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Theoretical modeling of tunable vibrations of three-dimensional serpentine structures for simultaneous measurement of adherent cell mass and modulus

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MRS Bulletin 2020年

作者： Zhao, Jianzhong Li, Weican Guo, Xingming Wang, Heling Rogers, John A. Huang, Yonggang Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States School of Engineering Brown University Rhode Island United States Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States Departments of Materials Science and Engineering Biomedical Engineering Chemistry Mechanical Engineering Electrical Engineering and Computer Science and Neurological Surgery Northwestern University Illinois United States Querrey-Simpson Institute for Bioelectronics Northwestern University Illinois United States Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States Querrey-Simpson Institute for Bioelectronics Northwestern University Illinois United States

Vibration-based methods can be used effectively to characterize the physical properties of biological materials, with an increasing interest focused on the mechanics of individual, living cells. Real-time measurements of cell properties, such as mass and Young's modulus, can yield important insights into many aspects of cell growth and metabolism as well as the interaction of cells with external stimuli (e.g., drugs). Vibrational test structures designed for the study of such cell properties often use fixed configurations and operational modes, with associated limitations in determining multiple characteristics of the cell, simultaneously. Recent development of mechanics-guided techniques for deterministic assembly of three-dimensional (3D) microstructures provides a route to vibrational frameworks that offer tunable configurations, vibration modes, and resonant frequencies. Here we propose a method that exploits such tunable vibrational structures to simultaneously determine the mass and modulus of a single adherent cell, or of other biological materials or small-scale living systems (e.g., organoids), through theoretical modeling and finite element analysis. The idea involves a 3D architecture that supports two different vibrational structures and can be converted from one to the other through application of strain to an elastomeric substrate. Specifically, tailored designs for serpentine ribbons in these systems enable a decoupling of the dependence of the resonant frequencies of the two structures to the cell mass and modulus, with an associated ability to measure these two properties accurately and independently. These same concepts can be scaled to apply to various types of cells, as well as to organoids (3D clusters of cells) and other biological materials with small geometries, across a range of values of mass and modulus. This method could serve as the foundation for microelectromechanical systems capable of monitoring mass and modulus in real time for use

关键词： Serpentine

Gamma-Ray Burst Light Curve Reconstruction: A Comparative Machine and Deep Learning Analysis

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arXiv 2024年

作者： Kaushal, A. Manchanda, A. Dainotti, M.G. Deepu, A. Naqi, S. Felix, J. Indoriya, N. Magesh, S.P. Gupta, H. Gupta, K. Madhan, A. Hartmann, D.H. Pollo, A. Bogdan, M. Prochaska, J.X. Fraija, N. Debnath, D. Department of Computer Science and Engineering UIET-H Panjab University Punjab 146001 India Department of Physics Indian Institute of Technology Telangana Hyderabad502284 India National Astronomical Observatory of Japan 2 Chome-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomical Sciences The Graduate University for Advanced Studies SOKENDAI Shonankokusaimura Miura District Kanagawa Hayama240-0115 Japan Nevada Center for Astrophysics University of Nevada 4505 Maryland Parkway Las VegasNV89154 United States Space Science Institute 4765 Walnut Street Suite B BoulderCO80301 United States Department of Ocean Engineering and Naval Architecture Indian Institute of Technology West Bengal Kharagpur721302 India KNIT Uttar Pradesh Sultanpur228118 India Indian Institute of Technology Kharagpur721302 India Department of Electrical Engineering and Computer Science IISER Madhya Pradesh Bhopal462066 India Indian Institute of Technology Madras600036 India MIT WPU School of Computer Science & Engineering Pune411038 India Department of Computer Science PSIT College Uttar Pradesh Kanpur209305 India Visveswaraya Technological University Karnataka Belagavi590018 India Department of Physics and Astronomy Clemson University ClemsonSC29634 United States Astronomical Observatory of Jagiellonian University in Kraków Orla 171 Kraków30-244 Poland National Centre for Nuclear Research Warsaw02-093 Poland Department of Mathematics University of Wroclaw Wroclaw50-384 Poland University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States National Autonoma University of Mexico Circuito Interior Mexico City04510 Mexico NIT Tripura Agartala799046 India

Gamma-Ray Bursts (GRBs), observed at large redshifts, are probes of the evolution of the Universe and can be used as cosmological tools. To this end, we need tight (with small dispersion) correlations among key parameters. To reduce such a dispersion, we will mitigate gaps in light curves (LCs), including the plateau region, key to building the two-dimensional Dainotti relation between the end time of plateau emission (Ta) to its luminosity (La). We reconstruct LCs using nine models: Multi-Layer Perceptron (MLP), Bi-Mamba, Fourier Transform, Gaussian Process–Random Forest Hybrid (GP-RF), Bidirectional Long Short-Term Memory (Bi-LSTM), Conditional GAN (CGAN), SARIMAX-based Kalman filter, Kolmogorov–Arnold Networks (KANs), and Attention U-Net. These methods are compared to the Willingale model (W07) over a sample of 545 GRBs. MLP and Bi-Mamba outperform other methods, with MLP reducing the plateau parameter uncertainties by 25.9% for log Ta, 28.6% for log Fa, and 37.7% for α (the post-plateau slope in the W07 model), achieving the lowest 5-fold cross validation (CV) mean squared error (MSE) of 0.0275. Bi-Mamba achieved the lowest uncertainty of parameters, a 33.3% reduction in log Ta and log Fa and a 41.9% in α, but with a higher MSE of 0.130. Bi-Mamba brings the lowest outlier percentage for log Ta and log Fa (2.70%), while MLP carry α outliers to 0.900%. The other methods yield MSE values ranging from 0.0339 to 0.174. These improvements in parameter precision are needed to use GRBs as standard candles, investigate theoretical models, and predict GRB redshifts through machine learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

