International Journal of Innovations in Science & Technology
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Evaluation of Thermal Mixing in T-Junctions Using Computational Fluid Dynamics (CFD)
Get PDFThe thermal mixing process in T-junctions presents a significant challenge in optimizing heat transfer and temperature distribution, especially in systems involving both hot and cold fluids. The problem addressed in this study was to understand how variations in inlet velocities, pipe diameters, flow rates, and turbulence models affect heat transfer and thermal mixing. The solution was achieved by performing detailed CFD simulations, evaluating these factors under controlled boundary conditions of 40 m/s hot inlet velocity, 30 m/s cold inlet velocity, and a 15 K temperature difference between the main and branch pipes. The results reveal that higher inlet velocities enhance thermal mixing, with outlet temperatures increasing from 223.382 K to 325.975 K as hot inlet velocity increases from 20 m/s to 40 m/s. Increasing the hot inlet diameter from 2 cm to 4 cm improves temperature distribution, raising the outlet temperature from 325.95 K to 329.797 K. The introduction of dual hot inlets further enhances the temperature to 329.797 K. Comparative analysis of turbulence models (k-ω and k-ε) indicates that the k-ω model provides more uniform temperature distribution. Moreover, variations in flow rates show that higher flow rates in the main pipe led to an outlet temperature of 312 K, while higher flow rates in the branch pipe reduced the outlet temperature to 305 K. This research offers critical insights for optimizing T-junction designs, improving thermal mixing, and enhancing heat transfer in industrial applications
Design And Implementation Of Black Box For Automobiles Using Esp 32
Get PDFA black box system in vehicles acts as an important tool that records important information to make vehicles safer, investigate accidents, and even improve the overall performance of the vehicles. This study will present the Black Box System that has been developed using ESP32 microcontrollers for cars to enhance data collection and analysis in automotive fields using technology. The Black Box System or Event Data Recorder (EDR) is an important tool in the enhancement of road safety, investigation of accidents, and evaluation of the performance of a vehicle. The system utilizes ESP32 as the main microcontroller since it is cost-effective, efficient, and can be programmed in multiple ways. It comprises several sensors and data acquisition modules to collect key parameters including speed, acceleration, geographical location, engine, and various diagnostic information about the vehicle. This paper also presents a detailed overview and integration of the system into Hardware and software parts of the automobile. A user-friendly interface facilitates data retrieval and analysis, supporting applications in fleet management, driver behavior monitoring, and accident investigations. The study focuses on the responsibility and protection of personal data, as well as ways of protecting personal data from misuse and violation of the law. Therefore, using ESP32 technology in the vehicle’s Black Box System is a great improvement towards road safety and vehicle monitoring. By ensuring data security and privacy, this system provides the users with a complete data set to support a decision-making process for self-employed drivers and other organizations
Investigating The Impact of Perovskite Layer Thickness Variation on The Performance of Perovskite Solar Cells
Get PDFThe present risk of the depletion of the non-renewable sources of energy at an alarming rate has encouraged man to lookup for new ways to produce power and move towards "Renewable Energy Resources". One major source of energy is the heat and intensity from the Sunlight using the Perovskite Solar Cells. This technology has captured extensive attention worldwide in previous few years due to its high efficiency, fast development, low-cost and easy manufacturing process. In this research, the thickness variation of different types of perovskite layers and their impacts on the functioning of the perovskite solar cells have been explored using SCAPS software. The absorption coefficient of semiconducting material is exponentially related to the thickness, so if absorption coefficient is high, lesser thickness can absorb more light. But to avoid the excessive resistance and to lessen the production cost, the thickness should be in the range of the depletion region width. Much smaller thickness yields weak static electric fields in the depletion region. The designs of different perovskite solar cell structures will be simulated and their effects will be critically analyzed in order to have detailed study
Comparative Analysis of Machine Learning Models for Lung Cancer Detection Using CT Scan Images
Get PDFThe CT scan provides useful information but has limitations in detecting subtle patterns. Machine learning models enhance cancer detection by extracting features, reducing errors, and enabling early-stage diagnosis. Unlike earlier studies that focused on single models, this paper compares three models: CNN, RF, and SVM. A total of 995 CT images were resized to 128x128 pixels, representing both healthy individuals and patients across the full range of lung cancer types. Using a feature hierarchy, CNN achieved a 96% validation accuracy, and RF reached 95%, showing robustness. However, SVM with an RBF kernel optimization outperformed the others, achieving over 98% accuracy with superior alignment of hyperplanes, particularly in detecting fine malignant patterns. The key metrics used in this study were sensitivity, specificity, and AUC, all of which showed a low false positive rate for early lung cancer detection, bridging theoretical accuracy and clinical practicality. Data volume and processing resources remain significant challenges for applying machine learning in early lung cancer diagnosis. To address these issues, we suggest hybrid architectures (e.g., CNN-SVM) that combine hierarchical feature learning and hyperplane optimization. These findings could pave the way for AI-based clinical approaches, improving patient diagnosis and treatment.
