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Polyvinylpyrollidone/Cellulose Acetate (PVA/CA) Fiber Size Prediction Using Scaling Law Model

Journal of Physics: Conference Series 2020年第1期1467卷

作者： J Jauhari AJ Suharli R Saputra Z Nawawi I Sriyanti Laboratory of Instrumentation and Nanotechnology Applications Faculty of Computer Science Sriwijaya University Palembang Indonesia Department of Physics Education Sriwijaya University Palembang Indonesia Department of Electrical Engineering Universitas Sriwijaya Palembang Indonesia

The purpose of this research was to compare the diameter of the fiber produced using the electrospining method and scaling law model. The electrospinning process is a simple technique in producing nanofibers from the incorporation of various polymers. The polymer material used is Poly (vinylpyrrolidone) (PVP) with Cellulose acetate (CA) solvent. Cellulose acetate (CA) solvents using different concentrations were 6% (PC 1), 10% (PC 2) and 17% (PC 3). The process parameters used electrospinning voltage is 10 kV, the needle tip to collector distance is 13 cm and flowrate used is 3.2 µL/hour. The product of nanofibers was morphologically characterized using a digital microscope and the fiber diameter size was predicted using a scaling law model. The scaling law model predicts electrical conductivity in PC 1, PC 2, PC 3 samples are 0.004298 S/m, 0.001289 S/m, and 0.000374 S/m respectively. The results of the logarithmic comparison of Q/K to the diameter of the nanofiber produce a linear graph pattern with a decrease in the value of K resulting in the diameter of the nanofiber-based on the results of experiments that have been carried out.

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Corrigendum to “High-performance perovskite solar cells using graphene quantum dot modified SnO 2 /ZnO photoelectrode” [Mater Today Energy 22 (2021) 100853]

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Materials Today Energy 2024年 42卷

作者： G. Nagaraj Mustafa K.A. Mohammed Masoud Shekargoftar P. Sasikumar P. Sakthivel G. Ravi M. Dehghanipour Seckin Akin Ahmed Esmail Shalan Bharat Rural Research Center Dharmapuri Tamil Nadu India Computer Sciences Department Dijlah University College Al-Masafi Street Baghdad 00964 Iraq Al-Turath University College Baghdad 00964 Iraq Laboratory for Biomaterials and Bioengineering (CRC-I) Department of Min-Met-Materials Engineering Laval University Quebec City QC G1V0A6 Canada Department of Physics Periyar University P.G Extension Center Dharmapuri Tamil Nadu India Department of Physics Alagappa University Karaikudi 630003 Tamil Nadu India Department of Physics Faculty of Science Yazd University Yazd Iran Karamanoglu Mehmetbey University Department of Metallurgical and Materials Engineering Karaman Turkey BCMaterials Basque Center for Materials Applications and Nanostructures Martina Casiano UPV/EHU Science Park Barrio Sarriena s/n Leioa 48940 Spain Central Metallurgical Research and Development Institute (CMRDI) P.O. Box 87 Helwan Cairo 11421 Egypt

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Weighted graphlets and deep neural networks for protein structure classification

arXiv

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arXiv 2019年

作者： Guo, Hongyu Newaz, Khalique Emrich, Scott Milenković, Tijana Li, Jun Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46556 United States Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Eck Institute for Global Health University of Notre Dame Notre DameIN46556 United States Interdisciplinary Center for Network Science & Applications University of Notre Dame Notre DameIN46556 United States Department of Electrical Engineering & Computer Science University of Tennessee KnoxvilleTN37996 United States

As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem of protein structure classification, which computationally classifies the structures of proteins into pre-defined groups. We develop a weighted network that depicts the protein structures, and more importantly, we propose the first graphlet-based measure that applies to weighted networks. Further, we develop a deep neural network (DNN) composed of both convolutional and recurrent layers to use this measure for classification. Put together, our approach shows dramatic improvements in performance over existing graphlet-based approaches on 36 real datasets. Even comparing with the state-of-the-art approach, it almost halves the classification error. In addition to protein structure networks, our weighted-graphlet measure and DNN classifier can potentially be applied to classification of other weighted networks in computational biology as well as in other domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Active flow control for external aerodynamics: from micro air vehicles to a full aircraft in stall

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Journal of Physics: Conference Series 2020年第1期1522卷

作者： Oriol Lehmkuhl Adrián Lozano-Durán Ivette Rodriguez Dpt. of Computer Applications in Science and Engineering Barcelona Supercomputing Center Spain Center for Turbulence Research Stanford University USA Dpt. of Heat Engines Universitat Politecnica de Catalunya Spain

