Institute of Aeronautical Engineering

Our investigation into synthesizing one-dimensional (1-D) mesoporous lead monoxide (PbO) nanofibers and their application as an anode material in lithium-ion batteries holds considerable practical significance. The PbO nanofibers were synthesized via electrospinning and subsequent calcination. We meticulously examined their properties using several techniques, including XRD, BET surface area analysis, FT-IR, Raman spectroscopy, FE-SEM, TEM, and EDX. The XRD measurements validate the presence of a pure orthorhombic PbO phase, whereas the FTIR and Raman spectroscopy results suggest a structurally coordinated PbO sample. The PbO nanofibers possess a BET-specific surface area of 61.23 m² g⁻¹, and FE-SEM and TEM images indicate that their diameter ranges from 90 to 150 nm. The electrical conductivity of the PbO nanofiber sample at 423 K is 3.38 × 10⁻⁶ S/cm. Subsequently, we evaluated the PbO nanofibers as an anode material in half-coin CR-2032 lithium batteries and performed electrochemical assessments. The initial discharge capacity of the PbO nanofiber was 1270 mAh g⁻¹. After 50 charge - discharge cycles, the PbO nanofibers exhibit a capacity of 372 mAh g⁻¹ as an anode material for lithium-ion batteries, indicating their viability for practical applications.

The Software Defined Networking (SDN) method has evolved to project future systems and collect novel application needs for several years. SDN delivers sources for enhancing management and system control by splitting data and control plane, and the control logic is federal in a controller. Conversely, the central logical control is a perfect objective for malicious assaults, chiefly Distributed Denial of Service (DDoS) threats. Deep Learning (DL) is one of the influential models useful in cyber-security, and numerous Network Intrusion Detection (NIDS) were developed in current studies. Some researchers have specified that deep neural networks (DNN) subtly perceive adversarial assaults. These attacks are examples of definite worries that cause DNNs to misclassify. Therefore, this manuscript develops a novel Cybersecurity in Software-Defined Networking utilizing Hybrid Deep Learning Models and a Binary Narwhal Optimizer (CSSDN-HDLBNO) approach. The presented CSSDN-HDLBNO approach provides a scalable and effective solution to safeguard against evolving cyber threats in DDoS attacks within the SDN environment. Initially, the CSSDN-HDLBNO approach utilizes min-max normalization to scale the features within a uniform range using data normalization. Furthermore, the binary narwhal optimizer (BNO)-based feature selection is accomplished to classify the most related features. For the DDoS attack classification process, the attention mechanism with convolutional neural network and bidirectional gated recurrent units (CNN-BiGRU-AM) is employed. To ensure optimal performance of the CNN-BiGRU-AM model, hyperparameter tuning is performed by utilizing the seagull optimization algorithm (SOA) model to enhance the efficiency and robustness of the detection system. A wide range of simulation analyses is implemented to certify the improved performance of the CSSDN-HDLBNO technique under the DDoS SDN dataset. The performance validation of the CSSDN-HDLBNO technique portrayed a superior accuracy value of 99.40% over existing models in diverse evaluation measures.

Nanotechnology has entered the worldwide industry immensely during the recent two decades. Nanoparticles have been widely used for reducing economic and environmental issues with lesser toxicity and have better chemical stability. Based on these applications, the present study is based on the nanoparticle aggregation effect on MHD flow by an expanding sheet with the joule heating effect and employing the convective boundary conditions. Using suitable similarity variables, the system of partial differential equations (PDEs) regulating the flow and heat transfer in the examined situation is turned into a set of ordinary differential equations (ODEs). These ODEs are then solved numerically using the RKF‐45 (Runge Kutta Fehlberg 45) performance and a shooting system. The solutions produced are utilized to inspect the effect of different nondimensional factors on the related flow and temperature profiles, which are shown graphically for improved visualization and understanding. In addition, the higher impacts of the magnetic parameter decrease the velocity profiles at around 1.05% and 2.15% in the absence and presence of nanoparticle aggregation, respectively. On the other hand, the temperature profiles intensify percentage‐wise at about 4.95% and 5.35% with and without nanoparticle aggregations, respectively, for the superior effects of the magnetic parameter. Increases in the heat source/sink and Eckart numbers, on the other hand, improve the thermal profile, while the Biot number has the reverse effect.

TiO2 and Zr/P substituted TiO2 nanocatalysts were prepared by the sol–gel technique. The phase confirmation, elementary concentrations, particle size, morphology and oxidation states of each element are observed using XRD (X-Ray diffraction), E-DAX (Energy dispersive X-ray spectrum), TEM (Transmission Electron Microscopy) and XPS (X-ray photoelectron spectroscopy). The X-ray diffraction peaks of TiO2 and Zr/p substituted TiO2 nano catalysts confirm the tetragonal anatase phase. E-DAX spectra of all nano catalysts exhibit an elemental ratio in each catalyst that is well matched with the initial percentages of weight ratio. The ZrPT6 (0.25 Wt % of Zr and 1 Wt % of P doped in TiO2) nanocatalyst has a grain size of 15.956 nm, making it the smallest of all the other nanocatalysts. TEM images’ predicted particle size and the XRD results agreed quite well. The average particle size of ZrPT6 and TiO2 nanocatalysts is 6.59 nm and 15.73 nm. The corresponding planes and inter-planer distance show that Zr/P is appropriately doped in TiO2, and these results are consistent with XRD. XPS reveals the oxidation states, quantitative surface and chemical composition of TiO2 and Zr/P substituted TiO2 nanocatalysts. The antifungal activity of the TiO2 and ZrPT6 nanocatalysts was tested against the organism Aspergillus niger. The pathogen Aspergillus niger-MTCC 282 with ZrPT6 nanocatalyst had a strong inhibition zone at 600 µg/mL which is 28.37 mm.