Silver Nanoparticles Synthesis by Serratia Marcescens W2 Strain, Its Biocontrol Efficacy Against Fungal Phytopathogens, And Its Effect on Wheat Seeds
Get PDFDue to its cost-effectiveness and eco-friendliness, the production of silver nanoparticles (AgNPs) utilizing biological agents has attracted a lot of attention. In this study, we investigate the potential of the Serratia marcescens W2 strain as a bio-factory for the production of silver nanoparticles and evaluate the biocontrol capability of this strain against fungal phytopathogens as well as its impact on wheat seeds. Using the extracellular enzymes and metabolites produced by Serratia marcescens W2, AgNPs were biosynthesised. The size, form, and composition of the AgNPs were determined using a variety of analytical techniques, such as X-ray diffraction (XRD), and scanning electron microscopy (SEM). AgNPs\u27 impressive ability to inhibit fungal growth in vitro experiments demonstrates their robust biocontrol capabilities. Microscopic and biochemical investigations helped to better clarify the AgNPs\u27 mode of action against these phytopathogens. Additionally, investigations on seed germination and seedling growth were used to evaluate the effect of the AgNPs on wheat seeds. As a result of the application of AgNPs, seed germination rates, and seedling vigor dramatically increased, according to the findings. Additionally, compared to the control group, the seedlings treated with AgNPs showed enhanced resistance to fungal infection. Overall, the results of this study demonstrate the potential of Serratia marcescens W2 strain in the green synthesis of AgNPs with improved antifungal activities. Furthermore, the use of these AgNPs promotes the germination and growth of wheat seedlings, indicating their potential use as a biocontrol agent and seed treatment to safeguard crops against fungus phytopathogens in sustainable agriculture. To completely understand the underlying mechanisms and determine the long-term impacts of AgNPs on the ecosystem and human health, more research is necessary
Digital Dermatologist: An AI-Powered Mobile App for Early Detection of Skin Diseases
Get PDFAn increasing number of people are experiencing skin problems, causing overcrowding in hospitals and clinics. This situation highlights the need for a quicker and more convenient way to diagnose these conditions. To address this, we have developed a mobile application that uses artificial intelligence (AI) to detect skin diseases. The app provides fast and useful information about skin issues through AI. Its user-friendly design makes it easy for anyone to use, even without technical knowledge. This tool helps people monitor their skin health and reduces the burden on healthcare facilities. By using the app, users can identify skin problems early and receive guidance on possible treatments
AI-Enhanced Pneumonia Detection with Visual Interpretability
Get PDFPneumonia is a serious lung infection that can be life-threatening, particularly for young children, the elderly, and people with weakened immune systems. Early detection is crucial but difficult because pneumonia signs on X-rays can be subtle. Many AI tools can help diagnose pneumonia, but they often work like “black boxes,” making it hard for doctors to trust their decisions. This study introduces a mobile app that uses Convolutional Neural Networks (CNNs) to detect pneumonia from X-rays. To improve transparency, we use Explainable AI (XAI) to highlight the areas of the X-ray that influenced the diagnosis. Additionally, we integrate a Large Language Model (LLM) to generate clear, structured medical reports. Our goal is to create a trustworthy and user-friendly tool for doctors in real-world settings
Improving Thermal Conductivity in Heat Sink Using Copper Foam-Paraffin Phase Change Materials
Get PDFPhase change materials (PCMs) are essential in thermal energy storage systems due to their abilities in heat transfer and storage. Yet, their low thermal conductivity restricts the rate at which energy is stored and released. Introducing copper metal foam into PCMs can greatly enhance their thermal conductivity. This research examines the boost in thermal conductivity of a metal foam-paraffin composite compared to pure paraffin. Copper metal foam, noted for its high thermal conductivity, effectively heightens the heat transfer rate within the composite. Both theoretical and experimental assessments of the thermal conductivity of the copper foam/PCM composite were carried out and compared. Findings indicated that the composite\u27s thermal conductivity reached 5.5 W/mK at 13 W, which is significantly higher than the 0.2 W/mK of pure PCM. Furthermore, the copper/PCM composite lowered the heat sink\u27s temperature by 25-30%. This improvement is due to the enhanced thermal pathways offered by the copper foam\u27s structure, notwithstanding the inverse relationship between infiltration ratio and pore density
Design of Multiband MIMO Antenna for 5G Applications
Get PDFThis study introduces a new antenna design that combines a bow-tie slot antenna with four Linear Tapered Slot Antennas (LTSAs) on a single aperture, supporting multiple frequency bands for 5G applications. The design uses a coplanar waveguide transmission line to feed the bow-tie slot antenna, and a MIMO configuration is applied to the LTSAs in the mm-wave band. The antenna shows a bi-directional radiation pattern in the microwave range with a maximum gain of 6.70 dB, and an end-fire radiation pattern for each LTSA in the mm-wave band. The design effectively covers several microwave bands and the n258 band in the mm-wave range, covering both lower and higher 5G frequency bands. With its compact and efficient structure, this antenna is a promising solution for multi-band applications, making it suitable for various wireless communication systems. Its versatility and performance make it a strong candidate for future 5G technologies
Object Detection in High Resolution Aerial Imagery Using Detection Transformer
Get PDFObject detection in high-resolution aerial imagery has received much attention nowadays due to its applications in geosciences, urban planning, disaster management, and surveil- lance. However, there exist challenges such as scale variation, cluttered backgrounds, occlusions, and less annotated datasets. Traditional CNNs have shown great promise, yet they fail to detect long-distance dependencies and complicated spatial relationships. This paper evaluates the function of DETR for object detection in aerial images. Unlike CNN-based detectors that depend on region proposal networks and anchor-based methods, DETR depends on a full end-to-end transformer architecture along with a direct set prediction method that removes the requirement for hand-designed priors. With extensive experiments carried out on datasets like Airbus Aircraft, Rare Planes, and DOTA, observations show that DETR performs better with mAP scores that are as much as 18% higher than ResNet-based architectures. Fur- Furthermore, we propose a hybrid model that is DETR-CNN, which partners both the strength of feature extraction from CNNs and the global attention mechanisms in DETR, thereby improving the accuracy of detection on both Horizontal and Oriented Bounding Box detections. Our results show that transformer-based models are most effective in aerial object detection, which bodes well for remote sensing, autonomous surveillance, and disaster response applications. This study presents an end-to-end DETR-based method for object detection in aerial imagery, demonstrating improvements in accuracy and simplicity over traditional methods