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Correction to “A Sigmoid-Colon-Straightening Soft Actuator With Peristaltic Motion for Colonoscopy Insertion Assistance: Easycolon”

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IEEE Robotics and Automation Letters 2021年第3期6卷 5736-5736页

作者： Hansoul Kim Joonhwan Kim Jae Min You Seung Woo Lee Ki-Uk Kyung Dong-Soo Kwon Division of Mechanical Engineering School of Mechanical Aerospace and Systems Engineering KAIST Daejeon South Korea Robotics Program KAIST Daejeon South Korea Division of Gastroenterology Department of Internal Medicine College of Medicine The Catholic University of Korea Seoul South Korea

There are two corresponding authors who have contributed to this letter, yet only one of them has been mentioned as the corresponding author in the original letter. As a result, “Ki-Uk Kyung” was added to footnote a... 详细信息

关键词： Actuators Colonoscopy

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Design and analysis of component for non-rotor unmanned aerial vehicle

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Journal of Physics: Conference Series 2021年第1期2020卷

作者： Y R Shiau J Fang Y L Chen Department of Industrial Engineering and Systems Management Feng Chia University Taichung 407 Taiwan Department of Aerospace and Systems Engineering Feng Chia University Taichung 407 Taiwan Ph.D. Program of Mechanical and Aeronautical Engineering Feng Chia University Taichung 407 Taiwan

Unmanned aerial vehicles (UAVs) are widely applied in various industries and fields. Because they use a variety of rotor types, from single rotors to multi-rotors, UAVs offer a wide array of functions. However, continuously rotating rotors can be dangerous if they accidentally encounter foreign objects or bare hands. Therefore, non-rotor UAVs are the focus of discussion and modification in the present study. Non-rotor UAVs do not contain visible rotor mechanical components. Compared with the development of popularized multirotor UAVs, that of non-rotor UAVs is challenging in terms of structure and flight control. To address this challenge, wind tunnel structure models were developed in this study for different levels of aerodynamic force, and computer-aided engineering was used to conduct structural and flow field analyses to determine the key elements that reinforce non-rotor UAV structures. The research results revealed that the thickness and fillet design at the joint of the non-rotor UAV aerodynamic wind tunnel system were crucial factors influencing the system's performance. Motor rotors can be embedded inside support structures to reduce airflow turbulence in the wind tunnel.

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Explainable Machine Learning for Anaerobic Digestion: A Critical Review

SSRN

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SSRN 2022年

作者： Li, Wangliang Gupta, Rohit Zhang, Le Liu, Hongtao Zhang, Zhikai Hou, Jiayi You, Siming Ok, Yong Sik James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Nanoengineered Systems Laboratory UCL Mechanical Engineering University College London LondonWC1E 7JE United Kingdom Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London LondonW1W 7TS United Kingdom Department of Resources and Environment School of Agriculture and Biology Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Institute of Geographic Science and Natural Resources Research Chinese Academy of Sciences Beijing100101 China CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China School of Water Resources and Environment Hebei GEO University Hebei Shijiazhuang050031 China Korea Biochar Research Center APRU Sustainable Waste Management Program Division of Environmental Science and Ecological Engineering Korea University Seoul02841 Korea Republic of

Anaerobic digestion (AD) is a traditional process to achieve energy recovery from a variety of organic waste streams. However, the AD performance in terms of biogas production and process stability relies on the cooperation of the functional microbial communities and the operating parameters. The latest progress in machine learning for AD processes including optimization, parametric prediction, perturbation detection, and long-term monitoring were critically reviewed. The explainable data-driven models including black-box machine learning (ML) models and nature-inspired optimization techniques were compared, while summarizing their applications. The review also highlighted the challenges, perspectives, and opportunities for future research of applying ML for AD processes. © 2022, The Authors. All rights reserved.

