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Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

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Computers, Materials & Continua 2022年第4期71卷 1567-1580页

作者： E.Ahila Devi K.C.ramya K.Sathesh Kumar Sultan Ahmad Seifedine Kadry Hyung Ju Park Byeong-Gwon Kang Department of Instrumentation and Control Engineering St.Joseph’s College of EngineeringChennai600119India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and *** nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery *** planning in MANET is a multi-hop communication process due to the restricted transmission range of the *** aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy *** few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design *** this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for *** EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR *** fitness value of the created solutions is determined next to pass the solutions to the objective *** MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy *** ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take *** resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared *** resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.

关键词： MANET routing lar protocol energy efficiency mrfo algorithm metaheuristics

New Results on One Modulo N-Difference Mean Graphs

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Open Journal of Discrete Mathematics 2023年第4期13卷 100-112页

作者： Pon Jeyanthi Meganathan Selvi Damodaran ramya Research Centre Department of Mathematics Govindammal Aditanar College for Women Tiruchendur Tamilnadu India Department of Mathematics Dr. Sivanthi Aditanar College of Engineering Tiruchendur Tamilnadu India Department of Mathematics Government Arts College (Autonomous) Salem-7 Tamilnadu India

A graph G is said to be one modulo N-difference mean graph if there is an injective function f from the vertex set of G to the set , where N is the natural number and q is the number of edges of G and f induces a bijection from the edge set of G to given by and the function f is called a one modulo N-difference mean labeling of G. In this paper, we show that the graphs such as arbitrary union of paths, , ladder, slanting ladder, diamond snake, quadrilateral snake, alternately quadrilateral snake, , , , , friendship graph and admit one modulo N-difference mean labeling.

关键词： Skolem Difference Mean Labeling One Modulo N-Graceful Labeling One Modulo N-Difference Mean Labeling and One Modulo N-Difference Mean Graph

Application of extracellular vesicles from mesenchymal stem cells promotes hair growth by regulating human dermal cells and follicles

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World Journal of Stem Cells 2022年第7期14卷 527-538页

作者： ramya Lakshmi Rajendran Prakash Gangadaran Mi Hee Kwack Ji Min Oh Chae Moon Hong Young KwanSung Jaetae Lee Byeong-Cheol Ahn Department of Nuclear Medicine School of MedicineKyungpook National UniversityDaegu 41944South Korea BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents Department of Biomedical SciencesSchool of MedicineKyungpook National UniversityDaegu 41944South Korea Department of Immunology School of MedicineKyungpook National UniversityDaegu 41944South Korea Department of Nuclear Medicine Kyungpook National University HospitalDaegu 41944South Korea

BACKGROUND Dermal papillae(DP)and outer root sheath(ORS)cells play important roles in hair growth and regeneration by regulating the activity of hair follicle(HF)*** To investigate the effects of human mesenchymal stem cell-derived extracellular vesicles(hMSC-EVs)on DP and ORS cells as well as *** are known to regulate various cellular ***,the effects of hMSC-EVs on hair growth,particularly on human-derived HF cells(DP and ORS cells),and the possible mechanisms underlying these effects are *** hMSC-EVs were isolated and characterized using transmission electron microscopy,nanoparticle tracking analysis,western blotting,and flow *** activation of DP and ORS cells was analyzed using cellular proliferation,migration,western blotting,and real-time polymerase chain *** growth was evaluated ex vivo using human *** Wnt3a is present in a class of hMSC-EVs and associated with the EV ***-EVs promote the proliferation of DP and ORS ***,they translocateβ-catenin into the nucleus of DP cells by increasing the expression ofβ-catenin target transcription factors(Axin2,EP2 and LEF1)in DP *** with hMSC-EVs also promoted the migration of ORS cells and enhanced the expression of keratin(K)differentiation markers(K6,K16,K17,and K75)in ORS ***,treatment with hMSC-EVs increases hair shaft elongation in cultured human *** These findings suggest that hMSC-EVs are potential candidates for further preclinical and clinical studies on hair loss treatment.

关键词： Mesenchymal stem cells Extracellular vesicles Hair growth Dermal papillae Outer root sheath cells

An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

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Computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

Studies on the Growth and Characterization of L-Arginine Maleate Dihydrate Crystal Grown from Liquid Diffusion Technique

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Journal of Minerals and Materials Characterization and Engineering 2016年第2期4卷 143-153页

作者： K. ramya C. Ramachandra Raja Department of Physics TUK Arts College Thanjavur India Department of Physics Government Arts College (Autonomous) Kumbakonam India

Nonlinear optical crystals of L-Arginine maleate dihydrate were grown from liquid diffusion method. The lattice parameters of the crystal were identified using single crystal and powder crystal X-ray diffraction analyses. Fourier transform infrared spectroscopy and Fourier transform Raman spectroscopy were made to study the vibrational functional groups in the grown crystal. Optical absorption and transmission ranges were measured from UV-VIS-NIR spectrum. The molecular structure of the crystal is established through 1H-NMR and 13C-NMR studies. Thermal stabilities and decomposition of the grown crystal were studied from TG/DTA and DSC analyses. Nonlinear optical property of the crystal was determined by Kurtz and Perry powder technique.

