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

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.

The escalating crisis of antimicrobial resistance claims nearly 5 million lives annually. Resistant infections now account for 4.95 million deaths worldwide and economic losses projected to reach $300 billion by 2030. Despite this urgent threat, traditional antibiotic discovery has declined precipitously. New chemical entity approvals have fallen by over 50%, while existing therapeutics are rapidly rendered obsolete by sophisticated bacterial resistance mechanisms including extended-spectrum β-lactamases, carbapenemases, and multidrug efflux pumps. Bio-based products have historically provided humanity’s most transformative antibiotics, yet conventional discovery pipelines face insurmountable bottlenecks. A total of 99.9% of environmental microbes remain unculturable. Biosynthetic gene clusters are predominantly silent under laboratory conditions, and dereplication efforts achieve only 2 to 5% annotation rates. This review presents a comprehensive examination of how artificial intelligence (AI) is revolutionizing bio-based product-based antibacterial discovery. We analyze AI-driven genome mining tools that have identified over 170,000 biosynthetic gene clusters across bacterial genomes, deep learning architectures achieving 88.5% bioactivity prediction accuracy, and generative models delivering experimental hit rates exceeding 50%—representing 50- to 90-fold improvements over traditional screening. Through validated case studies spanning in silico prediction to in vivo efficacy, we demonstrate that AI integration is not merely accelerating discovery but fundamentally transforming our capacity to access nature’s previously inaccessible chemical diversity in the fight against antimicrobial resistance.