We investigate the aerodynamic performance of active flow control of airfoils and wings using synthetic jets with zero net-mass flow. The study is conducted via wall-resolved and wall-modeled large-eddy simulation using two independent CFD solvers: Alya, a finite-element-based solver; and charLES, a finite-volume-based solver. Our approach is first validated in a NACA4412, for which numerical and experimental results are already available in the literature. The performance of synthetic jets is evaluated for two flow configurations: a SD7003 airfoil at moderate Reynolds number with laminar separation bubble, which is representative of Micro Air Vehicles, and the high-lift configuration of the JAXA Standard Model at realistic Reynolds numbers for landing. In both cases, our predictions indicate that, at high angles of attack, the control successfully eliminates the laminar/turbulent recirculations located downstream the actuator, which increases the aerodynamic performance. Our efforts illustrate the technology-readiness of large-eddy simulation in the design of control strategies for real-world external aerodynamic applications.

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Electrospun of Poly (vinyl alcohol)/ Potassium hydroxide (PVA/KOH) nanofiber composites using the electrospinning method

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IOP Conference Series: Materials science and engineering 2020年第1期850卷

作者： M R Almafie Z Nawawi J Jauhari I Sriyanti Department of Physics Education Sriwijaya University Palembang Indonesia Department of Electrical Engineering Sriwijaya University Palembang Indonesia Laboratory of Instrumentation and Nanotechnology Applications Faculty of Computer Science Sriwijaya University Palembang Indonesia

The Poly (vinyl alcohol)/Potassium hydroxide (PVA/KOH) nanofiber Composites for application as a supercapacitor electrolyte. Electrospinning method has been successfully used to synthesize composite of the PVA/KOH nanofiber. The PVA and KOH have been made with concentration are 5% w/w (PK1), 10% w/w (PK2) and 15% w/w (PK3), with a mass ratio of PVA/KOH is 10:1. The Physicochemical properties of PVA/KOH nanofibers with three various in the experiment were studied, including morphology, size, and chemical interaction. The microscope result shows that nanofiber of PK1, PK2 and PK3 have bead fiber and free-bead fiber, Where the PK1 is bead fiber and the PK2 and PK3 are free-bead fiber. The average diameter of PK1, PK2 and PK3 were 635, 826, 1021 nm, respectively. The FTIR results show that there is interaction between Poly (vinyl alcohol) and Potassium hydroxide (KOH) in the form of a spectrum and widening of transmittance are 3320 cm-1 and 3301cm-1 which identifies hydrogen bonds.

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Proton-irradiation-immune electronics implemented with two-dimensional charge-density-wave devices

arXiv

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arXiv 2019年

作者： Geremew, A. Kargar, F. Zhang, E.X. Zhao, S.E. Aytan, E. Bloodgood, M.A. Salguero, T.T. Rumyantsev, S. Fedoseyev, A. Fleetwood, D.M. Balandin, A.A. Nano-Device Laboratory Department of Electrical and Computer Engineering Materials Science and Engineering Program University of California RiversideCA92521 United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States Department of Chemistry University of Georgia AthensGA30602 United States Center for Terahertz Research and Applications Institute of High-Pressure Physics Polish Academy of Sciences Warsaw01-142 Poland Ultra Quantum Inc. HuntsvilleAL35758 United States

Proton radiation damage is an important failure mechanism for electronic devices in near-Earth orbits, deep space and high energy physics facilities1-4. Protons can cause ionizing damage and atomic displacements, resulting in device degradation and malfunction5-10. Shielding of electronics increases the weight and cost of the systems but does not eliminate destructive single events produced by energetic protons 8,10. Modern electronics based on semiconductors - even those specially designed for radiation hardness - remain highly susceptible to proton damage. Here we demonstrate that room temperature (RT) charge-density-wave (CDW) devices with quasi-two-dimensional (2D) 1T-TaS2channels show remarkable immunity to bombardment with 1.8 MeV protons to a fluence of at least 1014H+cm-2. Current-voltage I-V characteristics of these 2D CDW devices do not change as a result of proton irradiation, in striking contrast to most conventional semiconductor devices or other 2D devices. Only negligible changes are found in the low-frequency noise spectra. The radiation immunity of these "all-metallic" CDW devices can be attributed to their two-terminal design, quasi-2D nature of the active channel, and high concentration of charge carriers in the utilized CDW phases. Such devices, capable of operating over a wide temperature range, can constitute a crucial segment of future electronics for space, particle accelerator and other radiation environments. Copyright © 2019, The Authors. All rights reserved.

