Owing to its remarkable features, lithium niobate (LiNbO3) has been deployed in wide range of applications. In this study, the structural, electronic, and optical properties of the pristine LiNbO3 and Ru substituted B-site of LiNbO3 were investigated by employing density functional theory approach (DFT) within CASTEP code based on (GGA-PBE). Moreover, different hydrostatic pressures (0, 5, 10, 15, and 20 GPa) were applied. The results revealed that the optimized lattice parameters and energy bandgap are in well agreement with the published results. Besides, the insertion of Ru atoms at Nb site of LiNbO3 leads to a decrease in the crystal lattice constants. This reduction becomes more increased in both pure and doped structures with rising pressure. At 0 GPa, the calculated bandgap is significantly narrowed from 3.35 eV, 0.73 eV and 0 eV for LiNbO3, LiNb0.5Ru0.5O3, and LiRuO3, respectively. However, increasing pressure to 20 GPa shows a minor increase in the bandgap width of the host structure, while it is decreased in the doped compounds. The absorption spectrum at zero pressure for the host system reveals an improved in the UV region with slight absorption in the visible part. Ru substitution can effectively narrow the bandgap and increase visible-light absorption in the visible region of electromagnetic radiation. At high pressures, the absorption coefficient shifts toward the UV range, while it reduced in the visible range of Ru: LiNbO3. The current results suggest Ru-alloyed LiNbO3 as novel lead-free compounds for next-generation applications.
With the ever-increasing demand for secure transmission and storage of medical records in a healthcare system, quick and effective data protection techniques are required. In this paper, a new approach of using Speck-Cipher, Discrete Wavelet Transform (DWT), and Quantum-based Particle Swarm Optimization (QPSO) is proposed for the security of embedded medical data in medical images. The technique can also maintain good image quality and offers Speck-96bit to safeguard sensitive medical data with high noise & compression attack resistance (CR = 0.95). The results showed that the proposed method performs better, with a Peak Signal-to-Noise Ratio (PSNR) of 46.0 dB (Max = 62.5) and a Structural Similarity Index Measure (SSIM) of 0.98, revealing minimal distortion between itself and the original cover image. In addition, it provides excellent compression robustness (0.94), which ensures that the quality of the hidden data remains intact even under aggressive compression. Encryption time 12.25 ± 1.8ms avg (10 runs/image) shows the efficiency of the Speck cipher and shows that the results are applicable to real-time healthcare in an IoT environment. Furthermore, the method shows an NCC of Normalized Cross-Correlation) near 1, meaning data can be retrieved accurately; high-quality post-compression retention (0.95) that guarantees hidden data integrity with aggressive compression; and a computational efficiency of 90 img/s (Max = 95) that is suitable for real-time IoMT (90 img/s on Raspberry Pi 4). The results obtained prove that the proposed technique is computationally efficient and enhances data confidentiality, respectively, for real-world healthcare systems.
Large-scale networked systems now face security threats that shift faster than fixed schedulers can follow. Conventional scheduling optimizes throughput and latency, and treats security as an afterthought. This paper takes a different approach: a reinforcement learning framework for risk-aware scheduling, with its risk signal drawn from multi-modal analysis of UNSW-NB15 traffic – statistical profiling, DBSCAN and Gaussian mixture clustering, and sequential pattern mining. The pipeline runs in two phases. First, preprocessing and feature engineering turn raw network data into usable scheduling features. Second, DQN and PPO agents learn scheduling policies under a dynamic, risk-weighted reward. The balance between security and performance shifts with the current mined risk level, not a fixed rule. The risk signal compares live traffic against mined high- risk patterns, restricted to the feature dimensions those patterns actually weighted by each correlation dimension with the real attack label. Across five seeds on real UNSW-NB15 data, both schedulers significantly cut security violations against Random, Round Robin, and Greedy baselines (p < 0.001) and earn significantly higher reward. PPO reaches the lowest violation, cutting violations by roughly three-quarters relative to the baselines. DQN keeps utilization closest to the baselines while still improving reward. The tradeoff is response time weighing risk before every placement costs speed.
The healing of wounds is one of the complex biological processes, which involves appropriate coordination of cellular and molecular reactions and the production of innovative biomaterials turned out to be the prerequisite to enhance the efficiency of the healing process. The objective of this study was to prepare and evaluate a Vitamin A-loaded chitosan/polyvinyl alcohol (CS/PVA) biodegradable composite film for potential wound-healing applications. The composite films were fabricated by means of the 98: 2 CS/PVA mixture with various concentration of vitamin. A physicochemical characterization was done using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA) Immunohistochemical mouse wound evaluation of Ki-67 expression and statistical analysis were used to evaluate the performance of the biological aspect in a mouse wound model. It was revealed that incorporation of vitamin A increased the intermolecular interactions without affecting chemical structure of the composite. Thermal testing showed that it became more stable and had lesser degradation, mechanical testing showed that the higher the vitamin A concentration the higher the stiffness and tensile strength. Biologically, it was found that the group that was treated to chitosan/vitamin A had increased epithelial proliferation, decreased inflammation, and faster wound healing. There was a high early increase and a controlled decrease of Ki-67 expression and this demonstrates an effective transition between the proliferative and the remodeling stage. To sum up, vitamin A-impregnated CS/PVA composite had better Structural and Thermal Characterization, which proved its usefulness and potential to be a success and promising biomaterial in wound healing applications.
This study aims to quantify the activity concentrations of 226Ra and 228Ra in Technologically Enhanced Naturally Occurring Radioactive Material (TENORM) contaminated soils from selected sites within the South Rumaila oilfield, Basra Governorate, southern Iraq, and to evaluate physical and chemical remediation options to reduce contamination below IAEA limits. Using High-Purity Germanium (HPGe) gamma-ray spectrometry, the mean activity concentrations were 7.67× 104; ± 1.5% and 6.26× 103 ± 1.43% Bq/kg for 226Ra and 228Ra, respectively (n = 25). Physical separation by particle size and sequential chemical leaching were applied as remediation approaches. The highest leaching efficiencies (78.4% and 80.6% for 226Ra and 228Ra, respectively) were achieved using acetic acid (2M CH3COOH) at a liquid-to-solid ratio of 5:1 mL/g, 40°C, and 2 h contact time. The resulting radioactive leachate was subsequently treated with a MnO2-modified Na2SO4@zeolite-NaA composite adsorbent (mass ratio 1:0.5 g/g, pH 6.3), achieving maximum removal efficiencies of 76.2% and 78.9% for 226Ra and 228Ra, respectively.
