Hydroxyapatite (HAP) is a significant bone mineral that establishes bone strength. HAP composites in combination with biodegradable and bioactive polymer poly xylitol sebacic adipate (PXSA) would result in a constant release at target sites. Numerous studies have shown that vitamin K (VK) might possess a vital function in bone metabolism. The purpose of the present study was to inspect the synthesized composite HAP/PXSA/VK in developing polymeric biomaterials composite for the application of bone tissue regeneration. FTIR, X-ray diffraction, SEM and TEM techniques were applied to characterize the prepared composites. The release of VK from the HAP/PXSA/VK composite was evidenced through UV-Vis spectroscopy. In vitro studies proved that the HAP/PXSA/VK composite is appropriate for mesenchymal stem cell culture. Compared to pure HAP prepared following the same method, HAP/PXSA/VK composite provided favourable microstructures and good biodegradation distinctiveness for the application of tissue engineering, as well as tissue in-growth characteristics and improved scaffold cell penetration. This work reveals that the HAP/PXSA/VK composites have the potential for applications in bone tissue engineering.
In an era marked by pervasive digital connectivity, cybersecurity concerns have escalated. The rapid evolution of technology has led to a spectrum of cyber threats, including sophisticated zero-day attacks. This research addresses the challenge of existing intrusion detection systems in identifying zero-day attacks using the CIC-MalMem-2022 dataset and autoencoders for anomaly detection. The trained autoencoder is integrated with XGBoost and Random Forest, resulting in the models XGBoost-AE and Random Forest-AE. The study demonstrates that incorporating an anomaly detector into traditional models significantly enhances performance. The Random Forest-AE model achieved 100% accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC), outperforming the methods proposed by Balasubramanian et al., Khan, Mezina et al., Smith et al., and Dener et al. When tested on unseen data, the Random Forest-AE model achieved an accuracy of 99.9892%, precision of 100%, recall of 99.9803%, F1 score of 99.9901%, and MCC of 99.8313%. This research highlights the effectiveness of the proposed model in maintaining high accuracy even with previously unseen data.
Covalent triazine frameworks (CTFs) have attracted tremendous attention with respect to their rich nitrogen content, functional triazine units, and high porosity. However, efficient and simple preparation of CTF monoliths is still a challenge. Here, we propose a novel and facile approach for the in situ preparation of CTF aerogels. Trifluoromethanesulfonic acid was used as the solvent and catalyst, which could not only promote the polymerization reaction to form a gel but also protonate the CTFs. By virtue of the simplicity and efficiency of the strategy, a series of macroscopic CTF aerogels with tunable density were obtained by adjusting the monomer concentration. Due to the porous and partially crystalline structure, the as-produced macroscopic CTF aerogels behaved with good mechanical and thermal insulation performances. This finding thus offers an effective and easily scalable approach toward fabricating macroscopic CTF aerogels and broadens the applications of CTFs in a variety of domains.