Metallurgical and Materials Engineering (E-Journal)
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Investigation Of Synthesis Methods For Novel Pyrrolo[2,3-D]Pyrimidine Derivatives As Kinase Inhibitor Compounds
Get PDFThe current study has been planned to synthesize and assess the pharmacological characteristics of pyrrolo[2,3-d]pyrimidine derivatives as kinase inhibitors. The findings of the study indicated that different structural modifications, especially at critical cyclic sites, significantly influenced the development of inhibitory potency and specificity against the target enzymes. Compound 5k was identified as a highly potent multi-target inhibitor with an IC50 value in the nanomolar scale and is capable of inducing apoptosis and facilitating cell cycle arrest in HepG2 cells. These results not only highlight the significance of rational compound design based on efficient chemical structures but also reveal new avenues for the design of anticancer drugs with enhanced efficacy. One of the major accomplishments of this study is the establishment of optimized and effective synthetic procedures for the synthesis of pyrrolopyrimidine derivatives with significant yield and sufficient levels of purity. The application of new reaction conditions and advanced catalytic systems, including microwave-assisted reactions and DBU-catalyzed cyclization, allowed for the circumvention of the drawbacks associated with conventional methodologies. From the pharmacological perspective, the study showed that the pyrrolopyrimidine derivatives synthesized were able to interact effectively with the ATP-binding site of kinase enzymes. Moreover, data about compounds 14a and 17 showed that the derivatives can cause cell cycle arrest at the G1/S phase, which is especially useful for the treatment of cancers that manifest with increased cell growth. The 2-thioalkyl-6-amino-4-oxo pyrimidine compounds were able to engage effectively in a Michael reaction with functionalized nitroalkenes. In addition, the substitution of the thioalkyl group at the C2 position of pyrrolo[3,2-d]pyrimidines was successfully realized
Analyzing Data with Different Charts and Visualizations in Power BI
Get PDFThis paper explores the process of Extract, Transform, and Load (ETL) and its application in data analysis using Power BI. ETL is a critical step in the data integration pipeline, allowing organizations to collect, clean, and structure data from various sources for insightful analysis. The study delves into the stages of ETL, detailing how data is extracted from multiple sources, transformed through data cleaning and aggregation techniques, and loaded into a data model for reporting. Additionally, the paper highlights how Power BI, a powerful business intelligence tool, leverages this prepared data for creating interactive visualizations, reports, and dashboards. By combining ETL processes with Power BI's visualization capabilities, businesses can effectively analyze large datasets, uncover trends, and make data-driven decisions. The paper also discusses challenges related to data quality, performance optimization, and the integration of real-time data for enhanced analytics.The objectives of this paper are to gain an understanding of the ETL process and to demonstrate the visualization of a dashboard in Power BI. Overall, it demonstrates the synergy between ETL and Power BI in enabling comprehensive data analysis for organizations
The Role of Artificial Intelligence in Personalized Medicine: Challenges and Opportunities
Get PDFThe integration of Artificial Intelligence (AI) in personalized medicine has revolutionized healthcare by enabling precise, data-driven, and patient-specific treatment strategies. AI-powered algorithms, particularly those leveraging machine learning (ML) and deep learning (DL), have enhanced the ability to analyze vast datasets, uncover hidden patterns, and generate predictive models that facilitate early disease detection, drug discovery, and customized treatment regimens. AI applications in genomics, medical imaging, and electronic health records (EHRs) have significantly contributed to the advancement of precision medicine, ensuring more accurate diagnoses and effective therapies.Despite these remarkable advancements, the implementation of AI in personalized medicine presents several challenges. Data privacy and security concerns are at the forefront, as the use of AI relies heavily on patient data, which necessitates strict regulatory compliance and ethical considerations.
Additionally, biases in AI algorithms due to imbalanced training datasets can lead to disparities in medical outcomes, disproportionately affecting underrepresented populations. The integration of AI into clinical workflows is another significant hurdle, as healthcare providers require specialized training to interpret AI-generated insights and incorporate them into patient care effectively. Moreover, the need for standardized protocols and regulatory frameworks remains critical to ensuring the reliability, safety, and ethical application of AI in medical practice.Opportunities for AI in personalized medicine continue to expand with advancements in computational power, data analytics, and collaborative efforts between medical researchers and AI developers. Emerging technologies such as explainable AI (XAI) aim to enhance transparency in decision-making, allowing physicians and patients to better understand AI-generated recommendations.