IC-U-Net: A U-Net-based denoising autoencoder using mixtures of independent components for automatic EEG artifact removal

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arXiv 2021年

作者： Chuang, Chun-Hsiang Chang, Kong-Yi Huang, Chih-Sheng Jung, Tzyy-Ping Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Department of Artificial Intelligence Research and Development Elan Microelectronics Corporation Hsinchu Taiwan Institute of Engineering in Medicine Institute for Neural Computation University of California San Diego La Jolla United States

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent misinterpretations of neural signals and underperformance of brain-computer interfaces. This study developed a new artifact removal method, IC-U-Net, which is based on the U-Net architecture for removing pervasive EEG artifacts and reconstructing brain sources. The IC-U-Net was trained using mixtures of brain and non-brain sources decomposed by independent component analysis and employed an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain sources and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noises) was demonstrated in a simulation study and three real-world EEG datasets collected at rest and while driving and walking. IC-U-Net is user-friendly and publicly available, does not require parameter tuning or artifact type designations, and has no limitations on channel numbers. Given the increasing need to image natural brain dynamics in a mobile setting, ICU-Net offers a promising end-to-end solution for automatically removing artifacts from EEG recordings. Copyright © 2021, The Authors. All rights reserved.

关键词： Electroencephalography

String-Breaking Dynamics in Quantum Adiabatic and Diabatic Processes

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arXiv 2024年

作者： Surace, Federica Maria Lerose, Alessio Katz, Or Bennewitz, Elizabeth R. Schuckert, Alexander Luo, De De, Arinjoy Ware, Brayden Morong, William Collins, Kate Monroe, Christopher Davoudi, Zohreh Gorshkov, Alexey V. Department of Physics Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Rudolf Peierls Centre for Theoretical Physics Clarendon Laboratory OxfordOX1 3PU United Kingdom Institute for Theoretical Physics KU Leuven Celestijnenlaan 200D Leuven3001 Belgium Duke Quantum Center Departments of Physics and Electrical and Computer Engineering Duke University DurhamNC27708 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States

Confinement prohibits isolation of color charges, e.g., quarks, in nature via a process called string breaking: the separation of two charges results in an increase in the energy of a color flux, visualized as a string, connecting those charges. Eventually, creating additional charges is energetically favored, hence breaking the string. Such a phenomenon can be probed in simpler models, including quantum spin chains, enabling enhanced understanding of string-breaking dynamics. A challenging task is to understand how string breaking occurs as time elapses, in an out-of-equilibrium setting. This work establishes the phenomenology of dynamical string breaking induced by a gradual increase of string tension over time. It, thus, goes beyond instantaneous quench processes and enables tracking the real-time evolution of strings in a more controlled setting. We focus on domain-wall confinement in a family of quantum Ising chains. Our results indicate that, for sufficiently short strings and slow evolution, string breaking can be described by the transition dynamics of a two-state quantum system akin to a Landau-Zener process. For longer strings, a more intricate spatiotemporal pattern emerges: the string breaks by forming a superposition of bubbles (domains of flipped spins of varying sizes), which involve highly excited states. We finally demonstrate that string breaking driven only by quantum fluctuations can be realized in the presence of sufficiently long-ranged interactions. This work holds immediate relevance for studying string breaking in quantum-simulation experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Excited states

Invited: Drug discovery approaches using quantum machine learning

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arXiv 2021年

作者： Li, Junde Alam, Mahabubul Sha, Congzhou M. Wang, Jian Dokholyan, Nikolay V. Ghosh, Swaroop Department of Computer Science and Engineering Penn State University University Park United States Department of Electrical Engineering Penn State University University Park United States Department of Engineering Science and Mechanics Penn State University University Park United States Department of Pharmacology Penn State College of Medicine Hershey United States Department of Biochemistry & Molecular Biology Penn State College of Medicine Hershey United States Departments of Chemistry and Biomedical Engineering Penn State University University Park United States

Traditional drug discovery pipeline takes several years and cost billions of dollars. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently produce atypical patterns of quantum computers which might improve the training quality of learning tasks. We propose a suite of quantum machine learning techniques e.g., generative adversarial network (GAN), convolutional neural network (CNN) and variational auto-encoder (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively. © 2021, CC BY.

