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搅拌针压入深度对搅拌摩擦成形AA 5052-H32和AA 6061-T6金属板的接头形成和力学性能的影响（英文）

Transactions of Nonferrous Metals Society of China 2018年第4期28卷 613-628页

作者： Tinu P.SAJU R.Ganesh Narayanan Department of Mechanical Engineering Indian Institute of Technology Guwahati

研究不同等级的AA 5052-H32和AA 6061-T6铝合金搅拌摩擦成形连接的可能性。结果表明,当搅拌针压入深度为0.5~0.7 mm时,采用万能试验机测试得到的搭接剪切载荷为7.16 k N,横向拉伸载荷为3.51 k N,优于采用搅拌摩擦焊和搅拌摩擦点焊在相... 详细信息

研究不同等级的AA 5052-H32和AA 6061-T6铝合金搅拌摩擦成形连接的可能性。结果表明,当搅拌针压入深度为0.5~0.7 mm时,采用万能试验机测试得到的搭接剪切载荷为7.16 k N,横向拉伸载荷为3.51 k N,优于采用搅拌摩擦焊和搅拌摩擦点焊在相同材料上焊接的接头的性能。采用光学显微镜观察接头微观组织,发现接头的增强是由于机械销连锁或冶金结合。采用维氏硬度仪测量显微硬度,揭示搅拌针压入深度对搅拌区形成的作用和摩擦热流密度对低层金属薄片的影响。电子显微镜形貌显示,搅拌针压入深度对接头中机械销的形成和几何特征存在显著影响。不同的失效模式,例如销拔出、销剪切、部分结合分层和剥落,取决于不同的搅拌针压入深度形成的关键薄弱区。

关键词：搅拌摩擦成形搅拌针压入深度铝合金搭接剪切试验横向拉伸试验失效模式

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Enhancing the Surface Properties of a Bioengineered Anterior Cruciate Ligament Matrix for Use with Point-of-Care Stem Cell Therapy

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Engineering 2021年第2期7卷 153-161页

作者： Xiaohua Yu Paulos Y.Mengsteab Ganesh Narayanan Lakshmi S.Nair Cato T.Laurencin Connecticut Convergence Institute for Translation in Regenerative Engineering University of Connecticut HealthFarmingtonCT 06030USA Raymond and Beverly Sackler Center for Biological Physical and Engineering SciencesUniversity of Connecticut HealthFarmingtonCT 06030USA Department of Orthopedic Surgery University of Connecticut HealthFarmingtonCT 06030USA Department of Biomedical Engineering University of ConnecticutStorrsCT 06269USA Department of Materials Science and Engineering University of ConnecticutStorrsCT 06269USA Department of Chemical and Biomolecular Engineering University of ConnecticutStorrsCT 06269USA Department of Reconstructive Sciences University of Connecticut HealthFarmingtonCT 06030USA

We have previously developed a poly(L-lactic)acid(PLLA)bioengineered anterior cruciate ligament(ACL)matrix that has demonstrated enhanced healing when seeded with primary ACL cells prior to implantation in a rabbit model,as compared with the matrix alone.This suggests that improving cell adhesion on the matrix may beneficially affect the healing response and long-term performance of the bioengineered ACL matrix.One regenerative engineering approach involves enhancing the surface properties of the matrix to support cell adhesion and growth in combination with point-of-care stem cell therapy.Herein,we studied the cell adhesion properties of PLLA braided microfiber matrices enhanced through the physical adsorption of fibronectin and air plasma treatment.We evaluated the kinetics and binding efficiency of fibronectin onto matrices at three time points and three fibronectin concentrations.Incubating the matrix for 120 min in a solution of 25 lgmL1 fibronectin achieved the greatest binding efficiency to the matrix and cellular adhesion.Exposing the matrices to air plasma treatment for 5 min before fibronectin adsorption significantly enhanced the cell adhesion of rabbit bone marrow-derived mesenchymal stem cells(R-BMMSCs)24 h post cell seeding.Finally,cellular proliferation was monitored for up to 21 d,the matrices were exposed to air plasma treatment,and fibronectin adsorption was found to result in enhanced cell number.These findings suggest that exposure to air plasma treatment and fibronectin adsorption enhances the cellular adhesion of PLLA braided microfiber matrices and may improve the clinical efficacy of the matrix in combination with point-of-care stem cell therapies.

