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Formation of microstructural features in hot-dip aluminized AISI 321 stainless steel

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International Journal of Minerals,Metallurgy and Materials 2018年第2期25卷 190-198页

作者： Prashant huilgol k.Rajendra Udupa k.Udaya Bhat Department of Metallurgical & Materials Engineering National Institute of Technology Karnataka

Hot-dip aluminizing（HDA） is a proven surface coating technique for improving the oxidation and corrosion resistance of ferrous substrates. Although extensive studies on the HDA of plain carbon steels have been reported, studies on the HDA of stainless steels are limited. Because of the technological importance of stainless steels in high-temperature applications, studies of their microstructural development during HDA are needed. In the present investigation, the HDA of AISI 321 stainless steel was carried out in a pure Al bath. The microstructural features of the coating were studied using scanning electron microscopy and transmission electron microscopy. These studies revealed that the coating consists of two regions： an Al top coat and an aluminide layer at the interface between the steel and Al. The Al top coat was found to consist of intermetallic phases such as Al_7Cr and Al_3Fe dispersed in an Al matrix. Twinning was observed in both the Al_7Cr and the Al_3Fe phases. Furthermore, the aluminide layer comprised a mixture of nanocrystalline Fe_2Al_5, Al_7Cr, and Al. Details of the microstructural features are presented, and their formation mechanisms are discussed.

关键词： hot-dip aluminizing aluminide layer intermetallic phases microstructural features stainless steel

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

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Structural Durability & Health Monitoring 2021年第4期15卷 317-334页

作者： S.kalyani k.Venkata Rao A.Mary Sowjanya Department of Computer Science&Systems Engineering Andhra University College of Engineering(A)VisakhapatnamIndia

Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under *** data of all possible states of a system are used for building machine learning *** models are further used to predict the possible downtime for proactive action on the system *** engine data from run to failure is used in the current *** run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final *** the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous *** methodology is based on different sampling sizes to obtain optimum results with great *** time series of each sensor is converted to a 2D image with a specific time *** Images would represent the health of a system in higher-dimensional *** created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed *** these created images,a model for estimating the remaining life of the aircraft is ***,the proposed net is also used for predicting the number of engines that would fail in the given time *** current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial *** accuracy in the classification of components is achieved using the TimeImagenet-based approach.

关键词： Multivariate sensor data TimeImageNet Remaining life estimation machine learning 2D image Convolutional Neural Network

Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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Computers, Materials & Continua 2023年第4期75卷 149-164页

作者： k.kalyani Sara A Althubiti Mohammed Altaf Ahmed ELaxmi Lydia Seifedine kadry Neunggyu Han Yunyoung Nam Department of Computer Science Dr.Nalli Kuppusamy Arts College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613003India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Lepartment of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. Computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is *** addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

关键词： Skin cancer deep learning melanoma classification dermoscopy computer aided diagnosis

Energy Efficient Clustering Protocol to Enhance Network Lifetime in Wireless Sensor Networks

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Computers, Materials & Continua 2021年第9期68卷 3595-3614页

作者： S.Nanthini S.Nithya kalyani Sudhakar Sengan Department of Computer Science and Engineering Sri Ramakrishna College of EngineeringPerambalur621113India Department of Information Technology K.S.R College of EngineeringTiruchengode637215India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152India

In this paper,the energy conservation in the ununiform clustered network field is *** fundamental reason behind the methodology is that in the process of CH election,nodes Competition Radius(CR)task is based on not just the space between nodes and their Residual Energy(RE),which is utilized in Energy-Aware Distributed Unequal Clustering(EADUC)protocol but also a third-degree factor,i.e.,the nearby multi-hop node *** contrast,a third-factor nearby nodes count is also *** surrounding data is taken into account in the clustering feature to increase the network’s life *** proposed method,known as Energy Conscious Scattered Asymmetric Clustering(ECSAC),self-controls the nodes’energy utilization for equal allotment and un-equal ***,extra attention is agreed to energy consumption in the communication process by applying a timeslot-based backtracking algorithm for increasing the network’s *** proposed methodology reduces the clustering overhead and node communication energy consumption to extend the network’s *** suggested method’s results are investigated against the classical techniques using the lifetime of the network,RE,alive hop count and energy consumption during transmission as the performance metric.

关键词： Wireless sensor networks energy conservation lifetime enhancement clustering cluster head

Biodegradation of Crystal Violet by Agrobacterium radiobacter

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Journal of Environmental Sciences 2011年第8期23卷 1384-1393页

作者： G.k.Parshetti S.G.Parshetti A.A.Telke D.C.kalyani R.A.Doong S.P.Govindwar Department of Biomedical Engineering and Environmental Sciences National Tsinghua University Division of Applied Life Sciences Gyeongsang National University Division of Chemical Engineering Konkuk University Department of Biochemistry Shivaji University

