Metallurgical and Materials Engineering (E-Journal)
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Badge Based Resin: A Novel Method For The Durable Preservation Of Powder Developed Latent Fingerprints
Get PDFThe preservation of latent fingerprints is a crucial aspect of forensic investigations, as it enables the identification of individuals involved in criminal activities. Traditionally, the lifting methods have been widely used for capturing latent prints; however, it presents limitations in terms of durability, clarity, and applicability to different surfaces. This paper investigates a novel method for preserving latent fingerprints which is powder developed, using bisphenol A diglycidyl ether (BADGE) based resin, which offers several advantages over the conventional lifting technique. There already exist various lifting methods for the powder developed fingerprints such as tape lifting, using hinge lifters and gel-lifters (gelatin-lifters). However, these methods have disadvantages over the novel method of powder developed latent fingerprint using BADGE based resin method discussed here, for a long-term preservation of fingerprints.
BADGE based resin, with its superior clarity, strength, and resistance to environmental degradation, ensures long-term preservation of fingerprint details with minimal distortion, even under adverse conditions. Unlike hinge lifters, which rely on adhesive-backed materials that can be prone to damage or leave behind residue, BADGE based resin provides a more durable and rigid medium for lifting prints, ensuring better preservation and clarity. Its structural integrity allows for the precise handling of prints without the risk of distortion, which is especially important when lifting from textured or irregular surfaces. In addition, BADGEbased resin is more resistant to environmental conditions like temperature and humidity, which can influence the adhesive properties of hinge lifters. The adhesive tape lifters can leave adhesive residue on prints, in addition to this may distort or damage delicate prints. According to some studies gel lifters can easily deform the prints under pressure as they are susceptible to temperature and humidity changes, affecting adhesive strength. This hardened resin preserves the fine details of the fingerprint ridges and minutiae, providing an exceptionally clear and stable record of the print over a long period of time. The study delivers the effectiveness of BADGE based resin as a fingerprint lifting cum preservation method by evaluating factors such as print preservation quality, ease of application, and potential for future analysis. The results indicates that BADGE based resin provides better clarity and structural integrity, making it a promising alternative for forensic fingerprint preservation. Furthermore, this novel method demonstrates significant potential in improving the reliability and accuracy of fingerprint analysis in criminal investigations
Proactive Cyber Security: End-To-End Deep Learning For Web Attack Mitigation
Get PDFAs web attacks grow increasingly sophisticated, the need for advanced and proactive security solutions has never been greater. Traditional rule-based and signature-based systems struggle to keep pace with evolving threats, necessitating innovative approaches. This paper proposes an end-to-end deep learning-based framework for detecting and mitigating web attacks in real-time. By leveraging deep neural networks, the system analyzes web traffic to identify malicious patterns, such as SQL injection, cross-site scripting (XSS), and denial-of-service (DoS) attacks. The system's ability to learn from vast amounts of data enables it to adapt to new and unknown threats, significantly reducing false positives and improving detection accuracy. The proposed solution offers a scalable, adaptive, and automated defense mechanism, ensuring robust protection for web applications against a wide range of cyber threats
Environmental Improvement Of Geotechnical Characteristics Of Weak Soils Using Polymers And Recycled Rubber Components
Get PDFMany construction projects are required to be erected on soils characterized as weak or problematic. Weak soils need to modify and improving their properties to create a solid layer for shallow foundations and pavements. In addition, from an environmental perspective, the possible use of waste tires in soil improvement has been considered as it has few adverse environmental effects. The presence of waste rubber fiber and rubber powder has not been used in the soils improved by traditional materials, because their use leads to a decrease in the resistance of the lime concrete. In this research, the effects of adding different amounts of waste rubber and waste rubber fibers on the mechanical features of treated lime and lime-fly ash additives are investigated. Results shows that the addition of rubber powder slightly reduces the compressive strength of the improved soil. Meanwhile, the use of rubber fibers significantly improves the compressive strength, ductility, fracture strain, as well as the moduli of elasticity, bulk, and shear strength in all processing times. Also, adding 12% of waste rubber powder and 1% of rubber fibers to the lime and lime-fly ash additive can provide good and reduces adverse environmental effects
Enhancing Carbon Capture And CO2 Reduction Processes Using Machine Learning And AI Technologies To Improve Biomedical Outcomes
Get PDFEnhancing carbon capture and CO2 reduction processes using Machine Learning and AI technologies to mitigate health risks and improve biomedical outcomes is a critical area of research that combines environmental science with healthcare innovation. By leveraging advanced algorithms and data analytics, researchers can optimize carbon capture systems to operate more efficiently, thereby reducing greenhouse gas emissions. Additionally, these technologies can be employed to model the impact of air quality on public health. Biomedical outcomes such as asthma rates, respiratory diseases, and cardiovascular conditions can be better understood through the analysis of pollutant exposure levels. By integrating environmental data with health records, researchers can identify correlations between air quality and disease prevalence. This approach not only aids in developing targeted interventions but also informs policymakers about the potential health benefits of improving air quality
Ultrasound Findings Of Patients With Microscopic Hematuria Considering The Patients' History Through Questionnaire
Get PDFPurpose: This study was performed to determine the sonographic findings of urinary system to clarify the prevalence of disease as determined by age, sex and the degree of haematuria at presentation in patients with microscopic hematuria attending to Amiralmomenin Hospital (Tehran, Iran) in 2016 and 2017.