关键词： Failure (mechanical)

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Aligning dynamic networks with DynaWAVE (vol 34, pg 1795, 2017)

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BIOINFORMATICS 2018年第11期34卷 1980-1980页

作者： Vijayan, Vipin Milenkovic, Tijana Department of Computer Science and Engineering ECK Institute for Global Health and Interdisciplinary Center for Network Science and Applications (iCeNSA) University of Notre Dame Notre Dame IN 46556 USA. Department of Computer Science and Engineering ECK Institute for Global Health and Interdisciplinary Center for Network Science and Applications (iCeNSA) University of Notre Dame Notre Dame IN 46556 USA.

Motivation: Network alignment (NA) aims to find similar (conserved) regions between networks, such as cellular networks of different species. Until recently, existing methods were limited to aligning static networks. However, real-world systems, including cellular functioning, are dynamic. Hence, in our previous work, we introduced the first ever dynamic NA method, DynaMAGNA ++, which improved upon the traditional static NA. However, DynaMAGNA ++ does not necessarily scale well to larger networks in terms of alignment quality or ***: To address this, we introduce a new dynamic NA approach, DynaWAVE. We show that Dyna- WAVE complements DynaMAGNA ++: while DynaMAGNA ++ is more accurate yet slower than DynaWAVE for smaller networks, DynaWAVE is both more accurate and faster than DynaMAGNA ++ for larger networks. We provide a friendly user interface and source code for *** availability: https://***/∼cone/DynaWAVE/.Contact: tmilenko@*** information: Attached.(© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: ***@***)

关键词： DYNAMIC simulation SEQUENCE alignment (Bioinformatics)

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Rooted Tree Optimization for Backstepping Power Control of DFIG Wind Turbine: dSPACE Implementation

Research Square

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Research Square 2020年

作者： Bossoufi, Badre Karim, Mohammed Taoussi, Mohammed Alami-Aroussi, Hala Bouderbala, Manale Deblecker, Olivier Motahhir, Saad Nayyar, Anand Masud, Mehedi LIMAS Laboratory Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez Morocco LGEM Laboratory Higher School of Technology Mohammed First University Oujda Morocco EPEU unit Polytech of Mons Mons Belgium Engineering Systems and Applications Laboratory Sidi Mohamed Ben Abdellah University Fez Morocco Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam Department of Computer Science College of Computers and Information Technology Taif University Taif Saudi Arabia

This work is devoted to a new contribution to the field of optimization and control of a wind energy conversion system (WECS). A Rooted Tree Optimisation (RTO) will be applied to the non-linear adaptive Backstepping technique to improve its robustness and performance. The non-linear Backstepping control was carried out to control the powers of the doubly-fed induction generator (DFIG) connected to the electrical network by two converters (network side and machine side). Initially, a review of the wind power system was presented. Then, an exhaustive explanation of the Backstepping technique based on the Lyapunov stability and the optimization method was reported. Subsequently, a validation on the Matlab & Simulink environment was carried out to test the performance and robustness of the proposed model. The last part of this work was dedicated to the experiment of the Backstepping adaptive algorithm on a test bench using the dSPACE-DS1104 card, to prove the performance of the system. The results obtained of this work either by follow-up or robustness tests or by experimental validation show a great improvement in terms of performance compared to other control techniques. © 2020, CC BY.

关键词： Simulink

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Perspectives for self-driving labs in synthetic biology

arXiv

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arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

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An efficient meta-heuristic chemical reaction optimization based algorithm for Association rule Hiding using an advanced perturbation approach

An efficient meta-heuristic chemical reaction optimization b...

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International Conference on Intelligent Computing and Control Systems

作者： T.Satyanarayana Murthy N.P.Gopalan Pardha Praneeth Pudi Department of Computer Applications National Institute of Technology Tiruchirapalli Tamilnadu India Department of Computer Science and Engineering IIIT Tiruchirapalli Tamilnadu India

ISBN: (纸本)9781538628430;9781538628423

Meta-heuristic approaches like genetic algorithm, particle swarm, chemical reaction and cuckoo optimization algorithms have a trade-off in the reduction of ghost rules and lost rules in association rule hiding. The implications found in this context are motivating the researchers towards novel meta-heuristic chemical reaction optimization algorithm based on data modification algorithm. This paper inherits the chemical reaction optimization functionalities and has been used for association rule hiding. It produces better results in comparison with Genetic Algorithm based, Particle Swarm Optimization based, Cuckoo based algorithms.

关键词： Association rule hiding Genetic algorithm Ghost rules Lost rules

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