Agrobacterium radiobacter MTCC 8161 completely decolorized the Crystal Violet with 8 hr （10 mg/L） at static anoxic conditions. The decreased decolorization capability by A. radiobacter was observed, when the Crystal Violet concentration was increased from 10 to 100 mg/L. Semi-synthetic medium containing 1% yeast extract and 0.1% NH4C1 has shown 100% decolorization of Crystal Violet within 5 hr. A complete degradation of Crystal Violet by A. radiobacter was observed up to 7 cycles of repeated addition （10 rag/L）. When the effect of increasing inoculum concentration on decolorization of Crystal Violet （100 mg/L） was studied, maximum decolorization was observed with 15% inoculum concentration. A significant increase in the activities of laccase （184%） and aminopyrine N-demethylase （300%） in cells obtained after decolorization indicated the involvement of these enzymes in decolorization process. The intermediates formed during the degradation of Crystal Violet were analyzed by gas chromatography and mass spectroscopy （GC/MS）. It was detected the presence of N,N,N＇,N＂-tetramethylpararosarfiline, IN, N-dimethylaminophenyl] [N-methylaminophenyl] benzophenone, N, N-dimethylaminobenzaldehyde, 4-methyl amino phenol and phenol. We proposed the hypothetical metabolic pathway of Crystal Violet biodegradation by A. radiobacter. Phytotoxicity and microbial toxicity study showed that Crystal Violet biodegradation metabolites were less toxic to bacteria （A. radiobacter, P. aurugenosa and A. vinelandii） contributing to soil fertility and for four kinds of plants （Sorghum bicolor, Vigna radiata, Lens culinaris and Triticum aestivum） which are most sensitive, fast growing and commonly used in Indian agriculture.

关键词： Agrobacterium radiobacter Crystal Violet biodegradation gas chromatography and mass spectroscopy toxicity study

Dimethylammonium iodide stabilized bismuth halide perovskite photocatalyst for hydrogen evolution

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Nano Research 2021年第4期14卷 1116-1125页

作者： He Zhao kalyani Chordiya Petri Leukkunen Alexey Popov Mousumi Upadhyay kahaly krisztian kordas Satu Ojala Environmental and Chemical Engineering Research Unit University of OuluP.O.Box 4300FI-90014 OuluFinland ELI-ALPS ELI-HU Non-Profit Ltd.Dugonicster 136720 SzegedHungary Institute of Physics University of Szegedf D6m ter 9H-6720 Szeged Hungary Nano and Molecular Systems Research Unit University of OuluP.O.Box 3000FI-90014 OuluFinland Optoelectronics and Measurement Techniques Unit University of OuluFI-90570 OuluFinland Microelectronics Research Unit University of OuluP.O.Box 4500FI-90014 OuluFinland VTT Technical Research Centre of Finland FI-90590 OuluFinland

Metal halide perovskites have emerged as novel and promising photocatalysts for hydrogen ***,their stability in water is a vital and urgent research *** this paper a novel approach to stabilize a bismuth halide perovskite[(CH_(3))_(2)NH_(2)]_(3)[Bil_(6)](DA_(3)Bil_(6))in water using dimethylammonium iodide(DAI)without the assistance of acids or coatings is *** DA3Bil6 powder exhibits good stability in DAI solutions for at least two *** concentration of DAI is found as a critical parameter,where the I^(-)ions play the key role in the *** stability of DA3Bil6 in water is realized via a surface dissolution-recrystallization *** DA3Bil6 demonstrates constant photocatalytic properties for visible light-induced photo-oxidation of I^(-)ions and with PtCI4 as a co-catalyst(Pt-DA_(3)Bil_(6)),photocatalytic H2 evolution with a rate of 5.7μmol·h^(-1)from HI in DAI solution,obtaining an apparent quantum efficiency of 0.83%at 535 *** study provides new insights on the stabilization of metal halide perovskites for photocatalysis in aqueous solution.

关键词： bismuth halide perovskite dimethylammonium iodide photocatalysis hydrogen evolution

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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Computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： k.kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

A Novel Integrated Machine&Business Intelligence Framework for Sensor Data Analysis

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Journal on Internet of Things 2021年第1期3卷 27-38页

作者： S.kalyani A.Mary Sowjanya k.Venkat Rao Vignan’s Institute of Engineering for Women Andhra UniversityVisakhapatnamIndia

Increased smart devices in various industries are creating numerous sensors in each of the equipment prompting the need for methods and models for sensor *** research proposes a systematic approach to analyze the data generated from sensors attached to industrial *** methodology involves data cleaning,preprocessing,basics statistics,outlier,and anomaly *** study presents the prediction of RUL by using various Machine Learning models like Regression,Polynomial Regression,Random Forest,Decision Tree,XG *** Parameter Optimization is performed to find the optimal parameters for each *** each of the model for RUL prediction RMSE,MAE are *** of the RUL prediction should be useful for decision maker to drive the business decision;hence Binary cclassification is performed,and business case analysis is *** case analysis includes the cost of maintenance and cost of non-maintaining a particular *** research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.

关键词： RUL prediction sensors IoT aircraft engine business intelligence

Water use, growth and yield of green gram under rainfed upland crop sequences in Gangetic Plains of Indian sub-tropics

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Journal of Geographical Sciences 2002年第3期12卷 301-304页

作者： A. Zaman, S.k. Choudhuri( Regional Research Station, Old Alluvial Zone, Majhian, kalyani-741 235, West Bengal, India) Regional Research Station Majhian Kalyani 741 235 West Bengal India

A field experiment was conducted to study the effect of different crop sequences on water use, growth and yield of green gram (Vigna radiate (L) Wilezek) during 1992–93 and 1993–94 under rainfed upland condition in Indo-Gangetic plains of West Bengal. Green gram sown in the month of March as pre-rainy (summer) season crop, as and when the winter crops vacated the land, produced highest dry matter of 372 gm-2 which was significantly highest in black gram-yellow sarson sequence. The results of the field experimentation revealed that green gram gave highest grain yield to the extent of 10.80 q/ha when sown after black gram (rainy season) followed by yellow sarson (winter season) while the crop produced 10.63 q/ha under sesame-yellow sarson sequence. Highest water use of 267 mm was achieved in green gram under black gram-yellow sarson sequence and the crop gave water use efficiency of 4.07 kg ha-1mm-1 under black gram-yellow sarson sequences.

关键词： green gram water use crop sequences Gangetic Plains (India)