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Praseodymium metal nanorods as a 5-fluorouracil carrier

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Journal of Rare Earths 2024年第7期42卷 1328-1336,I0005页

作者： Varnitha Manikantan govindaraj Sri Varalakshmi Ujma Ashapak Tamboli Archana Sumohan pillai Aleyamma Alexander Anushka Lucas Bose Allben Akash Israel V.M.V.Enoch Centre for Nanoscience and genomics Karunya Institute of Technology and Sciences(Deemed University)Coimbatore641114Tamil NaduIndia Department of Applied Chemistry Karunya Institute of Technology and Sciences(Deemed University)Coimbatore641114Tamil NaduIndia

Interest in metal nanoparticles as nanoscale drug-carrying vehicles has been *** such an aspect,lanthanide metal nanoparticles are particularly ***,we report praseodymium nanoparticles as a novel anticancer therapeutic transporting *** material was made as a composite with the supramolecular host polymer,poly-β-*** praseodymium nanorods were characterized using standard analytical techniques,including X-ray diffraction(XRD),X-ray photoelectron spectroscopy(XPS),and thermogravimetric analysis(TgA).In addition,they were imaged using transmission electron *** nanostructures are a long rod-shaped material with 2.0±0.1μm in length and 230±10 nm in *** material shows a saturation magnetization value above 30 emu/g with a coercivity value of 130 *** nanocarrier is freighted with the drug 5-fluorouracil,and the encapsulation efficiency is 76.09%.The drug release is continuous from the nanocarrier(polymer-containing praseodymium nanoparticles)and proceeds for above 250 *** in vitro cytotoxicities of the blank nanocarrier and the 5-fluorouracil-loaded one are deduced by testing them on MCF-7 and Vero(non-cancerous)cell *** cytotoxicity on the cancerous cell lines is observed,whereas the non-cancerous cells are *** IC_(50)values on the cell lines are reported.

关键词： Metal nanoparticles Praseodymium Poly-cyclodextrin Drug delivery Anticancer activity Rare earths

Laboratory evaluation of toxicity of selected insecticides against egg and larval stages of cotton pink bollworm, Pectinophora gossypiella (Saunders) (Lepidoptera: gelechiidae)

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Journal of Cotton Research 2024年第1期7卷 15-28页

作者： BUSNOOR Abhishek V. WADASKAR R.M. FAND Babasaheb B. TAMBE V.J. pillai T. MAHULE D.J. NAgRARE V.S. PRASAD Y.g. Entomology Section College of Agriculture(Dr.Panjabrao Deshmukh Krishi Vidyapeeth)Nagpur 440001MaharashtraIndia Indian Council of Agricultural Research-Central Institute for Cotton Research Nagpur 441108MaharashtraIndia

Background The cryptic nature of pink bollworm Pectinophora gossypiella(Saunders)larvae enables its reduced vul-nerability to insecticidal ***,the development of resistance against Bacillus thuringiensis(Bt)toxins posed a serious threat to transgenic cotton *** necessitated determining the critical timing of spray applications on the control *** study assessed the influence of egg age(freshly laid ***-day-old)and the loca-tion of larvae(directly exposed to the insecticide residues on the boll rind *** inside the bolls)on insecticide control *** The results revealed a significant decrease in the ovicidal activity of tested insecticides with an increase in the age of eggs from one day old to three days old(paired t-test,P80%mortality)than the larvae exposed after they had bur-rowed inside the bolls(gy were more pronounced in the experiment on pre-larval release insecticide treatment compared with insecticide treatment given post-larval release and entry inside the *** Egg age influences the insecticide susceptibility,as does the larval location,directly exposed vs bur-rowed inside the *** eggs and the larvae that had burrowed inside the green bolls of cotton were relatively less susceptible to the insecticide *** toxic effects of insecticides on egg and larval stages were primar-ily *** findings are significant for devising a comprehensive strategy for pink bollworm management on a sustainable basis.

