Abstract: This research investigates the magnetohydrodynamic (MHD) flow and thermal transport characteristics of a silver–graphene oxide (Ag–GO) composite nanofluid within a rotating arrangement formed by a pair of parallel plates. The working fluid consists of a 50:50 water–ethylene glycol combination. The mathematical formulation includes heat radiation, Joule heating, and Darcy resistance to represent porous medium influences, while both the upper and lower plates are assumed to be permeable and stretchable, respectively. Appropriate similarity transformations transform the governing coupled nonlinear ordinary differential equations into dimensionless form, and the resulting equations are solved numerically using the MATLAB bvp4c technique. The effects of several important parameters, including Darcy number, magnetic parameter, rotation parameter, suction/blowing parameter, radiation parameter, Reynolds number, and nanoparticle volume fraction, are examined in detail. The findings reveal that higher Darcy resistance reduces fluid velocity while increasing temperature distribution. Furthermore, the incorporation of Ag–GO nanoparticles considerably enhances thermal performance. The Nusselt number rises with greater nanoparticle concentration and fluid injection, whereas it declines as porous resistance increases.

Keywords: Hybrid Nanofluid (HNF), Heat Transfer, Rotating System, Magnetohydrodynamics (MHD), Thermal Conductivity, Joule Heating.

Title: Heat Exchange Phenomena of Ag–GO Hybrid Nanofluid in a Rotating Porous Medium under MHD, Thermal Radiation, and Joule Heating

Author: C Sulochana, Nityanand

International Journal of Novel Research in Engineering and Science

ISSN 2394-7349

Vol. 13, Issue 1, March 2026 - August 2026

Page No: 24-37

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 02-June-2026

Abstract: The purpose of this study is to investigate the corrosion behavior of mild steel and carbon steel in soil environment through experimental exposure and comparative analysis. Weight loss measurements, corrosion rate calculations, and surface morphology observations were conducted over a 360day period. Corrosion determining parameters such as pH, electrical conductivity, chloride, sulphate, resistivity, moisture content, and Total Heterotrophic Bacteria (THB) were analyzed for the soil sample collected prior to the burial of the steel coupons. Soil resistivity at the location was taken using Vertical Electrical Sounding (VES). The results of the soil sample analyzed showed that the soil was slightly acidic with a pH of 6.4. The electrical conductivity (EC) of the soil was 630 μS/cm, chloride 96 mg/l, sulphate 20 mg/l, resistivity of 65 Ω.m, moisture content 45%, and THB of 2.5×104 CFU/ml. Some of the analyzed soil parameter values are within the range of environmental factors that significantly influence corrosion behavior of buried metals. The results of the experiment showed that the soil has a moderate corrosive risk and that both mild steel and carbon steel are susceptible to corrosion in the soil environment, but the degree and form of corrosion differ. Hence, mild steel experienced higher corrosion rates (uniform corrosion) than carbon steel which exhibited localized pitting and crevice corrosion.

Keywords: Corrosion, Mild Steel, Carbon Steel, Soil Environment, Niger Delta.

Title: Comparative Analysis of Mild and Carbon Steel Corrosion in Soil Environment in Niger Delta – Nigeria

Author: Amadi, T.N., Ukoha-Onuoha, E.

International Journal of Novel Research in Engineering and Science

ISSN 2394-7349

Vol. 13, Issue 1, March 2026 - August 2026

Page No: 17-23

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 25-May-2026

Abstract: Underwater object detection and classification are essential for marine exploration, environmental monitoring, and autonomous underwater systems. However, complex underwater conditions such as poor visibility, color distortion, and frequent object overlap significantly degrade the performance of conventional detection methods. In particular, overlapping and occluded objects pose serious challenges to accurate localization and classification, necessitating more robust vision-based solutions. To address this problem, this work proposes an occlusion-resilient instance segmentation framework that integrates deep learning models for effective separation and identification of overlapping underwater objects. The solution combines spatial feature extraction and contextual learning to enhance boundary delineation and class discrimination under partial visibility conditions. The methodology employs the Underwater Object Detection Dataset, consisting of 10,000 annotated images across fish, coral, and debris categories. Image enhancement, normalization, augmentation, and refined instance annotations are applied, followed by CNN, RNN, and hybrid CNN–RNN modeling for segmentation and classification. Experimental results demonstrate that the proposed hybrid model achieves 96% accuracy, 94% precision, 93% recall, and an F1-score of 94%, outperforming standalone CNN and RNN architectures in handling overlapping and occluded underwater objects.

Keywords: Underwater object detection, instance segmentation, occlusion handling, deep learning, CNN–RNN hybrid model, marine image analysis.

Title: Occlusion-Resilient Instance Segmentation for Overlapping Underwater Object Detection and Classification

Author: Anuspa Sahani, Dr. Anupama Sahu, Dr. Sasmita Mishra

International Journal of Novel Research in Engineering and Science

ISSN 2394-7349

Vol. 13, Issue 1, March 2026 - August 2026

Page No: 1-16

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 04-April-2026