P.E.S. Institute of Technology and Management is an engineering and management college located in Shivamogga, Karnataka, India. It is affiliated to the Visvesvaraya Technological University, Belgaum.
In this study Fe-substituted ZnO (Fe/ZnO) nanoparticles (NPs) are synthesized and utilized as an enhanced photocatalyst for the degradation of Methylene Blue (MB) and the inactivation of bacterial strains such as S.aureus and E.coli. The Fe/ZnO NPs were successfully synthesized using a sol-gel method assisted by combustion. Characterizations were performed using a range of analytical instruments, including Fourier Transform Infrared Spectroscopy (FTIR), X-ray Photoelectron Spectroscopy (XPS), Transmission Electron Microscopy (TEM), Scanning Electron Microscopy with Energy Dispersive X-ray analysis (SEM-EDX), Brunauer–Emmett–Teller (BET) surface area analysis, Thermogravimetric Analysis (TGA), Photoluminescence (PL), and X-ray Diffraction (XRD). Compared to pure ZnO (24 nm) and other dopants, the resulting Fe(0.5 %)-ZnOnanohybrid exhibited a smaller particle size (12 nm). The degradation efficiency of the synthesized nanohybrids was evaluated by monitoring the degradation of Methylene Blue (MB) in an aqueous solution under visible light exposure. The photocatalytic efficiency of the synthesized Fe/ZnOnanohybrid was significantly higher within 120 min under consistent conditions, including pH:10, catalyst loading (50 mg), and MB dye concentration (10 ppm). Additionally, the antibacterial effectiveness of the samples was evaluated against two pathogenic strains (S.aureus and E.coli) using the agar diffusion method. At a concentration of 800 µg/mL, the Fe/ZnOnanohybrid demonstrated the highest inhibition (1.2 mm) of S. aureus growth, indicating that Fe doping enhances ZnO's antibacterial properties. The enhanced photocatalytic and antibacterial activities are attributed to the reduced band gap, increased visible light absorption, and improved charge separation caused by Fe doping, which leads to a greater generation of reactive oxygen species.
Bay habitats, which are economically and ecologically important, are more vulnerable to nutrients, heavy metals, and microbial diversity than other marine ecosystems. This research aims to investigate intertidal sediment distribution and microbial diversity. This study evaluated sediment particle size, water content, loss on ignition (LOI), acid volatile sulfide (AVS), total organic carbon (TOC), heavy metals, and microbial diversity in twelve samples from Asan Bay. The results demonstrate that the relative amount of clay decreased from the inner to outer zone, sand increased, and the island zone had only sand in the sediment. Water content, LOI, TOC, and AVS conc. decrease from inner to middle to outer to island zones. Heavy metals were more abundant in finer sediments than sandy sediments. The order of metal concentrations observed as: Zn > Cr > Li > Pb > Ni > Cu > As > Al > Fe > Cd > Hg. Pseudomonadota predominated, followed by Thermodesulfobacteriota and Bacteroidota. Thermodesulfobacteriota was more abundant in low tidal flat sediments and decreased from inner zone to island zone sediments, whereas Chloroflexota decreased from low to mid and high tidal flats. The principal component analysis and Pearson’s correlation analysis indicates the correlation between sediment texture, TOC, AVS, heavy metals, and microbial diversity. More study on sediment environmental variables and nutrients is needed to understand microbial diversity. This study presents the first data on the diversity of microorganisms in Asan Bay's intertidal sediments. The results will be beneficial in future studies focused on coastal environmental research, as well as in predicting future spatiotemporal variations.
The rapid growth in the field of digital healthcare systems has increased the risk of safeguarding the very important sensitive information including patient health records (PHRs) and scanned medical images. Although the use of conventional digital watermarking (DWM) techniques is very effective way of protecting the information, they also face various challenges that includes vulnerability to attacks, scalability issues, and authenticity problems against numerous image processing techniques. Additionally, the emergence of new technology “quantum computing” introduces a new threat to the security of existing “encryption” and “watermarking” techniques. The new approach “quantum watermarking” (QWM) presents a promising solution with its quantum mechanics principles (superposition, entanglement, and no-cloning theorem), which is the cause for improving security, robustness, and scalability of information. Techniques such as flexible representation of quantum images (FRQI) and novel enhanced quantum representation (NEQR) are used to encode medical information into quantum formats, which will facilitate secure integration of quantum watermarks (WMs). This technique also confirms the information compactness, security, and resistance to tampering, as any unauthorized changes disrupt quantum states, making breaches noticeable. Also, QWM is more beneficial over conventional methods, in terms of its improved security, resilience to image alterations, scalability for larger datasets, and resistance to quantum computing threats. However, issues like quantum hardware implementation limitations and challenges and high costs avoids widespread adoption of this technology. As quantum technology develops, QWM plays a critical role in protecting medical information; this enables secure information exchange over network, which ensures regulatory compliance and enhances healthcare security in the field of quantum era.
The need for real-time and robust monitoring system has become most important with the exponential growth of networked physical and cyber threats. This paper focuses on the design and implementation of an intrusion detection System by using swarm-based intelligent model. This proposed system is capable of detecting the threats in real-time to prompt timely responses by leveraging temporal data analytics. The main objective of this paper is to minimize the potential damages with timely threat identification by developing scalable models so that these models can process and analyze the real-time data. To achieve this objective, we are proposing a multi-layered framework by identifying temporal patterns to improve detection accuracy with low-latency. The proposed approach focuses on the extraction of meaningful features from temporal time series data so that it will help us in enabling dynamic threat identification in multiple domains. From this work, the proposed system for anomaly detection in view of high-speed data, an adaptive threshold mechanism will be considered to reduce the false positives rate by 18%, and a lightweight strategy to ensure capability for low-latency applications. The Swarm-based LSTM achieved accuracy of 98.7 and 96.5% F1 Score with a precision 95.3% demonstrating optimal scalability and efficiency for real-time cybersecurity applications when compared with the vanilla LSTM, GRU, and Bi-LSTM. All these models were evaluated based on the data set KDDcup99.
In this article, we explore Finslerian wormhole solutions within the framework of non-commutative geometry. Two smeared matter distributions (Gaussian and Lorentzian) are considered to investigate the existence of traversable wormholes. The corresponding shape functions are obtained and found to satisfy all necessary geometric conditions. The energy conditions are analyzed graphically, showing that the null energy condition (NEC) is violated near the throat, signifying the presence of exotic matter. However, the Finsler anisotropy, governed by the curvature parameter ( κ ), reduces the degree of this violation, implying that less exotic matter is required. Furthermore, the Tolman–Oppenheimer–Volkoff (TOV) equilibrium is employed to study stability, revealing a precise balance among the gravitational, hydrostatic, and anisotropic forces. This indicates that the obtained Finsler–noncommutative wormhole solutions are physically stable and in equilibrium.