Methods: In this observational study that was performed as a cross-sectional comparative descriptive survey, 216 consecutive patients with microscopic hematuria attending to Amiralmomenin Hospital in 2016 and 2017 were enrolled and the sonographic findings of urinary system were determined.
Results: The findings revealed that sonography was positive in 98.1%. The findings were as stone, cyst plus stone, and others in 80.7%, 13.7%, and 5.6%, respectively. The findings were in kidney, ureter, and bladder in 84.8%, 1.9%, 1.9% and 11.4% more than one location, respectively. It was in left side, right side, and bilateral in 23.7%, 19.3%, and 57%, respectively. Age was the only related factor and was higher in those with mass.
Conclusions: According to the obtained results in this study, it may be concluded that majority of patients with microscopic hematuria would have positive sonography that stone is the most common
Effects Of Engine Nacelle Dimension On Steam Turbine Cooling Performance And Optimization Of Dimension To Improve Cooling Performance
Get PDFConsidering the importance of increasing the performance of steam turbines and the effect of dimensional optimization on them, this paper aims to investigate the impacts of the nozzle's optimum axial distance to the engine nacelle and the engine nacelle length on the engine’s performance. In order to provide a close simulation of the actual condition, the results were obtained by solving the 2D steady-state compressible Reynolds-averaged Navier-Stokes (RANS) equations using the Finite Volume Method and the Shear Stress Transport (SST) turbulence model (FVM). The findings demonstrated that the mass flow rate of the coolant fluid rose by more than 50% as the nacelle length increased but was not considered when the nozzle-to-nacelle distance increased
Comparative Analysis Of Print Ink Density Of Conventional And Soya-Based Ink On Paper Board Using Offset Printing Process
Get PDFThe shift towards environmentally sustainable materials in the printing industry has led to increased interest in alternative ink formulations, such as soya-based inks, which offer reduced environmental impact compared to conventional petroleum-based inks. This study presents a comparative analysis of print ink density achieved using conventional and soya-based inks on paperboard substrates through the offset printing process. Ink density plays a critical role in determining print quality, influencing factors such as colour strength, image sharpness, and overall visual consistency. In this research, controlled printing trials were conducted using standard offset press conditions to ensure uniformity. Both ink types were applied to identical paperboard substrates, and ink density measurements were taken using a densitometer at designated control points. The aim is to evaluate the printing performance and visual strength of soya-based ink in relation to conventional ink, contributing to the understanding of its viability in commercial printing applications. The study offers insights into the print behaviour of sustainable inks and supports the industry's movement toward eco-friendly practices without compromising essential print quality parameters
AI-Enabled Predictive Maintenance Framework For Connected Vehicles Using Cloud-Based Web Interfaces
Get PDFA predictive maintenance framework based on arti- ficial intelligence (AI) is presented. Connected vehicles transmit vehicle-generated data to a cloud server from multiple vehicles. The data is processed with the AI model to forecast vehicle main- tenance requirements and issues and for scheduling maintenance operations in advance. The results—displayed on an easy-to-use, cloud-based web interface—consist of multiple-choice dropdowns to select the desired query for the AI model for processing and forecasting. The web interface facilitates smooth access to the AI process results and allows users to analyze data and identify the maintenance needs of individual connected vehicles
Impact of Coiflet Wavelet Decomposition on Forecasting Accuracy: Shifts in ARIMA and Exponential Smoothing Performance
Get PDFAccurate electricity demand forecasting is essential for efficient energy management and resource allocation. This study investigates the impact of Coiflet wavelet decomposition on the forecasting performance of Exponential Smoothing (ES) and ARIMA models. Two experimental approaches were employed: one using raw data and another incorporating wavelet denoising. Without wavelet transformation, ES performed better than ARIMA in the testing phase, with RMSE values of 13.62 and 13.93, MAE values of 11.22 and 11.54, and MAPE values of 3.04% and 3.14%, respectively. However, after applying wavelet decomposition, ARIMA showed significant improvement, reducing RMSE by 24.6%, MAE by 23.7%, and MAPE by 23.5% in the testing phase, outperforming ES. The hybrid ARIMA-wavelet model emerged as the most robust approach for forecasting electricity demand, demonstrating the effectiveness of wavelet-based denoising in improving predictive accuracy. These findings highlight the potential of integrating wavelet analysis with statistical forecasting models for more reliable time series predictions
Maximizing Electric Vehicle Battery Efficiency: A Multi-Model Machine Learning Approach for RUL Prediction of NMC-LCO Batteries
Get PDFElectric vehicles (EV) are becoming more prevalent because they are good for the environment and don't cost much to run. One big problem with EVs, though, is that their batteries don't last long. There is a complete way to figure out how long Nickel Manganese Cobalt-Lithium Cobalt Oxide (NMC-LCO) batteries will still work after this study. The information used in this study comes via the Hawaii Natural Energy Institute consist of 15 different batteries that were put through over 1000 rounds of controlled settings. A method with several steps is used, starting with collecting data and preparing it, then choosing features and getting rid of outliers. The RUL forecast method is made with machine learning (ML) methods like Bagging Regressor, XG Boost, Cat Boost, Light GBM and Extra Trees Regressor. Feature value analysis helps find important factors that affect the health and lives of a battery. Statistical tests show that there are no missing as well as duplicate data points and getting rid of outliers makes the method more accurate. Not surprisingly, XG Boost turned out to be the best algorithm, making predictions that were very close to being correct. This study shows how important RUL forecast is for improving battery lifetime management, especially in electric car uses, to make sure that resources are used in the best way possible, costs are kept low, and the environment is protected