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Performance and Steady State Heat Transfer Analysis of Functionally Graded Thermal Barrier Coatings Systems
Thermal barrier coatings (TBCfs), typically 8 wt.% Yttria Stabilized Zirconia (8YSZ), in single layered configuration have been traditionally used in aerospace components to protect them from degradation at high temperatures and to improve the thermal efficiency of the system. This paper compares the performance of two types of TBC configurations: Single layered and multilayered functionally graded materials (FGM). Aerospace alloy, Inconel 718 substrates, NiCrAlY bond coat (BC) and 8YPSZ top coat (TC) were the materials used. FGM configuration was used to improve the durability and life of the conventional TBC system by reducing the coefficient of thermal expansion (CTE) mismatch. The TBCs were subjected to thermal fatigue (thermal shock and thermal barrier test) in laboratory scale burner rig test and oxidation stability test in high temperature furnace upto 1000. The as-sprayed and thermal fatigue tested specimen were characterized by X-ray diffraction (XRD) analysis and Scanning Electron Microscope (micro-structure). Results are discussed in the light of suitability of coating configuration, thermal fatigue and spalling characteristics with reference to aerospace applications at temperatures in the 9000C to 15000C range. Computational work was carried out comprising a simulation model involving the developed TBCs. 2018 Elsevier Ltd. -
Influence of atmospheric plasma spray process parameters on crystal and micro structures of pyrochlore phase in rare earth zirconate thermal barrier coatings
Yttria-stabilized zirconia (YSZ) thermal barrier coatings is most widely used in gas turbine engines applications and its primary role is to protect the underlying base metal from degradation at its high temperature (>1000 C) service environment. While YSZ serves well in this role, materials with higher thermal stability and lower thermal conductivities are required to be developed for attaining higher operating temperatures and thereby higher energy conversion efficiencies. A number of rare-earth zirconates which form the cubic fluorite-derived pyrochlore structures (A2B2O7) where A: La, Gd, Sm, Ce and B: Zr are being developed, some compositions are more attractive due to their good amalgamation of thermal and mechanical properties. However, when these materials are plasma spray coated on metal substrates, the favorable properties are not immediately realized due to various contributing factors such as poor adhesion/cohesion, microstructure (porosity, defects) or even incomplete stabilization or destabilization of the desired phase (crystal structure) after passing through the plasma. In this paper, plasma sprayable powders of zirconate pyrochlores (or with disordered fluorite structures) synthesized from using La and Ce as the trivalent ''A cation, were plasma sprayed onto Inconel 718 substrates, by using different plasma spray parameters. The considerable influence of these spray parameters on the structural phases (analyzed via XRD) and microstructures (studied via SEM on polished cross section metallographs) are presented in detail. 2019 Elsevier Ltd. All rights reserved. -
Zirconia based pyrochlore thermal barrier coatings
Improvements in thermal barrier coatings (TBCs) technology, further than what is already in service to enable adequate protection to metallic components from higher (>1100C) operating temperatures requires newer developments in materials. Many research activities have been undertaken by scientists to seek alternatives after discovering the threshold of Yttria-stabilized zirconia (YSZ) TBCs on standard aero-space materials at elevated temperatures. To increase the thermal performance of gas turbine engines, alternate TBC materials with better sintering resistance and lower thermal conductivity are required. One of the promising candidates for the TBCs is Pyrochlore-type rare earth zirconium oxides (Re2Zr2O7, Re = rare earth). Re2Zr2O7 TBCs have higher phase stability, lower thermal conductivity, lower sintering rate, no phase transformation, and lower coefficient of thermal expansion at elevated temperatures when compared with YSZ. In this work, plasma spray powders of Lanthanum Zirconate (La2Zr2O7) and Lanthanum Ceria Zirconate (La2 (Zr0.7Ce0.3)2O7) were synthesized by the solid-state reaction method with the goal to develop pyrochlore oxide-based coatings with desired properties at high temperatures (>1200C), better than the YSZ TBCs: Currently the most popular choice for TBCs. These TBCs are expected to increase gas turbine efficiencies while protecting the underlying metallic substrate at high operation temperatures. The evaluation of the synthesised TBCs has been carrying out by studying their performances at 1200C. Results of evaluation for phase composition by employing X-Ray Diffractometry (XRD), microstructure via Scanning electron Microscope (SEM) and chemical composition via Energy Dispersive spectroscopy (EDS) also have been included. Published under licence by IOP Publishing Ltd. -
Adhesion strength studies on zirconia based pyrochlore and functionally gradient thermal barrier coatings
