JECRC University

The study of heat transport in fluid flow past a coaxial cylinder is crucial for several engineering and industrial applications where effective temperature management and mass diffusion are crucial. This study enables researchers to systematically identify the most significant characteristics that influence heat transfer performance. The Taguchi approach enables robust optimization by reducing variability and increasing design efficiency. Combining statistical analysis and simulation improves the forecasting accuracy of theoretical models. Motivated by this, the current research examines the flow of liquid past a coaxial cylinder with the effects of endothermic/exothermic chemical reactions. It is considered that the movement is entirely formed by the stretchy action of the internal cylinder, which is surrounded by a fixed outer cylinder. The Taguchi optimization approach is used in conjunction with Analysis of Variance and multivariate regression analysis to enhance the system's heat transfer in relation to specific parameters. The results reveal that the exothermic chemical reactions significantly enhance temperature and concentration fields due to internal heat generation, and endothermic chemical reactions suppress the heat transfer by absorbing energy from the flow domain. The rise in the chemical reaction parameter (58.5517%) strongly alters the heat transfer rate, followed by thermophoresis (28.40%) and Brownian motion (13.33%).

Biofilm mediated antimicrobial resistance (AMR) has become a critical global health and economic challenge, affecting both community and healthcare settings. Microbial Biofilms significantly enhance the antibiotic tolerance and cause the persistent and device-associated infections via limited drug penetration, degradation of antibiotics, and assist horizontal gene transfer. Biofilm-mediated antimicrobial resistance remains a major obstacle to treating infectious diseases today. Biofilms can boost antibiotic tolerance by up to 1,000 times and lead to chronic, persistent, and device-associated infections. The lack of FDA-approved anti-biofilm drugs highlights the urgent need for new therapeutic strategies and mechanistic insights. Redefining the treatment landscape and improving outcomes for resistant infections could be achieved through a multi-platform therapeutic approach. This review summarizes recent developments in our knowledge of how biofilms contribute to antibiotic resistance and highlights new therapeutic strategies, such as nanotechnology, antimicrobial peptides, bacteriophage-derived enzymes, quorum-sensing inhibitors, CRISPR-based tools, microbiome engineering, and AI-driven drug discovery.

In this research paper, we utilize an analytical technique to investigate the behavior of the Drinfeld-Sokolov-Wilson equation of arbitrary order. The implemented technique is an adequate composition of the Kharrat-Toma transform and the q-homotopy analysis approach. Here, a regularized form of the Hilfer-Prabhakar derivative of arbitrary order is used to formulate the problem. The Drinfeld-Sokolov-Wilson equation of arbitrary order is utilized to model the dispersive water waves and plays a very significant role in fluid dynamics. The results of the discussed model are presented graphically to show the efficiency and reliability of the obtained results.

MgH2 has garnered attention for its robust capacity (7.6 wt%) and economic viability. However, its sluggish hydrogen desorption kinetics and elevated hydride decomposition temperature pose significant hurdles. This study delves into the catalytic prowess of the ternary metal oxide Na2CrO4, enriched with alkali and transition metals, in augmenting MgH2 behaviour. The findings elucidate that the introduction of 10 wt% Na2CrO4 slashes the decomposition temperature of MgH2 to 215.6 degrees C, a stark contrast to the 417 degrees C observed for pristine MgH2. Employing the Kissinger equation, a significant decrement of 58.5% in the activation energy for desorption was observed for the composite with 10 wt% Na2CrO4 catalyst. The activation energy Ea was calculated as 70.5 f 4.3 kJ/mol for desorption while for absorption it is 72.1 f 3.8 kJ/mol. Thermodynamic profiles were charted as pressure-composition isotherms stated about unchanged enthalpy & entropy. The rate measure analysis indicated a significant enhancement in hydrogen absorption rates for catalyzed composites by achieving 5.19 wt% hydrogen absorption in just 1 min at 300 degrees C and rate of absorption is observed as 0.05 wt% per minute at 50 degrees C. Kinetic fitting with JMAK model unfolded dimensional defect mechanism for hydrogen absorption. Morphological, structural, and phase transition dynamics during reactions were meticulously scrutinized by employing cutting-edge techniques such as SEM-EDX and X-ray diffraction (XRD). The catalytic mechanism of kinetic enhancement was explored via XPS analysis and it is suggested that in-situ formed Cr2O3 and other species from the precursor Na2CrO4 is responsible for fast de/hydrogenation.

Deep Learning architecture on breast cancer is usually constructed in a particular imaging type, which restricts their application use in different workflows of diagnostics. This research explores the concept of cross-modality transfer learning by fine-tuning a convolutional neural network on breast ultrasound images to classify cancer by using the histopathology approach. The BreakHis dataset is experimented upon four magnification levels (40X, 100X, 200X and 400X). In order to provide good evaluation, both image level cross-validation and patient wise cross-validation are used in a five-fold system to provide robust evaluation. The presented method uses stain-aware preprocessing techniques with optical density-based normalization and augmentation, powerful regularization techniques such as MixUp, stratified sampling and label smoothing to overcome the issue of class imbalance. Findings indicate that random splits are optimal but on a patient basis an evaluation is more realistic as area under the ROC curve (AUC) values are at a range of 0.9276 to 0.9726 at different magnifications. A further magnification generalized model is expected to reach accuracy of 92.75% and AUC of 0.9708 with high sensitivity. The results prove that ultrasound trained representations can be successfully implemented on histopathology even when the domain differ, as well as the need to perform patient-specific validation in medical image processing. The research justifies cross-modality transfer learning as a data-efficient approach to breast cancer.