Arunachala College Of Engineering For Women

Battery vitality or the energy in the battery is said to be an uncommon asset in Mobile Ad-hoc network (MANET). This frequently influences the correspondence exercises in that are practiced in the network. The basic issue faced by MANET is the vitality proficiency. The simulation or test is the way to expand the restricted lifetime of energy that is there in the mobile nodes (MN). Vitality the board model is initially presented in this manuscript, in which every hub can move its state between dynamic mode power-spare modes. A novel routing Protocol (RP) has been proposed for additional vitality/energy control. In the convention, another routing capacity that manages both system layer as well as the Media Access Control (MAC) system layer has been characterized. Also, to deal with limiting the utilization of the energy, Advanced Energy Efficiency in MANET by improving Optimized Link State Routing Protocol version 2 (OLSRv2): (AEE-M-OLSRv2). AEE-M-OLSRv2 depends on the OLSRv2-RP and includes another vitality reasonableness boundary to the Multi-Point Relay (MPR) method. The new boundary is utilized by the proposed methodology and permits reasonableness vitality utilization in the equivalent set of Multi-point relay. Hubs with low force are forestalled in the network for routing procedure so as to keep up comparable force esteems for all the portable hubs. The results indicate that the proposed AEE-M-OLSRv2 approach out performs around 25% that the existing.

Chronic Kidney Disease (CKD) is a global silent epidemic affecting 10% of the population, with its asymptomatic nature and fragmented medical records often delaying diagnosis until irreversible stages. Traditional screening relies on static lab tests, failing to capture dynamic physiological shifts. This study introduces a Federated Multimodal AI Framework to democratize renal diagnostics via a privacy-preserving, decentralized architecture. The motivation is to shift from reactive care to proactive, continuous monitoring in resource-constrained rural areas. By utilizing Federated Learning, the system trains robust models across healthcare nodes without transferring sensitive raw data, ensuring strict privacy compliance. Its core contribution lies in integrating clinical records, wearable sensors, and renal imaging, providing a scalable solution for early detection and progression analysis while overcoming the systemic bottlenecks of diagnostic latency and data siloing. The objective is to fuse diverse data streams including clinical laboratory records, longitudinal vitals from wearable sensors such as heart rate and blood pressure, and structural renal ultrasound imaging—into a single predictive engine. The methodology involves advanced pre-processing steps, such as K-Nearest Neighbour imputation for handling missing clinical values and sliding-window segmentation for temporal vitals. To process this data, a hybrid deep learning architecture is implemented: Convolutional Neural Networks (CNNs) extract structural features from kidney scans to detect physical scarring, while Long Short-Term Memory (LSTM) networks identify temporal patterns in comorbid vitals. An attention-based fusion mechanism then weighs these inputs, and model transparency is ensured through SHAP analysis, which provides clinicians with clear, biomarker-driven justifications for every risk assessment. Experimental evaluation of a new framework across 13,900 records demonstrated superior performance, achieving an F1-score of 0.931 and AUC-ROC of 0.952, with high accuracy (92.7%–96.2%) across disease stages. A 500-patient pilot study in Neyyattinkara showed significant clinical impact, including a 68% reduction in diagnostic costs, a drop in delays from 127 to 12 days, and a 35% dialysis deferral rate. Future scope for this research includes integrating multi-omics and genomic data to enable personalized precision medicine, as well as the creation of Longitudinal Digital Twins to simulate disease trajectories. By deploying these models via Edge AI and implementing Secure Multi-Party Computation, the framework will continue to evolve as a scalable, transparent, and highly secure tool for global renal health. Keywords: Federated Learning, Chronic Kidney Disease, Convolutional Neural Network, Long Short-Term Memory, Patient Health Monitoring and Multimodal Medical Data Analysis.

Azamacrocyclic Schiff bases have a high affinity for coordinating with transition metals, making them valuable for various advanced scientific and technological applications. Despite extensive studies on Schiff base macrocycles from aromatic diamines and dicarbonyls, research on those involving aliphatic carbonyl compounds and triethylenetetramine remains limited; however, its integration into Schiff base macrocycles opens new avenues for material and biological applications. In this article, a novel 15-membered tetraazamacrocyclic Schiff base, (1E, 10E)1,4,7,10-tetraazacyclopentadeca-10,15-diene (GTETA) was synthesized through microwave-assisted condensation of pentane-1,5-dial and triethylenetetramine. The structural and electronic properties of GTETA were analyzed using Density Functional Theory (DFT/B3LYP) calculations, providing insights into its molecular geometry, vibrational and NMR properties. Global reactivity descriptors, molecular electrostatic potential (MEP) mapping, FMO and NBO analysis were employed to understand its chemical behavior. Additionally, first-order hyperpolarizability calculations and second harmonic generation (SHG) measurements confirmed its nonlinear optical activity. Molecular docking revealed that GTETA binds effectively to the target protein 6GGD via hydrogen-bond interactions, and in vitro biological assays further supported its biological potential.

Electromagnetic interference (EMI) is one of the main issues in brushless direct current (BLDC) motors, which harms efficiency, reliability and international EMI regulations. Traditional methods of suppression are not always flexible and deteriorate performance, which requires solutions of high quality. To solve this, a new system, the enhanced EMI resilience optimization system (EEROS), is proposed, which combines three new methods: active common mode swarm-enhanced EMI mitigation system (ACS-EMS), adaptive power management EMI control system (APM-ECS) and adaptive spread spectrum-filtered genetic EMI suppression (ASSF-GES). These techniques use spread spectrum modulation (SSM), active EMI filters (AEFs), genetic algorithms (GAs), particle swarm optimization (PSO) and dynamic frequency modulation (DFM) to create dynamic suppression of high-frequency EMI and motor efficiency of 98%, thermal stability of 59 degrees C and responsiveness of 98%. The paper also analyzes how the variations of control parameters affect the EMI suppression and performance trade-offs and proves the existence of a strong and high-efficiency mitigation framework. The proposed system offers a scalable solution to automotive, industrial and aerospace applications and has better EMI resilience, regulatory compliance and operational reliability.