关键词： Bioefficacy Cotton Insecticides Pink bollworm Pectinophora gossypiella Timings of spray

Fatigue reliability analysis of fixed offshore structures:A first passage problem approach

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第11期7卷 1839-1845页

作者： MADHAVAN pillai T.M. VEENA g. Department of Civil Engineering National Institute of Technology Calicut 673601 India

This paper describes a methodology for computation of reliability of members of fixed offshore platform structures, with respect to fatigue. Failure criteria were formulated using fracture mechanics principle. The problem is coined as a “first passage problem”. The method was illustrated through application to a typical plane frame structure. The fatigue reliability degradation curve established can be used for planning in-service inspection of offshore platforms. A very limited parametric study was carried out to obtain insight into the effect of important variables on the fatigue reliability.

关键词： Reliability Fatigue Fracture mechanics Offshore structures

Nonlinear model predictive control with relevance vector regression and particle swarm optimization

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控制理论与应用（英文版） 2013年第4期11卷 563-569页

作者： M.gERMIN NISHA g.N.pillai Department of Electrical Engineering Indian Institute of Technology Roorkee

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.

关键词： Relevance vector regression Least squares support vector machines Nonlinear model predictive control Particle swarm optimization with controllable random exploration velocity

Seed Extract of Psidium guajava as Ecofriendly Corrosion Inhibitor for Carbon Steel in Hydrochloric Acid Medium

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Journal of Materials Science & Technology 2011年第12期27卷 1143-1149页

作者： K.P.Vinod Kumar M. Sankara Narayana pillai g. Rexin Thusnavis Department of Chemistry University College of Engineering Nagercoil Anna University of TechnologyTirunelveli Nagercoil-629004 Tamil Nadu India Department of Chemistry Noorul Islam UniversityKumaracoil Nagercoil Tamilnadu India Department of Chemistry St. Xavier s Catholic College of EngineeringChunkankadai Nagercoil-629003 Tamil Nadu India

The anticorrosion characteristics of the seeds of Psidium guajava （P. guajava） fruits on carbon steel in acid medium were examined with weight loss data and subsequently thermodynamic factors such as heat of ad- sorption of the inhibitor on the metal surface （Q）, change in entropy （ΔS）, change in flee energy of the reaction （Δg）, corrosion rate （CR） and energy of activation for corrosion reaction of carbon steel （E） were also evaluated. Adsorption isotherm was plotted to study the adsorption of the inhibitor on the metal surface with increasing concentration of the inhibitor. The functional groups responsible for inhibition were investi- gated using Fourier transform infrared （FT-IR） spectra. Electrochemical parameters were evaluated through the potentiodynamic Tafel polarization and impedance spectral studies. Scanning electron microscopy （SEM） micrographs were recorded to investigate the change in surface morphology. The complete study reveals the efficiency of seed extract of P. guajava as a safe, ecofriendly and alternate corrosion inhibitor for carbon steel in acid medium.

关键词： Psidium guajava (P. guajava) Steel Corrosion Polarization Impedance Ecofriendly

Biodiversity and seasonal variation of benthic macrofauna in Minicoy Island, Lakshadweep,India

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Acta Oceanologica Sinica 2014年第10期33卷 58-73页

作者： Dalia Susan V Satheesh Kumar P. pillai N. g. K Central Marine Fisheries Research Institute Kochi Kerala 682018 India

From the Indian coast only limited data are available on the benthic fauna of the seagrass communities. In this study, seasonal variation in the distribution of macrobenthos and influence of environmental parameters was explored at four seagrass beds and two mangrove stations along the Minicoy Island, Lakshadweep, India, from September 1999 to August 2001. A total of 160 macrobenthic species from eight major groups represented the macrofauna of the Minicoy Island. Of the identified taxa, molluscs 70（gastropods 41.46%, bivalves 7.5%）, polychaetes 27（16.88%）, crustaceans 30（18.75%）, echinoderms 11（6.88%） and remaining others. Average seasonal abundance of benthic macrofauna ranged from 219 to 711 ind./m2, species diversity varied from 1.45 to 3.64 bits per individual, species richness index ranged from 4.01 to 26.17, evenness 0.69 to 1.66. In general, the higher abundance and species diversity was noticed in southern seagrass stations and northern seagrass stations, but in the mangrove stations comparatively low species diversity was observed. Three-way analysis of variance indicated that all communities resulted as being significantly different between seagrass and mangrove station, mainly when the seasonal interaction was considered. Multivariate analyses were employed to help define benthic characteristic and the relationship between environmental parameters at the six monitoring stations. Results of cluster analyses and multidimensional scale plot suggest that for mangrove region, different physiographic provinces, lower salinity, dissolved oxygen and sediment biotic structure have a higher influence on the species composition and diversity than other oceanographic conditions.