关键词： Optimization

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A robotic and virtual testing platform highlighting the promise of soft wearable actuators for wrist tremor suppression

Device

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Device 2025年

作者： Alona Shagan Shomron Christina Chase-Markopoulou Johannes R. Walter Johanna Sellhorn-Timm Yitian Shao Tobias Nadler Audrey Benson Isabell Wochner Ellen H. Rumley Isabel Wurster Philipp Klocke Daniel Weiss Syn Schmitt Christoph Keplinger Daniel F.B. Haeufle Robotic Materials Department Max Planck Institute for Intelligent Systems Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Stuttgart Germany Stuttgart Center for Simulation Science (SimTech) University of Stuttgart Stuttgart Germany Hertie Institute for Clinical Brain Research University of Tübingen Tübingen Germany Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany Institute for Computer Engineering (ZITI) University of Heidelberg Heidelberg Germany Paul M. Rady Department of Mechanical Engineering University of Colorado Boulder CO USA Center for Neurology Department of Neurodegenerative Diseases University of Tübingen Tübingen Germany Center for Bionic Intelligence (BITS) Tübingen and Stuttgart Germany Material Science and Engineering Program University of Colorado Boulder CO USA Werner Reichardt Center for Integrative Neuroscience University of Tübingen Tübingen Germany

Nearly 80 million people in the world deal with medical conditions that cause involuntary periodic movements known as tremors. Wearable soft robotic devices offer a potential solution for actively suppressing these tremors. However, existing prototypes face limitations in actuation performance and complex testing procedures. We present a comprehensive approach for the rapid evaluation of emerging wearable tremor-suppression technologies. This method combines reproducing patient-recorded tremor episodes and measuring tremor suppression in a robotic platform, termed a “mechanical patient”, with validation of the achieved suppression performance of soft actuators via biomechanical modeling, thereby avoiding time-consuming clinical testing in the early stages of development. Using this approach, we highlight that an antagonistic pair of slim and lightweight electrohydraulic actuators can effectively suppress clinically relevant tremors between 2 and 8 Hz. The biomechanical model confirms that the pair of actuators generates adequate suppression forces for all tested tremors.

关键词： tremor suppression mechanical patient soft actuators wearable devices biomechanical simulation DTI-4: Validate

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Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases

Electricity energy price forecasting based on hybrid multi-s...

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International Joint Conference on Neural Networks (IJCNN)

作者： Matheus Henrique Dal Molin Ribeiro Ramon Gomes da Silva Cristiane Canton Naylene Fraccanabbia Viviana Cocco Mariani Leandro dos Santos Coelho Industrial & Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Pato Branco University Center (UNIDEP) Pato Branco Parana Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering Federal University of Parana (UFPR) Curitiba Parana Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The development of accurate models to forecast electricity energy prices is a challenge due to the number of factors which can affect this commodity. In this paper, a hybrid multi-stage approach is proposed to forecast multi-stepahead (one, two and three-month-ahead) Brazilian commercial and residential electricity energy prices. The proposed data analysis combines the pre-processing named complementary ensemble empirical mode decomposition (CEEMD) in the first stage coupled with the coyote optimization algorithm (COA) to define the CEEMD's hyperparameters, aiming to deal with time series non-linearities and enhance the model's performance. On the next stage, four machine learning models named extreme learning machine, Gaussian process, gradient boosting machine, and relevance vector machine are employed to train and predict the CEEMD's components. Finally, in the final stage, the results of the previous step are directly integrated to compose a heterogeneous ensemble learning of components to obtain the final forecasts. In this case, a grid of models is obtained. The best model is one that has better generalization out-of-sample. Through developed comparisons, results showed that combining COA-CEEMD with a heterogeneous ensemble learning can develop accurate forecasts. The modeling developed in this paper is promising and can support decision making in electricity energy price forecasting.