Thermal Barrier Coating (TBC) plays a major role in the improvement of gas turbine and engine components in terms of their service life and performance. Generally, all coatings must possess certain primary properties to perform in the intended applications. However, regardless of applications, suitable adhesion strength is one major characteristic they must have to adequately protect the basic components on which they are applied upon. In TBCs, adhesion (or Bond) strength is a parameter that helps to illustrate the resistance of the ceramic top coat against spallation either from the bond coat (and component) or within the TBC layers itself. The performance of TBCs are reliant upon the adhesion between the coating and the metal substrate and also adhesion (or cohesion) between the bond coat and the overlying ceramic top coat layer. The de-bonding of the top coat layer or the inter-metallic bond coat layers are the main reasons of the failure of the overall TBC system. Some of the prominent problems associated with coatings applications are residual stresses, micro-cracks and pores etc. These and many other factors influence the adhesion of the coatings in addition to service environment conditions and pre coating substrate preparations such as substrate cleaning, grit blasting and very importantly plasma spray parameters. In the present work, results obtained from adhesion strength measurements carried out by following the ASTM C 633 standard test method, on various types of TBCs are being shared. Thermal barrier coatings (TBCs) were synthesized with NiCrAlY bond coat deposited on SS 304L substrate by using air plasma spray and different ceramic top coats (a) commercial 8%Yttria Stabilized Zirconia (8YSZ) (b) lab synthesized plasma spray powders of (i) Lanthanum Zirconate (La2Zr2O7) (ii) Lanthanum Ceria Zirconate (La2 (Zr0.7Ce0.3)2O7) and (iii) Lanthanum cerate (La2Ce2O7). The coating depositions were carried out in different configurations i.e. two layers, three layers and gradient layers (Functionally gradient materials). The evaluation of properties includes the studies of morphology of the strength (adhesive/cohesive failure mode) tested specimen as well. General conclusions drawn from the studies on several specimen in various configurations are that cohesive failures (between the ceramic top coat layers) is the predominant mechanisms followed by few adhesive failures in bond coat coat/ceramic interface. 2019 Elsevier Ltd. All rights reserved. -
Comparative analysis of Histogram Equalization techniques
Histogram Equalization (HE) is one of the techniques which is used for Image enhancement. This paper shows the comparative studies of Global Histogram Equalization, Local Histogram Equalization and Fast Quadratic Dynamic Histogram Equalization based on the execution time, mean squared error and Peak Signal to Noise Ratio (PSNR). This paper shows the experimental results for these three methods with graphical representation. 2014 IEEE. -
Effects of Macro Economic Indicators on Foreign Portfolio Investments
In this study, both institutional and retail investors were observed making exits and entries based on macroeconomic data, utilizing measurable indicators such as GDP, inflation, bank rates, foreign exchange rates, trade volume on the national stock exchange, and portfolio investments. Employing a Vector Error Correction Model (VECM) in an econometric analysis, the study found a significant association between macroeconomic indicators and portfolio investments in India. Investors followed a discernible pattern of entering and exiting markets, with economic growth fostering greater investments. Notably, GDP, NSE Volume, and bank rates were identified as variables impacting foreign portfolio investments. In the long run, GDP positively affected foreign portfolio investments, while inflation and foreign exchange rates exhibited a detrimental influence, leading to decreased portfolio investments. Foreign Institutional Investors, prioritizing profits over business operations, focused on market sentiments, directing investments towards economies with potential performance and resulting in a higher volume of capital inflow. Overall, the study concludes that a robust economic condition attracts superior foreign portfolio investments. 2024 IEEE. -
A Study of Segmentation Techniques to Detect Leukaemia in Microscopic Blood Smear Images
In medical image processing, the segmentation of the image is considered to be a vital stage and is effectively used to extract the region of interest. Automated diagnosis of leukaemia is highly associated with the accurate segmentation of the cell nucleus. The purpose of this paper is to review and analyze literature related to some of the major segmentation techniques used in the field of Acute lymphoblastic leukaemia (ALL) detection. This paper presents an overview of segmentation methods along with the experimental results of six implemented methods and highlights some of the advantages and disadvantages of implemented segmentation techniques. 2020 IEEE. -
Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images
Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. 2024 IEEE. -
Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images
Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. This paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. This prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action. 2023 IEEE. -
