The high-pressure torsion (HPT) process is one of the most powerful methods of severe plastic deformation, capable of significantly refining the microstructure and altering the functional properties of high-strength aluminum alloys. In this work, the effects of HPT on the microstructure, residual stresses, hardness, and damping characteristics of the AA7075 alloy were comprehensively studied. Microstructural investigation revealed that the average grain size of the starting material was around 95 µm, but after HPT, it decreased to 6.1 µm, indicating continuous dynamic recrystallization. The secondary-phase particles were fragmented and uniformly distributed, as observed by SEM. The lattice strain was evident from the broadening of the XRD peaks. High compressive residual stresses were found near the surface ( − 600 MPa), whereas at greater depths the stresses were tensile due to strain gradients. Microhardness rose about 35–40
Due to the rapid development of communication technology, the utilization of Internet of Things (IoT) has increased rapidly. Wireless Sensor Networks (WSNs) play a crucial role in IoT, which is highly applicable to diverse applications. Specifically, energy-efficient routing allows data to be transmitted within the network using less energy. However, existing optimization algorithms often cause unreliable communication due to changes in network conditions. This also limits the performance of IoT-based WSN by leading to high communication delay, reduced network lifetime, and lower throughput performance in dynamic environments. In order to address these challenges, a multi-objective function-based routing scheme is introduced in this research to provide energy-efficient routing and extend network lifetime in IoT-based WSNs. Initially, the group of sensor nodes is divided into clusters to enhance the network lifetime. The best routing path is selected, which is the shortest distance between the base station and the sensor node. Thus, the Cluster Head (CH) is selected by implementing a new Hybrid Reptile Search-Artificial Gorilla Troops Optimization (HRS-AGTO) algorithm to ensure better data communication. Unlike traditional models, the developed HRS-AGTO algorithm iteratively adjusts its position in a large search space to provide optimal CH selection and routing by mimicking hunting behavior. By exploring a broader solution space, the developed HRS-AGTO algorithm identifies near-optimal solutions that enhance performance and enable data transmission across wider network coverage. It is achieved by solving the multi-objective function that considers measures like distance, throughput, latency, energy, and path loss. This multi-objective function is derived by initializing the population in the developed HRS-AGTO algorithm. For each solution, the values in the objective function are calculated by updating them with the fitness function. The process is repeated until the convergence criteria are met to achieve the desired outcome. This objective function is attained with the same HRS-AGTO for determining the best routes to transmit data with minimal energy requirements. In order to get accurate statistical outcomes, the developed model shows 7.6
The current study examines the effects of multi-pass friction stir processing (FSP) with 100
Abstract - The significance of online job platforms has expanded considerably over time. However, along with technological advancements, there has been a rise in fraudulent job postings that aim to exploit job seekers. These fake job listings often mislead users by offering unrealistic opportunities, requesting personal information, or demanding money, which may lead to financial loss and data misuse. Such fraudulent postings pose a serious threat to users by spreading misleading and harmful content. Therefore, there is a strong need for an efficient and reliable detection system. This project addresses the problem of identifying fake job postings by analyzing job- related textual data using machine learning techniques. Various supervised learning algorithms, including Random Forest, Decision Tree, Logistic Regression, and Naive Bayes, along with TF-IDF-based feature extraction, are utilized to classify job postings as real or fake. Key Words: Fake Job Detection, Machine Learning, Random Forest, Naive Bayes, Logistic Regression, TF- IDF, NLP
Titanium dioxide (TiO₂) nanoparticles are promising materials for biomedical and environmental applications because of their physicochemical stability, photocatalytic activity, and antibacterial properties. This study synthesized TiO₂ nanoparticles through an eco-friendly biological route using cell-free supernatants of Bacillus subtilis, Pseudomonas aeruginosa, and Saccharomyces cerevisiae isolated from natural sources and identified by standard microbiological, morphological, and biochemical methods. Extracellular biomolecules in the supernatants functioned as reducing, capping, and stabilizing agents during conversion of titanium isopropoxide into TiO₂ nanoparticles at 80–100 °C. UV–Visible spectroscopy, SEM, FTIR, and XRD confirmed nanoparticle formation and biomolecular involvement, with B. subtilis producing well-crystallized anatase TiO₂, whereas the other preparations showed comparatively lower crystallinity. SEM revealed predominantly spherical to quasi-spherical nanoparticles with microorganism-dependent surface organization such as B. subtilis produced relatively uniform particles with moderate localized agglomeration, P. aeruginosa generated porous and densely clustered assemblies, and S. cerevisiae yielded comparatively well-dispersed particles with limited aggregation. TEM further demonstrated distinct particle boundaries, mainly spherical to slightly oval morphologies, organic capping layers, and crystalline lattice fringes at higher magnification, while confirming differences in dispersion and aggregation among the microbial systems. All preparations exhibited concentration-dependent antibacterial activity against Bacillus cereus, Staphylococcus aureus, Enterococcus faecalis, Escherichia coli, Pseudomonas fluorescens, Klebsiella pneumoniae, Proteus mirabilis, and Salmonella typhi, with the crystalline B. subtilis-mediated nanoparticles showing greater activity. Therefore, microscopy complemented spectroscopic and diffraction evidence for successful biosynthesis. These findings demonstrate that microbial synthesis offers a sustainable route for producing TiO₂ nanoparticles with stable structural properties and significant antimicrobial potential, highlighting their promise for biomedical, environmental, and pharmaceutical applications.