关键词： abundance seagrass macrobenthos mangrove Minicoy Island Lakshadweep

Drilling study on lightweight structural Mg/SiC composite for defence applications

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Defence Technology（防务技术） 2019年第4期15卷 557-564页

作者： K. Balamurugan M. Uthayakumar S. Thirumalai Kumaran g.S. Samy U.T.S. pillai Department of Mechanical Engineering VFSTR (Deemed to be University) Guntur 522213 India Faculty of Mechanical Engineering Kalasalingam University Krishnankoil 626126 Tamil Nadu India CSIR-NIIST Material Science and Technology Division Thiruvananthapuram 695019 India

Heat energy generated during machining has been found to have a greater influence on determining the machinability of the *** this work,magnesium-based silicon carbide composite,which has been identified as a suitable lightweight application material,is prepared with the weight ratio of 90:10 by a stir casting *** available HSS drill tools with different diameters of 4,6 and 8 mm are used to perform the drilling operations with governing parameters of spindle speed,feed rate and constant depth of *** image camera of the FLIR E60 series is used to measure the temperature variation in the cutting zone at different operating *** influences of machining temperature on chip morphology,tool wear and surface profile of the machined samples are *** speed has been found to have a significant effect on machining *** spindle speed increases,the diameter of drill tool increases the tool wear and surface profile,*** abrasion and adhesive type of wears are observed in the drill ***,change from abrasion to adhesive wear is noticed with the increase of the diameter of the drill *** plots are drawn with respect to the interaction of governing parameters along with the working temperatures obtained under different machining conditions.

关键词： Magnesium SiC Drilling Thermal effect Chip morphology Tool wear

A Review of graph Neural Networks and Their Applications in Power Systems

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Journal of Modern Power Systems and Clean Energy 2022年第2期10卷 345-360页

作者： Wenlong Liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna pillai Yuelong Wang Yusen Wang the Department of Energy Technology Aalborg UniversityAalborgDenmark the State grid Tianjin Chengxi Electric Power Supply Branch TianjinChina the School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

Deep neural networks have revolutionized many machine learning tasks in power systems,ranging from pattern recognition to signal *** data in these tasks are typically represented in Euclidean ***,there is an increasing number of applications in power systems,where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among *** complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean ***,many publications generalizing deep neural networks for graph-structured data in power systems have *** this paper,a comprehensive overview of graph neural networks(gNNs)in power systems is ***,several classical paradigms of gNN structures,e.g.,graph convolutional networks,are *** applications in power systems such as fault scenario application,time-series prediction,power flow calculation,and data generation are reviewed in ***,main issues and some research trends about the applications of gNNs in power systems are discussed.

关键词： Machine learning power system deep neural network graph neural network artificial intelligence

Scenario generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1563-1575页

作者： Wenlong Liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna pillai Zhe Yang Yusen Wang Kuangpu Liu AAU Energy Aalborg UniversityAalborgDenmark School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy *** paper proposes a deep generative network based method to model time-series curves,e.g.,power generation curves and load curves,of renewable energy sources and loads based on implicit maximum likelihood estimations(IMLEs),which can generate realistic scenarios with similar patterns as real *** training the model,any number of new scenarios can be obtained by simply inputting gaussian noises into the data generator of *** proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process,which leads to stronger applicability than explicit density model based *** extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and ***,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

关键词： Renewable energy source scenario generation implicit maximum likelihood estimation(IMLE) deep learning generative network