Additionally, federated learning techniques provide a promising solution to data-sharing challenges by enabling AI models to be trained across multiple institutions while preserving patient privacy. The convergence of AI with other innovations, such as blockchain for secure data management and the Internet of Medical Things (IoMT) for real-time patient monitoring, further strengthens its role in personalized medicine.This paper explores the transformative potential of AI in personalized medicine, analyzing its key applications, limitations, and future prospects. A thorough examination of current AI-driven methodologies, case studies, and policy considerations will provide a holistic understanding of the evolving landscape. While AI holds immense promise in improving patient outcomes through tailored treatments, addressing its challenges through interdisciplinary collaboration and regulatory advancements is crucial to maximizing its benefits. As AI continues to shape the future of medicine, a balanced approach that integrates technological innovation with ethical responsibility will be essential in harnessing its full potential for personalized healthcare solutions
Wear Rate Analysis of Metal Matrix Composite Using Machine Learning Algorithms
Get PDFThere is a lot of interest in a compound with improved mechanical power, toughness, wear resilience, and increased electric and thermal conductance. This work examined the tribological conduct and fabrication of the titanium metal matrix composite (TiMMC) augmented with graphene (Gr) and tungsten carbide (WC) fragments. The TiMMC, which had 8 percent mass percentages of WC and Gr, was created using stir casting. Taguchi's L27 orthogonal grid method was used for designing the tribological investigations, which were then conducted as wear experiments with a pinon-disc gadget. ANOVA and Taguchi's study show that loading and range have the most effect on wear percentage, accompanied by speed.By examining the worn areas of the composite samples using scanning electron microscopy, the wear mechanism was determined. The wear rate statistics were correctly categorized by machine learning classifying techniques such as random forest, support vector machines, and XG-Boost methods, which provided accuracy values of 72%, 66%, and 56.3%, respectively. Notwithstanding the encouraging outcomes, the research acknowledges that the system's efficiency may differ depending on certain properties of the composite component and operating circumstances. Therefore, it motivates further research to validate and extend these novel discoveries over a larger range of components and circumstances.
 
Advanced Finite Element Methods for Solving Fluid Dynamics Problems in Engineering Applications
Get PDFIn this research, Advanced Finite Element Methods (FEM) for engineering applications’ fluid dynamics problem solving are investigated, where high order discretization, hybrid solvers and AI enhanced FEM models are studied. SUPG, VMS and DG methods were analyzed in terms of their accuracy and efficiency of computation. We calculated about 35% reduction in numerical diffusion using SUPG vis a vis conventional FEM carried out at Reynolds numbers Re = 10⁵ to 10⁷. Turbulence modeling accuracy was enhanced by 28% with VMS approach, whereas 42% increased shock capturing capability have been realized with DG-FEM. Moreover, the computational time was reduced to 50% by integrating physics informed neural networks (PINNs) at a 97% accuracy level. The research also validated hybrid mesh free FEM approaches that allow 15% higher efficiency in fluid structure interaction problems. The fact that we can implement these findings in the context of real time engineering applications using AI enhanced FEM techniques indicates that these are strong candidates. Finally, the study shows that the advanced FEM methods are more accurate, more stable and far more computationally efficient than traditional models. FEM solvers accelerated by GPU and adaptive meshing strategies should be explored in future research for further optimality
Fuzzy Logic Based Power Distribution Strategy for Hybrid Fuel Cell Vehicle
Get PDFCurrently zero emission vehicles are the need of automotive industry. This research focuses on design of powertrain and power distribution strategy for fuel cell hybrid vehicle. Vehicle driven only by fuel cell as a single source have various disadvantages such as sluggish response to transient power requirements, higher fuel consumption, no provision for storage of energy during lost during braking, higher cost, lower range, more packaging space required. To overcome this, fuel cell can be combined with auxiliary energy storage source. This paper proposes powertrain topology for vehicle with Fuel cell and battery (FC+B) combination. Power split strategy is defined using Fuzzy logic. Simulations are performed in MATLAB SIMULINK for FTP-75 drive cycle. Results of proposed strategy are compared with FC alone powertrain. Results shows that with proposed powertrain configuration, fuel consumption is reduced thereby achieving higher driving range
A Multiscale Fusion Network Integrating ConvNeXtSmall and EfficientNetB0 with Enhanced Image Preprocessing for Robust Classification
Get PDFIn this paper, we introduce the Multiscale Fusion Network (MFNet), a pioneering approach that combines ConvNeXtSmall and EfficientNetB0 architectures with advanced preprocessing techniques to revolutionize image classification. By integrating Multiscale Retinex and GrabCut segmentation, MFNet enhances perceptual quality, extracts meaningful features, and minimizes background noise. Trained on a meticulously prepared dataset, our model demonstrates superior performance, achieving high accuracy and robustness across diverse datasets. This paper delves into the architectural synergy, preprocessing innovations, and rigorous evaluation that establish MFNet as a leading solution for image classification tasks
