ayurveda, doctors evaluate human operations through physical and mental features using the Pitta, Kapha, Vata classifications. The researchers developed a combination learning system to process structured Ayurvedic records containing 25 physical and behavioral characteristics of $\mathbf{6, 6 1 5}$ cases for accurate Tridosha type assignments. The preprocessing stage included label conversion as the core operation before SelectKBest executed feature selection and SMOTE performed data balancing on standardized features. The research team created Random Forest and Gradient Boosting and Stacked Ensemble models separately before performing evaluation. The highest performance level was achieved by using Random Forest along with Gradient Boosting as base learners joined by a Logistic Regression meta-learner which generated 99.15% training accuracy and 98.93% validation accuracy, followed by 92.44% external test accuracy. The model demonstrated complete results because it achieved high precision performance along with high recall and an F1-score and an AUC value of 1.00. The use of Ensemble learning methods led to the development of real-time diagnostic tools featuring processed features suitable for Ayurvedic medical diagnostics. The modeling field works to enhance reliability features through developing complex systems and acquiring additional medical data in order to deliver individualized healthcare services.
Abstract This study uses the Taguchi approach to conduct a thorough examination of the Electrical Discharge Machining (EDM) process for optimizing surface roughness (R a ) on ductile cast iron. The effects of major EDM parameters—peak current, pulse-on time, pulse-off time, and jet pressure—were examined using solid copper electrodes. Experimental results showed that using a lower peak current of 5 A, a pulse-on time of 6 µs, and a jet pressure of 10 kg/cm 2 reduced surface roughness to 2.157 µm. A higher peak current of 15 A, pulse-off duration of 5 µs, and jet pressure of 20 kg/cm 2 resulted in a maximum surface roughness of 4.853 µm. The ANOVA findings showed that current was the most relevant parameter, accounting for 62.81% of the total variation in surface roughness, followed by jet pressure (11.69%). The study also used an artificial neural network (ANN) model to predict surface roughness, which yielded a high correlation coefficient (R = 0.95773), verifying the experimental results and displaying great predictive ability. These findings emphasize the importance of current and jet pressure in determining surface finish during the EDM process. The findings contribute to improving the precision and efficiency of EDM, presenting substantial potential for applications in industries that need high-quality machining of sophisticated materials, such as aerospace, automotive, and heavy engineering sectors.
Magnesium ferrite (MgFe₂O₄) is increasingly recognised as a sustainable, visible-light-active, and magnetically recoverable photocatalyst for dye degradation. This review provides a comprehensive and critical analysis of the structural, optical, and magnetic characteristics of MgFe2O4. It further examines how synthesis routes, dopant incorporation, and nanostructural morphology influence its photocatalytic efficiency toward Malachite Green degradation. Special emphasis is placed on understanding the mechanistic aspects of photoexcitation, charge-carrier separation, defect engineering, and the generation of reactive oxygen species (ROS) that underpin its catalytic performance. The article further evaluates recyclability, long-term stability, and process scalability, integrating techno-economic and sustainability perspectives which are very crucial for the industrial implementation. Finally, the review identifies existing knowledge gaps and outlines emerging research opportunities aimed at developing an efficient, durable, and truly green MgFe₂O₄-based photocatalytic systems for the sustainable wastewater treatment applications.
Lutein, the main pigment in the macula, has been widely investigated for its role in retinal health and age-related macular degeneration. Although, its limited water solubility restricts effective ocular delivery. To address these limitations, the present study developed a lutein loaded nanostructured lipid carrier (Lu-NLCs) by applying a nano-template engineering strategy. The composition of Lu-NLCs was optimized using a two-factor, three-level central composite design (CCD) and further refined through a desirability function approach, wherein the effects of two independent variables, namely Compritol and olive oil concentrations, on zeta potential, particle size, entrapment efficiency (
