St. Martin's Engineering College

The tremendous potential of organic–inorganic hybrid crystals in the next generation of optoelectronic and photonic technologies has led to a steady increase in their investigation recently. The semi-organic nonlinear optical single crystals of L-argininium perrhenate (LAPR) were grown with bulk size by employing the slow evaporation solution growth technique (SEST), and the crystal’s structural and physical properties were analyzed. The orthorhombic crystal structure of LAPR was revealed by the single-crystal XRD (SCXRD) method. The NMR spectral analysis was carried out for the title sample. The grown specimen of LAPR has been subjected to FTIR analysis to confirm the existing functional groups. The outcome of UV–Vis–NIR analysis reveals that the crystal has 80

Machine learning (ML) has played a pivotal role in transforming genomic data to personalized medicine, allowing for accurate diagnosis, specific treatment options, and predictive modeling of disease susceptibility. Furthermore, herein review the variety of ML techniques, including deep learning, support vector machines, and ensemble learning, that have been employed to analyze complicated genomic data in a manner that reveals clinically useful clues. Also cover challenges related to genomic big data such as high dimensionality, noise, and data heterogeneity and examine how ML-based strategies overcome these challenges via feature selection, dimensionality reduction and model interpretability techniques. Also emphasize its practical applications for cancer genomics, rare disease diagnosis, and pharmacogenomics wherein ML can improve decision-making, personalize treatment, and optimize drug design. The paper covers ethical considerations and data privacy issues in genomic/ML applications. By providing an in-depth discussion of the most recent cutting-edge methods in clinical and organ-based decision-making, this study emphasizes the vital role of ML in facilitating precision medicine and tailored treatment methods.

Samarium-substituted cobalt nanoferrites (CoSmₓFe₂₋ₓO₄, x = 0.0 to 0.10) were synthesized via the citrate-gel auto-combustion method and calcined at 500 °C for 4 h. XRD confirmed a single-phase cubic spinel structure, with lattice parameters increasing with Sm content from 8.419 ± 0.002 Å to 8.458 ± 0.002 Å, following Vegard’s law. SEM and EDS revealed uniform, minimally porous microstructures with elemental homogeneity. FTIR spectroscopy confirmed preferential Sm³⁺ occupancy at octahedral B-sites through systematic redshift of the ν₂ band (364 → 352 cm⁻¹). The material exhibited semiconducting behavior, with optimal properties at x = 0.05–0.07, enhancing electrical conductivity to (1.2 ± 0.1) × 10⁻³ S/cm at 500 K (a tenfold increase), improving dielectric relaxation with impedance as low as (8.0 ± 0.4) × 10² Ω at 500 °C, and tailoring magnetic performance with Curie temperature tunable between 663–683 K ( ± 5 K). Piecewise linear regression revealed statistically significant slope changes at x ≈ 0.06 for conductivity (p = 0.02) and activation energy (p = 0.01), indicating a mechanistic transition in charge transport. These findings, driven by Sm³⁺-induced lattice strain and cation redistribution, advance nanoferrites for high-frequency devices and energy storage. The citrate-gel method’s energy efficiency highlights its sustainability for scalable synthesis.

The increasing penetration of renewable energy sources and converter-interfaced loads has intensified the need for fast and reliable grid-support services. Although electric vehicle (EV) battery chargers have emerged as promising resources for Vehicle-to-Grid (V2G) applications, existing solutions typically focus on individual services such as virtual inertia or frequency regulation, while limited attention has been given to the coordinated provision of multiple ancillary services within a unified framework. Furthermore, the use of batteries alone for fast frequency support may accelerate battery degradation due to frequent high-power transients. To address these challenges, this paper proposes a hybrid energy storage-based EV battery charger architecture and a coordinated multi-timescale control strategy capable of simultaneously providing virtual inertia support, long-term frequency regulation, reactive power compensation, and harmonic mitigation. The proposed approach utilizes a DC-link capacitor to deliver fast inertial response while the battery supplies sustained frequency support, thereby reducing battery stress and improving energy management efficiency. An enhanced frequency estimation method based on a phase-locked loop combined with a low-pass filter is also introduced to improve dynamic performance. Simulation results demonstrate the effectiveness of the proposed strategy under various grid disturbances. The system achieves an equivalent virtual inertia constant of approximately 1.85 s and delivers up to 786 W of transient inertial support within 80 ms during frequency events. The enhanced frequency estimation method significantly reduces transient overshoot, while harmonic compensation limits the grid current and voltage total harmonic distortion to 1.50% and 3.23%, respectively. In addition, the controller provides up to 400 VAR of reactive power support during voltage disturbances while maintaining stable battery operation. These results demonstrate that the proposed EV battery charger can function as a multifunctional grid-support resource, enhancing frequency stability, voltage regulation, power quality, and overall V2G capability in future smart grids.

Human Action Recognition (HAR) systems deployed in real-time surveillance, edge computing, and bandwidthconstrained environments require efficient video compression without sacrificing recognition accuracy. Conventional compression schemes such as Set Partitioning in Hierarchical Trees (SPIHT) are optimized for pixellevel fidelity metrics like PSNR and SSIM, which do not necessarily preserve motion dynamics, spatio-temporal edges, or skeletal structures critical for action recognition. This paper proposes a Task-Aware Progressive SPIHT (TA-PSPIHT) framework that bridges video compression and action recognition by aligning encoding priorities with task relevance rather than visual reconstruction quality alone. The proposed method integrates lightweight pose estimation and optical-flow magnitude maps to generate an importance mask that identifies motion- and skeleton-dominant regions. This mask is incorporated into the SPIHT set-partitioning mechanism through Weighted Significance Testing, enabling action-relevant wavelet coefficients to be encoded earlier in the progressive bitstream. Furthermore, a 3D Temporal-Priority SPIHT structure exploits spatio-temporal dependencies across frames, while a Policy-Gradient–based Bit-Dropping strategy optimizes rate– recognition trade-offs. Experimental results demonstrate that the proposed framework significantly improves action recognition accuracy at low bitrates compared to conventional SPIHT, while maintaining computational efficiency and progressive transmission capability. The proposed approach provides a practical and scalable solution for task-driven video analytics in resource-constrained environments.