关键词： Amino Acids Liquid Diffusion Method NMR Spectral Studies Nonlinear Optical Material

Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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Computer Systems Science & Engineering 2023年第10期47卷 919-935页

作者： ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

Additive manufacturing of biodegradable porous orthopaedic screw

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Bioactive Materials 2020年第3期5卷 458-467页

作者： ramya Dhandapani Priya Dharshini Krishnan Allen Zennifer Vishal Kannan Amrutha Manigandan Michael R.Arul Devina Jaiswal Anuradha Subramanian Sangamesh Gurappa Kumbar Swaminathan Sethuraman Centre for Nanotechnology&Advanced Biomaterials SASTRA Deemed UniversityThanjavur613401India Department of Orthopaedics UConn HealthFarmingtonCT06030USA Department of Biomedical Engineering Western New England UniversitySpringfieldMA01119USA

Advent of additive manufacturing in biomedical field has nurtured fabrication of complex,customizable and reproducible orthopaedic ***-by-layer deposition of biodegradable polymer employed in development of porous orthopaedic screws promises gradual dissolution and complete metabolic resorption thereby overcoming the limitations of conventional metallic *** the present study,screws with different pore sizes(916×918μm to 254×146μm)were 3D printed at 200μm layer height by varying printing parameters such as print speed,fill density and travel speed to augment the bone ***-CT analysis and scanning electron micrographs of screws with 45%fill density confirmed porous interconnections(40.1%)and optimal pore size(259×207×200μm)without compromising the mechanical strength(24.58±1.36 MPa).Due to the open pore structure,the 3D printed screws showed increased weight gain due to the deposition of calcium when incubated in simulated body ***-like cells attached on screw and infiltrated into the pores over 14 days of in vitro ***,the screws also supported greater human mesenchymal stem cell adhesion,proliferation and mineralized matrix synthesis over a period of 21 days in vitro culture as compared to non-porous *** porous screws showed significantly increased vascularization in a rat subcutaneous implantation as compared to control *** screws produced by additive manufacturing may promote better osteointegration due to enhanced mineralization and vascularization.

关键词： 3D printing Orthopaedic screws Biodegradable Porous screws

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使用双变压器配置时宽带ADC前端设计

电子技术应用 2006年第10期32卷 143-144页

作者： Rob Reeder ramya Ramchandran 单位不详

变压器用于信号隔离，并且将单端信号转换成差分信号。当在高速模数转换器(ADC)前端电路中使用变压器时常常忽略的一个问题是变压器绝非理想器件。任何由变压器引起的输入失衡都会使输入的正弦信号变成非理想的正弦信号波形传送给ADC的... 详细信息

变压器用于信号隔离，并且将单端信号转换成差分信号。当在高速模数转换器(ADC)前端电路中使用变压器时常常忽略的一个问题是变压器绝非理想器件。任何由变压器引起的输入失衡都会使输入的正弦信号变成非理想的正弦信号波形传送给ADC的输入端，从而导致ADC的总体性能不如其它方式耦合到ADC的性能。

关键词：双变压器 ADC 前端设计高速模数转换器宽带配置正弦信号输入端

Heterotopic Pregnancy: Challenges in Diagnosis and Management

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Open Journal of Obstetrics and Gynecology 2016年第8期6卷 445-450页

作者： V. Sunita M. ramya T. Shruthi G. Sangeetha Department of Obstetrics and Gynaecology Sri Devraj Urs Medical College Kolar India

Objectives: 1) To study the obstetric outcome in two women with heterotopic pregnancy, 2) To report the challenges faced during management of these pregnancies. Design: Case study of two heterotopic pregnancies managed by us. Setting: RL Jalappa Hospital and Research Hospital. Patients: Two women with heterotopic pregnancy. Interventions: Laparotomy and management of ectopic component. Main Outcome Measures: Pregnancy outcomes following surgery. Results: Both pregnancies continued with one live birth at 36 weeks and one preterm delivery at 29 weeks with neonatal death subsequently. Conclusion: Pregnancy outcome after surgery for heterotopic pregnancy is guarded and is at high risk for preterm birth. Salpingostomy is a good option when faced with dilemma of whether it is hematosalpinx or ectopic pregnancy instead of salpingectomy.

关键词： Heterotopic Pregnancy Hematosalpinx Ectopic Pregnancy