Nagpur Institute of Technology (NIT), is an engineering college in Nagpur district that is affiliated to Rashtrasant Tukadoji Maharaj Nagpur University and approved by All India Council for Technical Education, New Delhi, Directorate of Technical Education, Maharashtra, Mumbai, NAAC..
Wire-arc additive manufacturing (WAAM) is increasingly adopted for fabricating large-scale metallic components. However, reliable prediction and multi-objective optimization of weld-bead geometry remain critical challenges. This study systematically investigates the influence of voltage, wire feed speed (WFS), and torch travel speed (TS) on bead width (BW), bead height (BH), and microhardness (MH) using a structured Taguchi L18 experimental design. Statistical regression modeling and analysis of variance (ANOVA) were employed to identify significant process parameters and establish physically interpretable predictive relationships. To further assess potential nonlinear behavior, the XGBoost machine-learning algorithm was implemented as a surrogate model. Given the limited dataset size (n = 18), leave-one-out cross-validation (LOOCV) was adopted to obtain unbiased predictive performance estimates. Cross-validated results indicate that XGBoost provides performance comparable to linear regression within the investigated parameter window, achieving R²_LOOCV values of 0.879 (BW), 0.770 (BH), and 0.870 (MH). The comparatively lower prediction accuracy for BH is attributed to inherent melt pool instability and droplet transfer variability. Multi-objective grey relational analysis identified a locally optimal parameter combination of 15 V, 5.61 m/min WFS, and 10.9 mm/s TS within the defined design space, enabling simultaneous minimization of BW and maximization of BH and MH. The developed framework provides a physically grounded and cross-validated predictive optimization methodology for WAAM parameter selection within constrained single-bead deposition conditions.
Currently, the development of luminescent materials is a major focus of research.
Medical research doesn’t always begin in high-tech laboratories, it often takes root in everyday clinical encounters, in a doctor’s observation, or a pathologist’s note that sparks a new line of inquiry. Yet with many contributors and stages between idea and outcome, giving proper credit and protecting intellectual ownership becomes increasingly complex. Traditional intellectual property (IP) systems are often too slow, bureaucratic, and rigid for today’s fast-paced, collaborative research environments. As a result, valuable insights can remain vulnerable to misuse or uncredited appropriation. This paper explores how digital timestamping, when integrated with blockchain and cryptographic mechanisms, can offer a more immediate, tamper-proof, and transparent approach to protecting research ownership. It introduces a decentralized, multi-layered framework designed to document and preserve contributions at every stage of the medical research lifecycle. By ensuring verifiable authorship and data integrity, the proposed model aims to make intellectual attribution fairer, more traceable, and less dependent on lengthy administrative procedures. The study also discusses potential challenges, including scalability, implementation costs, and user adoption, while emphasizing how such a system can be extended to other domains of collaborative innovation beyond medicine.
Cardiac failure is a severe disease, which is generally found using the measurements of Left Ventricular Ejection Fraction (LVEF) using Magnetic Resonance Imaging (MRI) images. Even though MRI provides multiple images of the Left Ventricular (LV), which is effective in diagnosing cardiac failure, it consumes more time and cost. If the LV failure is not treated well, then it increases the mortality rate. In this research, Xception SpinalNet (XSp-Net) is developed to classify LV failure by combining the Xception module and SpinalNet. Initially, a cardiac MRI visual is given as input, and the Adaptive Weighted Median Filter denoises the MRI image in the pre-processing phase. The preprocessed visual is subjected to LV segmentation, which is done by the Fully Convolutional Network (FCN). Subsequently, the features, like Convolutional Neural Network (CNN), Complete Local Binary Pattern (CLBP), Local Optimal Oriented Pattern (LOOP), and Gray Level Co-occurrence Matrix (GLCM) are extracted from the image, which is segmented. These mined features are fed to the proposed XSp-Net to classify the LV failure. Furthermore, the highest accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) recorded by XSp-Net are 91.04%, 92.10%, and 91.34% respectively for the learning set 90%.