关键词： Predictive models Biological system modeling Forecasting Data models Optimization Empirical mode decomposition

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A review of automatic solar tracking systems

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Journal of Physics: Conference Series 2021年第1期2051卷

作者： A Mohamad H Mhamdi NAM Amin M Izham NA Aziz SY Chionh Mechanical Engineering Program Faculty of Mechanical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Laboratory of Electronic Systems Information Processing Mechanics and Energetics Faculty of Sciences University Ibn Tofail Kenitra Morocco Institute of Engineering Mathematics Faculty of Applied Sciences and Humanities Universiti Malaysia Perlis Perlis Malaysia

Solar tracking systems which can track the Sun movement can increase the power generation rate by maximizing the surface area of the solar panels that are exposed to the sunlight. By utilizing a solar tracker, the number of solar panels needed to generate the same amount of electrical energy will be significantly lower. In general, solar tracking systems are classified as single-axis solar tracking systems and dual-axis solar tracking systems. Several researchers had conducted both simulation and experimental work to compare and evaluate the performance of solar tracking systems against static solar panels systems, as well as between different solar tracking system mechanisms. General agreements among the researchers are that the performance of solar tracking systems is always exceeding that of static solar panels. Some researchers also conducted studies on how the weather conditions affect the performance of solar panels, and they concluded that the systems with tracking mechanisms are more resilient toward a variation of weather conditions.

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Corrigendum: Moving Next-Generation Phase-Field Models to BMS Applications -A Case Study that Confirms Professor Uzi Landau's Foresight [J. Electrochem. Soc., 171, 063507 (2024)]

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Journal of The Electrochemical Society 2024年第8期171卷 089002-089002页

作者： Tushar K. Telmasre Anthony César Concepción Suryanarayana Kolluri Lubhani Mishra Raghav S. Thiagarajan Aditya Naveen Matam Akshay Subramaniam Taylor R. Garrick Venkat R. Subramanian Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 United States of America Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 United States of America Battery Materials and Systems Research General Motors Global Research and Development Center Warren MI 48090 United States of America Chandra Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712 United States of America Energy Storage and Propulsion R&D and Manufacturing Engineering General Motors Technical Center Warren MI 48092 United States of America Affiliate Faculty Oden Institute for Computational Science and Engineering The University of Texas at Austin Austin TX 78712 United States of America

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Application of Emerging Innovations in Microbiome Science to Space Development and Settlement systems 73

Application of Emerging Innovations in Microbiome Science to...

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73rd International Astronautical Congress, IAC 2022

作者： Nastasi, Nicholas Anderson, Matthew Z. Balasubrahmaniam, Neeraja Bope, Ashleigh Britt, Rodney D. Cochran, Samuel J. Gill, Samuel J. Hegarty, Bridget Lee, Jiyoung Stamps, Blake W. Stickney, Amanda Taylor, Christopher G. Tram, Nguyen K. Van Dusen, John Varaljay, Vanessa A. Weir, Mark H. Horack, John M. Oglesbee, Michael Sullivan, Matthew B. Dannemiller, Karen C. Department of Civil Environmental & Geodetic Engineering Ohio State University ColumbusOH United States Division of Environmental Health Sciences College of Public Health Ohio State University ColumbusOH United States Environmental Sciences Graduate Program Ohio State University ColumbusOH United States Department of Microbiology College of Arts and Sciences Ohio State University ColumbusOH United States Department of Microbial Infection and Immunity Ohio State University ColumbusOH United States Center for Perinatal Research The Abigail Wexner Research Institute Nationwide Children's Hospital Department of Pediatrics Ohio State University ColumbusOH United States Department of Civil and Environmental Engineering Case School of Engineering Case Western Reserve University ClevelandOH United States Department of Food Science and Technology Ohio State University ColumbusOH United States Infectious Diseases Institute Ohio State University ColumbusOH United States Soft Matter Materials Branch Materials and Manufacturing Directorate Air Force Research Laboratory Wright - Patterson AFBOH United States Department of Plant Pathology College of Food Agriculture and Environmental Science Ohio State University ColumbusOH United States Department of Anesthesiology & Pain Medicine Nationwide Children's Hospital ColumbusOH United States Airman Systems Directorate 711th Human Performance Wing Air Force Research Laboratory WPAFBOH United States Sustainability Institute Ohio State University ColumbusOH United States Department of Mechanical and Aerospace Engineering College of Engineering John Glenn College of Public Affairs Ohio State University ColumbusOH United States Department of Veterinary Biosciences College of Veterinary Medicine Ohio State University ColumbusOH United States Center of Microbiome Science Ohio State University ColumbusOH United States