A Study of Preprocessing Techniques on Digital Microscopic Blood Smear Images to Detect Leukemia
Digital microscopic blood smear images can get distorted due to the noise as a result of excessive staining during slide preparation or external factors during the acquisition of images. Noise in the image can affect the output of further steps in image processing and can have an impact on the accuracy of results. Hence, it is always better to denoise the image before feeding it to the automatic diagnostic system. There are many noise reduction filters available; the selection of the best filter is also very important. This paper presents a comparative study of some common spatial filters like wiener filter, bilateral filter, Gaussian filter, median filter and mean filter which are efficient in noise reduction, along with their summary and experimental results. Performing comparative analysis of result based on PSNR, SNR and MSE values, it can be determined that median filter is most suitable method for denoising digital blood smear images. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Unified Approach to Predict and Understand Acute Myeloid Leukemia Diagnosis
Acute myeloid leukemia (AML) is a rapidly progressing disease that affects myeloid cells in blood and bone marrow. These abnormal cancerous cells called blast cells are non-functional cells that increase rapidly in bone marrow and are released into blood stream which crowd out the healthy functional cells leading to weak immune system. This life-threatening disease needs to be diagnosed at early stage and hence requires fully automated system for early detection of leukemia to aid pathologists and doctors. Most of the automated machine learning and AI models are not transparent and require techniques to explain model prediction. This paper presents methods to classify blood microscopic images into healthy or acute myeloid leukemia. Among all the methods implemented, Gradient Boosting outperforms with an accuracy of 96.67%. This paper also focuses on explainable AI to interpret model prediction and feature importance which further helps in understanding decision-making process of classification model and optimize it. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Investigation on AI-Based Techniques in Applications for Detecting Fatal Traffic Accidents
The difficulties with road accident rates today rank among the top concerns for health and social policy in nations across the continents. In this essay, we've spoken about the fatalities and injuries brought on by traffic accidents in several Indian states. We have also shed light on the various factors that contribute to traffic accidents. Many researchers have reported various methods for identifying automobile crashes or accidents that are discussed in this work. Additionally, we covered collision avoidance systems and their various kinds. An examination of the analysis techniques used to comprehend the numerous causes causing accidents is also included in the study. Traditional models are frequently used to identify problems such driver weariness, drowsiness, driving while intoxicated, and distractions. 2023 IEEE. -
Concept Drift Detection for Social Media: A Survey
The research over information retrieval from social media data has progressed for streaming data since the last decade. Recently, academic researchers have witnessed users' changing topics, trends, and intent on social media. This change of information with time takes into account the temporal attribute for real-time data, and thus, advances in this domain are exponentially growing. Although concept drift is still not explored due to a shortage of available datasets, concept drift for social media is minimally explored. This manuscript makes attempts to identify the types of concept drift for social media data, discuss the historical perspective of concept drift on social media, and enlist the possible research directions. 2021 IEEE. -
Potato Leaf Disease Identification using Hybrid Deep Learning Model
The potato is one of the most significant crops in the world. However, it is prone to several leaf diseases that can result in significant productivity losses, leading to economic challenges. Early and precise disease identification is essential for sensitive crops like potatoes. Deep learning approaches have demonstrated excellent potential in image-based disease classification tasks in recent years. This paper presents a hybrid strategy for classifying potato leaf image diseases by integrating Optimised Convolutional Neural Network (OCNN) and Long Short-Term Memory (LSTM) networks. The Adaptive Shark Smell Optimisation (ASSO) technique is used to optimize the weights of CNN models. The CNN component is initially used to extract pertinent characteristics from Potato leaves, capturing significant visual patterns related to various diseases. These extracted features are then fed into the LSTM model, which utilizes its sequential learning capability to model the temporal dependencies among the extracted features. The model performance is analyzed based on Accuracy, Precision, Recall, and F1-score criteria. Experimental results showed that the hybrid OCNN-LSTM model outperforms the individual CNN, LSTM, and MobileNet models. The proposed model results are compared with existing state-of-the-art work, and it was found that the OCNN-LSTM model performed better and received 99.02% accuracy. 2023 IEEE. -