关键词： Molecules

A testbed for investigation of selective laser melting at elevated atmospheric pressure

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arXiv 2021年

作者： Griggs, David A. Gibbs, Jonathan S. Baker, Stuart P. Penny, Ryan W. Feldmann, Martin C. Hart, A. John Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Naval Architecture and Ocean Engineering U.S. Naval Academy AnnapolisMD United States Materials and Manufacturing Directorate Air Force Research Laboratory DaytonOH United States

Metal additive manufacturing (AM) by laser powder bed fusion (L-PBF) builds upon fundamentals established in the field of laser welding which include the influence of gas and plume dynamics on weld depth and quality. L-PBF demands a thorough investigation of the complex thermophysical phenomena that occur where the laser interacts with the metal powder bed. In particular, melt pool turbulence and evaporation are influenced by the ambient gas chemistry and pressure. This paper presents the design and validation of high pressure laser melting (HPLM) testbed;this accommodates bare metal plate samples as well as manually-coated single powder layers, and operates at up to 300 psig. The open architecture of this testbed allows for full control of all relevant laser parameters in addition to ambient gas pressure and gas flow over the build area. Representative melt tracks and rasters on bare plate and powder are examined in order to validate system performance, and preliminary analysis concludes that pressure has a significant impact on melt pool aspect ratio. The HPLM system thus enables careful study pressure effects on processing of common L-PBF materials, and can be applied in the future to materials that are challenging to process under ambient pressure, such as those with high vapor pressures. © 2021, CC BY.

关键词： Lakes

An LQR-assisted control algorithm for an under-actuated in-pipe robot in water distribution systems

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arXiv 2020年

作者： Kazeminasab, Saber Jafari, Roozbeh Banks, M. Katherine Department of Electrical and Computer Engineering Texas A&M University College Station TX United States Departments of Electrical and Computer Computer Science and Biomedical Engineering Texas A&M University College Station TX United States College of Engineering Texas A&M University College Station TX United States

To address the operational challenges of in-pipe robots in large pipes of water distribution systems (WDS), in this research, a control algorithm is proposed for our previously designed robot [4]. Our size adaptable robot has an under-actuated modular design that can be used for both leak detection and quality monitoring. First, nonlinear dynamical governing equations of the robot are derived with the definition of two perpendicular planes and two sets of states are defined for the robot for stabilization and mobilization. For stabilization, we calculated the auxiliary system matrices and designed a stabilizer controller based on the linear quadratic regulator (LQR) controller, and combined it with a proportional-integral-derivative (PID) based controller for mobilization. The controller scheme is validated with simulation in MATLAB in various operation conditions in three iterations. The simulation results show that the controller can stabilize the robot inside the pipe by converging the stabilizing states to zero and keeping them in zero with initial values between -25° and +25° and tracking velocities of 10cm/s, 30cm/s, and 50cm/s which makes the robot agile and dexterous for operation in pipelines. © 2020, CC BY.

关键词： Controllers

RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-based Semantic Segmentation

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arXiv 2021年

作者： He, Shuang Lu, Xia Gu, Jason Tang, Haitong Yu, Qin Liu, Kaiyue Ding, Haozhou Chang, Chunqi Wang, Nizhuan Jiangsu Key Laboratory of Marine Bioresources and Environment Jiangsu Key Laboratory of Marine Biotechnology Co-Innovation Center of Jiangsu Marine Bio-Industry Technology Jiangsu Ocean University Lianyungang222005 China School of Geomatics and Marine Information Jiangsu Ocean University Lianyungang222005 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing211800 China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518055 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China

For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important. How to get the trade-off effectively is an open question, where current approaches of utilizing very deep models result in complex models with large memory consumption. In contrast to previous work that utilizes dilated convolutions or deep models, we propose a novel two-stream deep neural network for semantic segmentation of RSI (RSI-Net) to obtain improved performance through modeling and propagating spatial contextual structure effectively and a decoding scheme with image-level and graph-level combination. The first component explicitly models correlations between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions by using graph convolutional network, while densely connected atrous convolution network (DenseAtrousCNet) with multi-scale atrous convolution can expand the receptive fields and obtain image global information. Extensive experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets, where the comparison results demonstrate the superior performance of RSI-Net in terms of overall accuracy (91.83%, 93.31% and 93.67% on three datasets, respectively), F1 score (90.3%, 91.49% and 89.35% on three datasets, respectively) and kappa coefficient (89.46%, 90.46% and 90.37% on three datasets, respectively) when compared with six state-of-the-art RSI semantic segmentation methods. © 2021, CC BY-NC-ND.

关键词： Semantic Segmentation

Deep learning techniques for inverse problems in imaging

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arXiv 2020年

作者： Ongie, Gregory Jalal, Ajil Metzler, Christopher A. Baraniuk, Richard G. Dimakis, Alexandros G. Willett, Rebecca Department of Statistics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering University of Texas at Austin AustinTX United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Electrical and Computer Engineering University of Texas at Austin AustinTX United States Departments of Statistics and Computer Science University of Chicago ChicagoIL United States

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the tradeoffs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work. Copyright © 2020, The Authors. All rights reserved.

关键词： Taxonomies