Metallurgical and Materials Engineering (E-Journal)
Not a member yet
915 research outputs found
Sort by
Alanine -Zn(Ii) Complex Via Potentiometric Method And Study Of Its Stability Constant, Thermodynamic Parameters And Antimicrobial Effect
Get PDFThe Alanine-Zn(II) complex is a bio-coordination compound formed by the binding of zinc(II) ions with alanine, a non-essential amino acid, displaying potential biological activities and therapeutic applications. This research investigates the complexation behavior of alanine with Zn(II) ions through potentiometric titration methods at three different temperatures (303K, 308K, and 313K) under controlled ionic strength conditions. The stability constants of the Zn(II)-alanine complex were determined using the modified Irving-Rossotti technique. The thermodynamic parameters including Gibbs free energy (ΔG), enthalpy change (ΔH), and entropy change (ΔS) were evaluated to understand the thermodynamic nature and spontaneity of the complexation process. The proton-ligand stability constants (pKa) and metal-ligand stability constants (log K) were calculated at different temperatures to understand the binding strength of the complex. Furthermore, the antimicrobial potential of the synthesized Zn(II)-alanine complex was assessed against selected pathogenic microorganisms using standard protocols to explore its possible therapeutic applications. The complex formation was validated through various physicochemical analyses, and the results indicate the successful formation of stable coordination compounds
Exploring The Role Of Selenium And Its Nanoformulation In The Progression Of Neurodegenerative Diseases: A Review
Get PDFSelenium has been used traditionally for its various roles in traditional dosage forms, specifically for enhancing memory and overall human health. As per a recent research study, selenium is found to be safe. The positive role of selenium and its protein (SELENOP) promises to control and manage neurodegenerative diseases by directly and indirectly interacting with the pathophysiology of neurodegenerative diseases like Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD). Selenium, combined with established drugs for neurodegenerative diseases, is explored in its nanoformulation, elevating its therapeutic action. Selenium can be a good candidate in exploring prevention and managing less explored diseases like Huntington's disease (HD) and other neurodegenerative diseases. Selenium is used as a neuroprotective agent by redox regulation and antioxidant defense mechanisms, which protect neurons from oxidative damage
Evaluating Geotechnical Hazards For Long-Distance Gas Pipelines Across Pakistan’s Northern Areas
Get PDFThe geotechnical hazard assessment framework is vital to the safe operation of energy pipelines by ensuring that the structure of these pipelines does not suffer in the mountainous areas. This paper describes an open-source framework to determine terrain stability along potential long-distance gas pipeline corridors constructed in northern Pakistan, emphasising the Muzaffarabad area. The method combines machine learning algorithms, geographic information systems (GIS), and conventional geotechnical indicators to define the high-risk zones of landslides and seismic vulnerability. Based on a regionally specific data set containing 1,212 samples, two ensemble approaches, Random Forest and XGBoost, were learned against topographical, hydrological, geological, and seismic characteristics. Random Forest has the highest percentage accuracy (76.95%) and ROC, AUC (0.8384), which makes it a resilient model to apply in a terrain classification exercise. Flow accumulation, elevation, and precipitation were identified as significant factors of slope failure by feature importance analysis. A composite hazard index was computed, and pipeline segments were assigned Low, Moderate, High and Critical risk zones. Despite that, it is essential to note that the scores on segments 6-9 were critical at a frequency of more than 90% of observations. In addition, the seismic threat was simulated by taking synthetic Peak Ground Acceleration (PGA) and Factor of Safety (FOS) values and showing high correlation with the hazard zones predicted with machine learning. A blend of ML predictions and geotechnical thresholds has permitted a successful multi-modal validation. The suggested approach, which is entirely written in Python, allows one to pilot a reproducible, scalable, and region-specific approach to mitigating infrastructure risk. It gives practical information to engineers and planners practising in geologically sensitive locations and enables sustainable energy infrastructure planning in line with national safety and sustainability objectives
Scalability and Efficiency in Distributed Big Data Architectures: A Comparative Study
