Chaitanya Bharathi Institute of Technology

Equatorial plasma bubbles (EPBs) are ionospheric irregularities that degrade Global Navigation Satellite System (GNSS) signal quality through rapid total electron content (TEC) fluctuations and scintillation. This study presents a novel approach for EPB detection with Doppler Index and classification using an Xtreme Gradient Boosting (XGBoost) model based on multi-feature GNSS parameters. Doppler index (DI), vertical TEC (VTEC), elevation angle, timestamp and Ionospheric Pierce Point (IPP) were extracted from GNSS observations at the low-latitude IGS GNSS station HYDE during 12 geomagnetically active days spread across 2022, 2023 and 2025. Class imbalance was addressed using SMOTE and SMOTE-Edited Nearest Neighbors (SMOTEENN). To test the robustness, EPB events over the years 2015, 2023, 2024 and 2025 were considered, excluding the days from the train dataset. Results indicate that XGBoost with SMOTEENN has an accuracy of 96.67

Integrating cutting-edge technology with conventional farming practices has been dubbed “smart agriculture” or “the agricultural internet of things.” Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks.

In this work, D2205 duplex stainless steel has been laser shock peened without an ablative layer. The effect of shock peening on the microstructure, tensile strength, wettability, protein adsorption and biocompatibility has been studied. Due to thermal effect of the high-energy pulsed laser used for peening, lattice micro-strain and dislocation density decreased, and lattice parameters and grain size increased in the vicinity of the shock peened surface. However, beneath the heat affected area, the nature of residual stress changed from compressive in ferrite and tensile in austenite to compressive for both. Within 1 mm distance from the shock peened surface, induced residual compressive stress and grain refinement led to increase in nano-hardness from similar to 3.8 GPa to similar to 4.3 GPa. In addition, the yield and ultimate tensile strengths increased to 709 MPa and 845 MPa, respectively on the shock peened surface compared to 611 MPa and 750 MPa, respectively on the unpeened side. Laser shock peening led to decrease in surface energy and increase in hydrophobicity, indicated by increase in contact angle in the sessile drop test. Protein adsorption got decreased on the shock peened surface due to increase in hydrophobicity. Cell proliferation and decrease in secretion of pro-inflammatory cytokines indicated better biocompatibility.

The knowledge on thermodynamic irreversibility becomes indispensable in order to choose best design parameters of any thermal system. The main aim of this work is to assess thermal and hydrodynamic performance, and entropy generation analysis of a heat exchanger partially filled with aluminium metal foam. For the investigation, three different porous layer thicknesses (t) of 40, 60 and 80 mm varied from the wall side of the tube are considered. Four different pore densities of 10, 20, 30 and 45 PPI foam samples and their porosities ranging 0.90-0.95 are employed for the examination. The air flow Reynolds (Re) number is varied from 4500 to 20500. Local thermal non-equilibrium (LTNE) and Darcy-extended Forchheimer (DEF) flow models are employed in porous filled region of the heat exchanger. In the clear (non-metal foam) region of the heat exchanger, two-equations standard k-omega turbulence model is employed. Heat transfer enhancement ratio, performance evaluation criteria, 2nd law efficiency (eta 2nd) are discussed with respect to PPI and thickness of the foam sample. Additionally, total irreversibility associated with heat transfer and fluid friction increases with increasing flow Re number and found to be minimum for lower values of pore density of 30 PPI foam sample with porous layer thickness, t = 40 mm. The friction factor ratio (FFR) reduces marginally with increasing flow Re number and increases with increasing porous layer thicknesses of porous foams. Moreover, Bejan number (Be) reduces with increasing flow Re number and PPI's of metal foams. Further, for higher PPI with maximum porous layer thickness, the maximum entropy generation number (Ns) suggests that the process is more irreversible and as a result, a greater amount of energy becomes unavailable.

The changing needs of the modern agriculture require smart and resource saving solutions to such problems as falling productivity, irresponsible use of inputs, and deterioration of the environment. This paper presents a hybrid framework AgriCLWO-Net, which consists of lightweight Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) model along with Whale Optimization Algorithm (WOA) to provide precision agriculture services by sensors integrated Internet of Things (IoT) environments. The suggested model will categorize the health status of crops, optimize irrigation and spreading of fertilizers, and enhance sustainability performance based on spatiotemporal field information. The methodology takes advantage of CNN to perform spatial patterns area recognition based on multisensory stimuli, LSTM to perform temporal relationships in crop and atmospheric patterns, and WOA to tune the hyperparameters and adaptive decision-making. The model was tested against a sample dataset of the Indian agricultural areas including the temperature, soil moisture, humidity, and nutrient measurements. Findings show that the classification accuracy (98.54