A Comprehensive Review onCrop Disease Prediction Based onMachine Learning andDeep Learning Techniques
Leaf diseases cause direct crop losses in agriculture, and farmers cannot detect the disease early. If the diseases are not detected early and correctly, the farmer must undergo huge losses. It may lead to the wrong pesticide or over pesticide, directly affect crop productivity and economy, and indirectly affect human health. Sensitive crops have various leaf diseases, and early prediction of these diseases remains challenging. This paper reviews several machine learning (ML) and deep learning (DL) methods used for different crop disease segmentation and classification. In the last few years, computer vision and DL techniques have made tremendous progress in object detection and image classification. The study summaries the available research on different diseases on various crops based on machine learning (ML) and deep learning (DL) techniques. It also discusses the data sets used for research and the accuracy and performance of existing methods. It does mean that the methods and available data sets presented in this paper are not projected to replace published solutions for crop disease identification, perhaps to enhance them by finding the possible gaps. Seventy-five articles are analysed and reviewed to find essential issues that involve additional study for future research in this domain to promote continuous progress for data sets, methods, and techniques. It mainly focuses on image segmentation and classification techniques used to solve agricultural problems. Finally, this paper provides future research scope and challenges, limitations, and research gaps. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Hybrid Deep Learning-Based Potato andTomato Leaf Disease Classification
Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Smart Attendance Management System using IoT
Taking student attendance is mandatory in an educational organization, and maintaining those attendance plays a vital role. The conventional way of taking student attendance in any institution is time-consuming and challenging, because in the conventional procedure taking attendance/Roll call is performed manually by calling student names as per their roll numbers and marking 'absent(A)' or 'present(P)' on the attendance/logbook accordingly in every class per day. To improve teaching efficiency/teaching time in classrooms by reducing the time required for Roll call's, we have proposed a biometric student attendance system based on IoT. The proposed system records students' attendance using the facial-based biometric system and stores the attendance details on the server through the internet. In this system, the Raspberry pi camera captures the student face images and compares them with the stored images in the database. If the captured image is comparable with the stored image, then the student's attendance is recorded on the remote server as a present(P) in class; otherwise, attendance is recorded as absent (A). The developed system has been tested for sample classes, and the results proved that the system is simple, cost-effective, and portable for managing students' attendance. 2022 IEEE. -
Effect of phonon-substrate scattering on lattice thermal conductivity of monolayer MoS2
The effect of phonon-substrate scattering on lattice thermal conductivity (LTC) of supported MoS2 MLs is investigated over a wide temperature range (1 -
An improved AI-driven Data Analytics model for Modern Healthcare Environment
AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years. 2024 IEEE. -
Customer Segmentation and Future Purchase Prediction using RFM measures
Winning in the E-Commerce business race at a competitive age like this requires proper usage of Customer data. Using that database and grouping it in similar segments in terms of spending expenditure, observation time, sex, and location so that every customer falls in a segment of characteristics. This mechanism is called Customer Segmentation. In the modern era of highly compatible technological advancements, Machine Learning Algorithms are being vastly used to bring solutions to these difficult yet essential services. In the field of research methods like simple clustering based on purchase behaviour, buyer targeting or automated customer promotion mechanism by dividing into two major categories, have been worked on. However, ensemble algorithms have come handy where different clustering algorithms are combined to deliver best segmentation. Lately combination techniques like clustering and classification mechanism have also delivered good results where, not only segmentation is done but also classification of existing and new customers are possible into the clusters. Depending on that an effective customer relationship management can really benefit the company to a huge extent. Unlike other studies where clustering was performed directly on RFM table, a different approach was taken in this study where, one dimensional clustering was done individually on Recency, Frequency, Monetary columns, then an overall score was calculated and customers were classified into three segments. However, for a new customer depending on his purchase behaviour he/she also can be classified into any of the categories. 2022 IEEE.