Heavy metal contamination of soil presents significant environmental and human health concerns worldwide. In response, alternative remediation strategies such as vermiremediation have gained attention for their eco-friendly approach. Earthworms, ubiquitous in soil ecosystems, play pivotal roles in soil health maintenance through organic matter decomposition, nutrient cycling, and soil aeration. Additionally, earthworms possess inherent mechanisms for coping with heavy metal exposure, making them natural candidates for remediation efforts. Their ability to bioaccumulate, transform, and immobilize heavy metals underscores their potential in mitigating soil pollution. Through controlled laboratory experiments and field studies, the effectiveness of vermiremediation utilizing earthworms, particularly species like Eisenia fetida, has been demonstrated in reducing heavy metal concentrations in contaminated soil. This review provides insights into the pivotal role of earthworms in soil ecosystems. It highlights their promising potential in remediating toxic heavy metal pollution, contributing to sustainable soil management practices.

These days, we regularly update our online status and post photos and comments. We can use our own image as a profile picture, create an appealing profile, and transform your snapshot into an animated sign, for example. In this research, a technique is proposed for turning an input target image into exaggerated, cartoonish images. We extract feature points from a target image and define the feature point model on reference images to distort the target image. Then, to this paper, we apply a feature-based warping algorithm. In order to make our outcome seem more cartoonish, we also apply the brightness quantization approach and the edge enhancement method to the target image that has been warped. We are now adjusting the target picture deformation, brightness quantization, and edge enhancement intensities for the ability to produce diverse outputs.

In order to raise the nation’s cleaning standards, specific steps are now being implemented. An increasing number of people are taking proactive steps to keep their environment clean. Additionally, the government initiates a number of initiatives to improve sanitation. In order to remind the businesses to promptly empty the bin, we shall work to develop a mechanism. Using the Internet of Things (IoT) to monitor garbage collection systems at a reasonable cost has been the main focus of the majority of the literature’s current work. While an IoT-based technology can monitor a waste collection system in real time, it cannot manage the overspill gasses that spread. Waste that is not properly disposed of results in harmful gasses, and radiation exposure has a negative impact on the environment, human health, and the greenhouse system. Given the significance of air pollutants, waste management and air pollution concentration monitoring and forecasting are highly necessary. Here, we describe an The Internet of Things (IoT) smart bin that forecasts air pollution in the vicinity of the bin and manages garbage disposal using an ESP 8266 model. For the purpose of generating alarm messages about bin condition and estimating the quantity of air pollutant carbon monoxide (CO) in the air at a given time, we experimented with a conventional model such as the ESP8266 and an ultrasound sensor. The generation and delivery of the alarm message to a sanitary worker was delayed by 4s as a result of the system. Together with messages from the warning mechanism, the system offered real-time garbage level monitoring. By using machine learning, the suggested works provide better accuracy than current solutions that rely on straightforward methods.

The proliferation of fake product reviews in the E-commerce industry has emerged as a significant challenge, undermining consumer trust and integrity in online platforms. This project addresses this pressing issue by developing an advanced system for detecting and eliminating fake reviews using cutting-edge methods for machine learning (ML) and natural language processing (NLP). The system integrates models such as Random Forest, Naive Bayes, MLP, and a Voting Classifier, trained on the “Amazon Yelp dataset” to ensure scalability and adaptability for large-scale applications. By providing a real-time assessment of review authenticity, the system empowers platform owners to take informed actions against spurious content, thereby preserving the integrity of online shopping experiences. The project highlights the importance of employing advanced NLP and ML techniques in combating fake reviews and contributes significantly to enhancing user trust in the online marketplace. The project aims to address the critical need for effective fake review detection and elimination systems in the E-commerce industry. The escalating prevalence of fake reviews on prominent platforms like Flipkart and Amazon undermines consumer trust, highlighting the urgency for a robust solution. By leveraging the “Amazon Yelp dataset” for model training, the study emphasizes scalability and adaptability for large-scale applications. Recognizing the escalating impact of fake reviews on user trust, this research addresses the critical need for platforms to combat spammers and keep alive the integrity of online shopping experiences. The proposed model not only provides a real-time assessment of review authenticity but also offers a foundation for website owners to take informed actions against spurious content. With potential applications for platforms of varying sizes, this sophisticated model demonstrates its efficacy in detecting spam reviews, contributing to the enhancement of user trust in the online marketplace.

