This study investigates the structural, thermal, and electrical properties of polyvinyl alcohol (PVA) nanofibers doped with a ZnO–CoO binary oxide, synthesized via a controlled electrospinning process at an applied voltage of 16 kV and a flow rate of 0.8 mL/h. FESEM analysis revealed a systematic morphological transition from smooth, uniform fibers to a bead-on-string structure as the dopant concentration increased from 1 wt% to 5 wt%. Moreover, quantitative analysis revealed fine fiber morphologies, with the minimum diameters reaching 17 nm, 23 nm, and 26 nm of different concentration ratios, respectively. X-ray diffraction (XRD) analysis confirmed the amorphous nature of both the pristine and doped nanofibers. The absence of metallic crystalline peaks suggests that the dopants were uniformly distributed throughout the polymer matrix, which is consistent with established literature. Fourier Transform Infrared (FTIR) spectra demonstrated an increasing shift in the vibrations of the hydroxyl (–OH) functional groups, signifying strengthened intermolecular interactions. Thermal analysis via TGA/DSC indicated that while low-level doping (1 wt%) enhances thermal stability, higher dopant concentrations result in a reduction of the characteristic degradation temperatures. Electrical characterization revealed that the conductivity follows an Arrhenius-type behavior, in which the activation energy ( ) was significantly reduced with increasing dopant concentration. The lowest value of 0.031 eV was attained for the composite containing 4 wt% of the ZnO–CoO hybrid. These results suggest that the charge transport mechanism is thermally activated and that the incorporation of metal oxide dopants effectively lowers the energy barrier for carrier hopping. Consequently, the synthesized hybrid nanofibers demonstrate a high degree of tailoring capability for tunable electronic applications
The spectroscopic and plasma characteristics of Moringa oleifera were examined by laser-induced breakdown spectroscopy (LIBS) and optical emission spectroscopy (OES) at laser energies between 50 and 450 mJ. On compressed Moringa oleifera powder discs, laser-induced plasma was produced and was analyzed to determine elemental composition as well as plasma parameters. There were distinct emission lines of K, Ca, Na, and O at higher intensities with laser power. Based on Boltzmann plot analysis, we observed a change from a value of 1.298 electron temperature (Te) to 1.352 eV which was an increase in electron temperature (Te) and reflected the improved efficiency of excitation as well as plasma stability. The majority of the analysis was validated through Lorentzian fitting and Stark broadening thus, thus, allowing for quantitative evaluation electron density. The findings validate local thermal equilibrium condition and exhibit the suitability of LIBS/ OES to plant-based material characterization and green nanotechnology usage
Pseudomonas aeruginosa uses quorum-sensing systems to coordinate activities regulated by specific genes, such as metabolic and environmental responses. The pqsA gene is a key component of the pqs pathway (quinolone signaling in Pseudomonas aeruginosa).This study focused on applying a mouse model to some local isolates of P. aeruginosa and identifying those carrying the pqsA gene. The study included isolating 150 samples for analysis, which resulted in confirmation of the presence of 58 isolates of P.aeruginosa. The specimens including wound swabs, burn samples, urine samples, middle ear swabs, and sputum were collected from different hospitals. The presence of the pqsA gene was confirmed molecularly using polymerase chain reaction (PCR) on 19 selected isolates, all of which showed positive amplification (448 bp). The experimental mouse model consisted of Swiss albino mice, divided into a control group and two treatment groups with low (1×10⁴ CFU) and high (1×10⁸ CFU) concentration of bacteria. We evaluated body weight before and after exposure and also performed histological examinations of the liver and lung tissue. The results showed that increased bacterial concentrations led to increased weight loss and extensive histological changes. In contrast, low concentrations produced only minor tissue changes. Statistical analysis (results are presented as means ± SE calculated using one-way ANOVA) confirmed these findings as statistically significant (p ≤ 0.05). Based on the above information, it can be concluded that the pqsA gene is highly conserved in local isolates of P. aeruginosa and that the presence of this gene leads to significant effects in host tissue in controlled in vivo studies. Therefore, this research contributes to understanding of the bacterial quorum sensing mechanism, the pathogenicity of bacterial species and their ability to interact with host bacteria in in vivo studies
Web applications are an integral part of digital infrastructure in the modern era and have become increasingly susceptible to advanced cyber threats, thus needing more intelligent and adaptable intrusion detection system. Traditional signature and rule-based (IDS) systems have inherent limitations in detecting various types of web attacks because they are static in nature. This paper presents a multi-class web intrusion detection framework based on deep learning methods by using the WEB-IDS23 dataset, a realistic representation of modern web traffic environments and presents the first holistic multi-class evaluation on this dataset by employing an integrated deep learning and stacking-based ensemble model. The model utilizes Random Forest-based feature selection and combines it with three deep learning models (One-Dimensional Convolutional Neural Network (1D-CNN), Deep Neural Network (DNN) and Gated Recurrent Unit (GRU-RNN)). we then used a stacking-based meta-learning approach to aggregate the predictions. Strong detection performance is demonstrated in experiments. The 1D-CNN yielded best standalone accuracy of 96.43% and F1-score of 96.32%, while stacking ensemble further boosting the classification accuracy to 96.55% and precision to 97.15%. Although the stacking meta-model achieved a marginally lower F1-score compared to its standalone 1D-CNN, it performed with higher overall accuracy. This phenomenon is due to the imbalanced class distribution of the WEB-IDS23 dataset, where optimizing majority-class classification raised overall accuracy but lowered recall and precision on some minority classes. By combining these methods with feature selection, deep learning and stacking-based ensemble learning, the final result achieves higher accuracy in detection task as well as robust detection capability and scalability. The proposed system is an efficient approach for the multi-class web intrusion detection system
In the present study, we propose a new model, identified as the Shanker Rayleigh distribution, which is derived by combining the survival functions of the classical Rayleigh and Shanker distributions, both of which have a scale parameter only. We developed this hybrid model to offer the additional modeling flexibility required for complex lifespans that single-unit distributions cannot accurately represent. Theoretical characteristics of the distribution (probability density function, cumulative distribution function, survival, and hazard rate function) are derived in closed form. In addition, important distributional properties, namely the raw and central moments, moment-generating function, and quantile function, are methodically examined and developed from two statistical methods, maximum likelihood estimation (MLE) and ordinary least squares (OLS), for estimating the parameter of the proposed model, and their efficiency was assessed by applying them to real data to demonstrate the new models’ ability to better apply the data. In addition, this proposal was applied for scientific purposes through the analysis of real data