Get PDFWith the rapid expansion of the size of data, there is a need for the development of scalable and efficient architectures for large scale data processing. This research conducts a comparative analysis between the performance, scalability and efficiency of the Apache Hadoop, Apache Spark, Apache Flink, and Google Bigtable big data frameworks. Finally, the experimental results indicate that the Apache Spark is faster in execution times by 3.5× than Hadoop, and the Apache Flink achieves 40% lower latency on real time analytics than Spark. Google Bigtable had good throughput at 5 million queries a second, but it was not flexible to computationally intense processes. Furthermore, this study examined the application of the machine learning and blockchain technologies in the implementation of the distributed systems for the unified backend that incorporates processing efficiency improvement by 25% and data integrity with the added computational overhead of 12%. The research demonstrates that Flink is most suitable for real time data streams, spark is the best tool for iterative workloads and bigtable is the most appropriate for structured high throughput storage. Nevertheless, questions remain on how to scale in the extreme workload case and balance security with performance. Finally, future research will focus on hybrid architectures that enable high speed and security performance for the next generation big data applications
Digital Dentistry & Material Science: Sculpting the Future of Oral Care- A Narrative Review
Get PDFThis review examines the dynamic intersection of digital dentistry and material science, exploring current trends and advancements shaping modern dental practice. The integration of digital technologies, including intraoral scanners, cone-beam computed tomography (CBCT), and computer-aided design/computer-aided manufacturing (CAD/CAM) systems, has revolutionized diagnostic and restorative workflows. Concurrently, material science innovations, particularly in ceramics, composites, and 3D printing resins, are expanding the possibilities for dental restorations and prostheses. This review analyzes the accuracy, efficiency, and clinical outcomes associated with these technologies and materials, emphasizing the impact on patient care. It further explores the integration of digital workflows, including virtual treatment planning and teledentistry, and discusses the role of emerging technologies like artificial intelligence (AI), VR in future dental applications. By synthesizing current research and clinical evidence, this review provides a comprehensive overview of the evolving landscape of digital dentistry and material science, highlighting the potential for enhanced precision, predictability, and patient satisfaction in dental treatments, while also addressing current limitations and future directions
Digital Media and Artificial Intelligence in Education: A Smart Pedagogy Approach
Get PDFThis study explores the integration of digital media and Artificial Intelligence (AI) in education, addressing the need for effective teaching and learning practices in the digital age. Grounded in the Technological Pedagogical Content Knowledge (TPACK), this research aims to investigate the key principles and approaches of smart pedagogy, effective integration, and implications for teacher professional development and education policy. Specifically, this study seeks to answer three research questions: What are the key principles and approaches of smart pedagogy in the context of digital media and AI? How can digital media and AI be effectively integrated into teaching and learning practices? What are the implications of digital media and AI for teacher professional development and education policy? This research contributes to the existing body of knowledge on the convergence of technology and education, highlighting the significance of digital media and AI in enhancing teaching and learning practices. Building on recent research, this study employs a qualitative approach, using focus group interviews to gather data from educators, students, and administrators. The findings reveal key insights into the effective integration of digital media and AI, highlighting the need for teacher professional development and policy changes to support innovative teaching and learning practices. The study's results have implications for educators, policymakers, and researchers, suggesting avenues for future research and practical applications
The Role of HACCP-Based Hygiene Management in Safeguarding Paediatric Health in the Global Hospitality Supply Chain
Get PDFThis research explores the application of HACCP (Hazard Analysis and Critical Control Points) systems in global hospitality businesses, focusing on their effectiveness in protecting child guests from foodborne illnesses and hygiene-related risks. By examining case studies from luxury hotels, resorts, and international restaurant chains, the paper highlights how HACCP-based management strengthens food safety and safeguards paediatric health, especially in tourism-intensive destinations. The study also discusses gaps in compliance and suggests improvements to enhance global child health outcomes in the hospitality industry
Multimodal Deep Learning Framework for Proactive Plant Disease Diagnosis