Development of a Modified Predictive Coding Algorithm for High-Resolution Image Compression in Real-Time Structural Health Monitoring of Metallic Component
Get PDFReal-time monitoring requirements of structural integrity in metallic components across aerospace and automotive and civil engineering sectors drive the need to create reliable image compression methods. The detection of small defects including cracks and corrosion as well as deformations depends greatly on high-resolution imaging systems. The massive amount of generated image data creates problems with both data storage and its transmission requirements. The research advocates for developing a Modified Predictive Coding (MPC) algorithm which specializes in real-time high-resolution image compression for Structural Health Monitoring (SHM) systems. The proposed Modified Predictive Coding algorithm builds on predictive coding yet adds adaptive context modeling tools alongside edge-preserving processes to balance visual quality with high compression ratio fulfillment. This method surpasses traditional approaches by making prediction parameter modifications which happen automatically according to image local features in order to protect essential diagnostic information. Testing of the algorithm took place using a high-resolution image dataset containing metallic surface images which faced different stress scenarios. The proposed method underwent performance evaluations that applied Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Compression Ratio (CR) metrics for assessment. The experimental findings show that MPC algorithm achieves higher compression efficiency and maintains visual quality better than JPEG2000 and SPIHT methods do. Its fast processing abilities along with low latency system make MPC strongly compatible with deploying SHM systems utilizing edge devices and IoT methodologies. The research analyzes how the algorithm would function with sensor networks and cloud-based analytics systems for improving predictive maintenance decision capabilities. The Modified Predictive Coding algorithm proves to be a suitable technology for efficient high-quality image compression which enables prompt accurate assessment of metallic structures in critical infrastructure. This research initiative creates an opportunity to study advanced compression methods which unite machine learning with predictive coding for smart SHM system applications
An Interrogation of Dream and Disillusionment in F. Scott Fitzgerald’s The Great Gatsby
Get PDFThis essay aims to demonstrate that F. Scott Fitzgerald is interested in dream and the disillusionment that follows from it in his The Great Gatsby. The wealthy gangster, Jay Gatsby, the protagonist, longs for a beautiful, upper-class woman but eventually loses his heart. The tragedy of all dreamers is also exemplified by the story of the gangster, a youngster from the Midwest who is chasing the American dream of success. The rise and fall of a handsome bootlegger who became obsessed with the wealthy and attractive Daisy Buchanan was recounted in this novel. Whether the paradise lost is a Midwestern boyhood, Paris in the 1920s, or Gatsby’s platonic ideal of Daisy, it is the quintessential American story of social aspiration and the frequently terrible results of innocence betrayed. The novel explores American themes and presents a portrayal of the new social reality that the moralist tradition in America fears. The world that the story explores is one of strained relationships, one that prioritises wealth and success over societal duty, and one where people have much too much control over their own lives. In this novel, Fitzgerald has combined the American dream with the American disillusionment
Unifying Temporal Reasoning and Agentic Machine Learning: A Framework for Proactive Fault Detection in Dynamic, Data-Intensive Environments
Get PDFModern enterprises depend heavily on complex IT infrastructures which must be continually monitored for signs of internal failures. These systems generate vast amounts of time series data, but this data is rarely utilized for proactive detection of emerging anomalies and faults. Instead, alert systems are poorly tuned to high-volume, low-fidelity rules. We argue that time series data gives rise to unique challenges in fault detection, including temporal dependencies, agents working asymmetrically, synchronicity, and varying time granularity. However, traditional techniques in alarm tuning and anomaly detection do not succeed in modeling these properties. This is why we call for the unification of two established research fields—temporal logic and temporal pattern mining in temporal reasoning, and multiagent reinforcement learning in agentic ML. Many assertions and expectations must be placed on the agents and their learning interactions, and which of them evolve.
We outline requirements for a temporal agentic fault detection framework and our thesis that without an explanatory, accountable temporal model of entity interactions, proactive alerts will produce mostly false positives and have poor effects on complex nonstationary environments. We call for research collaboration to produce a toolkit drawing together the best of the temporal reasoning and agentic ML worlds. This toolkit for researchers and practitioners in complex systems will allow them to consider events and patterns from both horizons and then explore them with multicriteria time-based search and temporal multiarmed bandit functions. Output from these diverse functions can inform the final semantic entity relationship specification, as well as a variety of agentic objective functions that support the full temporal scale from immediate to multiweek backup.