关键词： Anterior cruciate ligament Ligament Poly(L-lactic)acid plasma treatment Fibronectin Stem cells Adhesion

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Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 103-141页

作者： narayanan ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than other traditional machine learning(ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Machine-learned interatomic potentials by active learning:amorphous and liquid hafnium dioxide

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npj Computational Materials 2020年第1期6卷 803-810页

作者： ganesh Sivaraman Anand narayanan Krishnamoorthy Matthias Baur Christian Holm Marius Stan Gábor Csányi Chris Benmore Álvaro Vázquez-Mayagoitia Leadership Computing Facility Argonne National LaboratoryArgonneIL 60439USA Institute for Computational Physics Universität StuttgartAllmandring 3Stuttgart 70569Germany Helmholtz-Institute Münster:Ionics in Energy Storage(IEK-12) Forschungszentrum Jülich GmbHCorrensstrasse 46Münster 48149Germany Applied Materials Division Argonne National LaboratoryArgonneIL 60439USA Department of Engineering University of CambridgeTrumpington StreetCambridge CB21PZUK X-ray Science Division Argonne National LaboratoryArgonneIL 60439USA Computational Science Division Argonne National LaboratoryArgonneIL 60439USA

We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential(GAP).Our active learning scheme consists of an unsupervised machine learning(ML)scheme coupled with a Bayesian optimization technique that evaluates the GAP model.We apply this scheme to a Hafnium dioxide(HfO2)dataset generated from a“melt-quench”ab initio molecular dynamics(AIMD)protocol.Our results show that the active learning scheme,with no prior knowledge of the dataset,is able to extract a configuration that reaches the required energy fit tolerance.Further,molecular dynamics(MD)simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates(i.e.,1.0 K/ps)not accessible via AIMD.The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment.In addition,the calculated diffusion constants are in good agreement with previous ab initio studies.

关键词： amorphous dioxide scheme

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Ivabradine in the treatment of systolic heart failure- A systematic review and meta-analysis

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World Journal of Cardiology 2017年第2期9卷 182-190页

作者： Mahesh Anantha narayanan Yogesh NV Reddy Janani Baskaran Abhishek Deshmukh David G Benditt ganesh Raveendran Division of Cardiovascular Diseases University of Minnesota Medical Center Division of Cardio-vascular Diseases Mayo ClinicRochester

AIM To perform a systematic-review and meta-analysis to compare outcomes of ivabradine combined with betablocker to beta-blocker alone in heart failure with reduced ejection fraction(HFr EF).METHODS We searched PubM ed, Cochrane, EMBASE, CINAHL and Web of Science for trials comparing ivabradine + betablocker to beta-blocker alone in HFr EF. We performed a systematic-review and meta-analysis of published literature. Primary end-point was combined end point of cardiac death and hospitalization for heart failure.RESULTS Six studies with 17671 patients were included. Mean follow-up was 8.7 ± 7.9 mo. Combined end-point of heart failure readmission and cardiovascular death was better in ivabradine + beta-blocker group compared to beta-blocker alone(RR: 0.93, 95%CI: 0.79-1.09, P = 0.354). Mean difference(MD) in heart rate was higher in the ivabradine + beta-blocker group(MD: 6.14, 95%CI: 3.80-8.48, P < 0.001). There was no difference in all cause mortality(RR: 0.98, 95%CI: 0.89-1.07, P = 0.609), cardiovascular mortality(RR: 0.99, 95%CI: 0.86-1.15, P = 0.908) or heart failure hospitalization(RR: 0.87, 95%CI: 0.68-1.11, P = 0.271). CONCLUSION From the available clinical trials, ivabradine + betablocker resulted in a significantly greater reduction in HRcoupled with improvement in combined end-point of heart failure readmission and cardiovascular death but with no improvement in all cause or cardiovascular mortality. Given the limited evidence, further randomized controlled trials are essential before widespread clinical application of ivabradine + beta-blocker is advocated for HFrEF.

关键词： Ivabradine 心失败

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