Get PDFEarly diagnosis, the correct diagnosis of plant diseases is important to ensure sustainable agriculture and the minimalization of the loss of production. Traditional approaches of plant disease detection, which involve manual inspection and single modal imaging, are highly cumbersome, erroneous and lack in capturing the niche characteristics of the disease. Some recent achievements of deep learning advocate for possible automatic plant disease diagnosis; however, still most of the current models are plagued from low generalization capability, high computational cost and the issue of real time implementation. To alleviate these difficulties, this article introduces a brand-new multiple-mode deep learning framework, that combines RGB, hyperspectral and thermal imaging to take on the task of setting up precision and efficiency for plant disease detection. The described framework makes use of EfficientNet-based CNN for spatial feature extraction from RGB images, 1D-CNN for hyperspectral spectral feature learning and Vision Transformers (ViT) for learning long-range contextual dependencies. Above sensor- features are fused by Means of weighted summation methodology, dynamically adjusts contribution of per modality to Obtain endurance and accurate. To achieve real-time performance, the model is optimized via quantization, knowledge distillation and model pruning, with a substantial decrease in its computational load. The final optimal model is implemented in NVIDIA Jetson Nano to allow low-latency inference supporting high precision agriculture. The results of the experimental results show, the proposed multi-modal framework has achieved 97.8% accuracy, 96.5% precision, 95.7% recall and 96.1% score of F, all far exceed traditional deep learning models of ResNet-50, VGG-16, EfficientNet and Vision Transformers (ViT). Moreover, the framework offers inferences in 20 milliseconds, which makes it really suitable for real-time applications. Accomplishing a successful integration of multi-modal data fusion and model optimization not only increase classification performance, but also makes the solution/matter practical and deployable in real-world agricultural environment. The proposed framework provides a hopeful solution to smart farming, which provides a possibility of detecting disease early and managing effectively the crops
A Robust MI-Based Hybrid Diagnostic Model for Early Detection of Heart Diseases
Get PDFHeart disease operates as one of the leading dangerous causes of death worldwide thus humans require both precise and speedy medical diagnosis applications. Machine learning (ML) exhibits impressive potential to boost clinical decision-making each year because it effectively duplicates patterns within complex HiMed data. Machine learning demonstrates pattern imitiation through this capability. The main purpose of this research involved the development of a hybrid machine learning system that predicted heart disease. The system utilizes majority voting ensemble method to unite SVM with DT and RF classifiers for prediction purposes. The research utilizes the Cleveland Heart Disease dataset found at UCI Machine Learning Repository to conduct training and testing operations. The preprocessing procedures contain One-hot category encoding together with normalization of data and Recursive Feature Elimination (RFE) feature selection functionality. The suggested hybrid combination model achieves 92.5% accuracy and 91.8% precision while reaching 93.2% recall and 92.5% F1-score making it perform better than single classifiers. The findings match with the conclusion about the hybrid ensemble approach being more resilient with general capabilities and diagnostic accuracy. Such systems prove to be an excellent practical solution for operational medical decision programs used in actual healthcare settings
Stability Analysis of Nonlinear Fluid Flows through Mathematical and Computational Approaches
Get PDFIn a mathematical and computational approach to this research, the instability of nonlinear fluid flows is investigated. Four advanced numerical algorithms named Finite Volume Method (FVM), Lattice Boltzmann Method (LBM), Spectral Element Method (SEM), and Weighted Essentially Non-Oscillatory (WENO) scheme are utilized to analyze the dynamics of the complex fluid systems. Fluid flow under different such conditions is then simulated using these algorithms as varying Reynolds numbers, thermal gradients, and non-Newtonian characteristics. The results show that changes in viscosity, Reynolds number, and thermal conditions lead to highly sensitive stability of the flow. As an example, the flow is unstable over Reynolds numbers larger than 1500, and the flow oscillations increase in intensity with increased thermal gradients. The relative error of the FVM was 2.5% high accuracy, while the WENO scheme had a better performance on the sharp gradient when compared with other methods (1.8%). Algorithmic performance comparison of the SEM and LBM revealed that due to superior computational efficiency for large scale simulations (20-30%), the SEM and LBM achieved processing time savings of a factor of 20 to 30. Indeed, the study helps provide robust framework for predicting and controlling fluid dynamics in systems of complexity. Such findings are critically important for aerospace, energy systems and environmental engineering applications