In the current world data is more important to analyze the surroundings. All the new inventions or future predictions are made using IoT. These data to make our life easy. These days the maximum of data collection is done through IoT, because it is easy and the data can be stored in an organized way. Many countries are working on how to protect this data. The major issue in the world is data security or protection and the topic of research that is going on IoT devices is that these devices should be made that intelligent, so that they can detect useful information and store it on a cloud and storage of inadequate information can be avoided. So, how can we make our IoT devices intelligent. Here machine learning comes into the picture. Machine learning (ML) helps that device to analyze things from the given instructions and store the information on the cloud. ML helps developers to classify the data and clustering/finding pattern in it. With this help, IoT devices can scan the product and analysis of the product using past data or in-build functions. Using Machine learning IoT devices will not only increase the performance of the devices but it will also help in the security of the devices and the data. Whenever a product is related to social welfare or any security-based system, the security of that particular device is given priority because the data that the device is transmitting or receiving may be important or highly confidential. So, if the device is secured, then the leakage of data can be avoided. Apart from this machine learning can be used to detect malicious attacks, analyzing mobile endpoints, a combination of Ml & AI to learn human behavior and to automate wearisome security tasks, and many more. There are many other applications of machine learning in IoT like advancing smart city projects, smart transportations, managing Big Data, thread detection, any kind of accident tracker, and many more smart devices that will help to optimize the remote areas of a country.

The current study examines the magnetic field impact on the three-dimensional motion of Casson nanoliquid via a revolving stretchable surface with the porous media. The impact of thermal radiation, chemical reaction, heat source/sink, joule heating, and viscous dissipation on the fluid motion is also considered. Experts and engineers may increase the efficacy of chemical reactions or heat transportation processes by examining how reactions affect flow and designing systems with improved flows. The non-linear governing partial differential equations (PDEs) of the fluid flow problem are transformed into dimensionless ordinary differential equations (ODEs) with the similarity variables. Further, the obtained ODEs are solved numerically with the aid of Runge Kutta Fehlberg’s fourth-fifth order (RKF-45) approach. The influence of different factors on the various profiles is depicted using the visual representation. As the porosity and magnetic parameters increases, the velocity profile declines. For higher values of Casson fluid parameter, the velocity profile decreases. The increase in values of the radiation parameter, heat source/sink (HSS) parameter, and Eckert number enhances the thermal profile.

Photocatalytic CO2 reduction for solar fuel production has garnered significant attention due to its potential to address both the energy crisis and CO2 pollution. In this study, ZnCo2O4-rGO hybrid catalyst, specifically ZnCO2O4/10% rGO and ZnCo2O4/20%rGO, were designed by ultrasonic-assisted hydrothermal method for photocatalytic CO2 reduction to address energy and environmental challenges. We evaluated the photocatalytic reduction ability of CO2 to methanol and observed that incorporating rGO significantly reduced the recombination of photogenerated electron–hole pairs, thereby enhancing the photocatalytic activity of ZnCo2O4. Among the synthesized photocatalysts, ZnCo2O4 with 20% rGO exhibited the highest photocatalytic performance, attributed to its narrow band gap and efficient charge mobility. The ZnCo2O4/20%rGO heterogeneous photocatalyst maintaining its effectiveness through five consecutive reaction cycles without observable degradation in catalytic activity. ¹³CO2 isotopic experiment validated that the produced methanol was from the photoreduction of CO2. This result demonstrates that after 10 h of irradiation, the yield of methanol was 145 µmol/g, which is significantly higher than that obtained with pristine ZnCo2O4 (66.2 µmol/g) and previously-reported photocatalysts. This underscores that ZnCo2O4/20% rGO is a simple, efficient, and promising visible-light-driven photocatalyst for the photoreduction of CO2 into solar fuels.

This existing work has several beneficial advantages in science and engineering including developing more efficient cooling systems, elastic deformation has a substantial application in the ceramic foam and plastic industries. Environmental applications require waste discharge concentration through a different geometry (Cone and Wedge). The current article examines the influence of a magnetic field (MF) on nanofluid passing through a cone and wedge with mass and heat transmission. Solving nonlinear partial differential equations (PDEs) that employ similarity transformations to transform into a collection of ordinary differential equations. The temperature equation considers physical aspects like elastic deformation, while the concentration equation considers external sources of local contaminants. Using numerical methods (Runge-Kutta-Fehlberg 4th 5th and shooting method), ordinary differential equations and related reduced boundary conditions (BCs) are solved. Plots are used to illustrate the significance of dimensionless constraints and their effects. The novel outcomes show that increases in elastic deformation constraint decrease the temperature distribution , and a rise in the local pollutant external source increases the concentration profile. The rate of mass and heat transfer, as well as fluid surface drag force, is enhanced by including solid volume fraction in combination with other parameters. The cone performs well in the rate of heat transfer and fluid surface drag force, while the wedge is in the rate of mass transmission. The study's outcomes are helpful in several practical applications, including enhanced cooling systems, environmental remediation, processing of materials, and nanotechnology.