Future human spaceflight beyond low-Earth orbit will bring substantial changes and new requirements to the development of fully sustainable, healthy indoor human environments across a range of gravitational, radiative, and thermal conditions. One key aspect is understanding (and potentially leveraging) microorganisms that must coexist with humans, plants, and animals in enclosed habitats (their microbiota or microbiomes). Modern, genome-resolved microbiome science provides an opportunity to better understand how we can utilize microbes to optimize space missions and protect human health and well-being long-term. Microorganisms will be critical to mission success due to their direct or indirect interactions with 1) humans, 2) other living systems (e.g., plant and animal life as a food source and for experimentation), and 3) the built environment. For example, the human gut microbiome is a complex community that has important implications for human health and performance that include mental health, nutrition intake, and immune system function. The health of plant microbiomes is also critical for sustainable, dependable, and nutritious food production. Humans, plants, and other living systems (e.g., animals used in experimentation) will also shed microbes into the built environment, which may impact human exposure, plant health, system integrity, and planetary protection from microbial contamination in both forward and reverse directions. Microbes from all three systems will interact, resulting in potentially important 'non-linear' system implications that must be understood for the overall long-term sustainability of hardware, crew, and other living systems. This paper discusses recent advances in technology that expand measurement capabilities to ultra-low-biomass samples and now allow for better understanding of microbial taxa, community structure and drivers, and functional capacity, which together will empower microbiome-optimized deep-space human exploration miss

关键词： Bacteria

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SYNAPTIC SPINE HEAD MORPHODYNAMICS FROM GRAPH GRAMMAR RULES FOR ACTIN DYNAMICS

arXiv

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arXiv 2025年

作者： Hur, Matthew Bartol, Thomas Rangamani, Padmini Sejnowski, Terrence Mjolsness, Eric Program in Mathematical Computational and Systems Biology Univeristy of California Irvine IrvineCA United States Computational Neurobiology Laboratory Salk Institute for Biological Studies La Jolla CA United States Departments of Pharmacology and Mechanical and Aerospace Engineering University of California San Diego La Jolla CA United States Deparment of Neurobiology University of California San Diego La Jolla CA United States Departments of Computer Science and Mathematics Univeristy of California Irvine IrvineCA United States

There is a morphodynamic component to synaptic learning by which changes in dendritic (postsynaptic) spine head size are associated with the strengthening or weakening of the synaptic connection between two neurons, in response to the temporal correlation of local presynaptic and postsynaptic signals. These morphological factors are in turn sculpted by the dynamics of the actin cytoskeleton. In this paper, we use Dynamical Graph Grammars (DGGs) implemented within a computer algebra system to model how networks of actin filaments can dynamically grow or shrink, reshaping the spine head. Dynamical Graph Grammars (DGGs) provide a well-defined way to accommodate dynamically changing system structure such as active cytoskeleton represented using dynamic graphs, within nonequilibrium statistical physics under the master equation. We show that DGGs can also incorporate biophysical forces between graph-connected objects at a finer time scale, with specialized DGG kinetic rules obeying biophysical constraints of Galilean invariance, conservation of momentum, and dissipation of conserved global energy. We use graph-local energy functions for cytoskeleton networks interacting with membranes, and derive DGG rules from the specialization of dissipative stochastic dynamics - separated into dissipative and thermal noise rule types - to a mutually exclusive and exhaustive collection of graph-local neighborhood types for the rule left hand sides. The dissipative rules comprise a stochastic version of gradient descent dynamics. The thermal noise rules use a Gaussian approximation of each position coordinate to sample jitter-like displacements. For the spine head model we designed and implemented DGG grammar mathematical sub-models including actin network growth, non-equilibrium statistical mechanics, and filament-membrane mechanical interaction to regulate the re-writing of graph objects. We simulate emergent biophysics of simplified networks of actin polymers and their interactions

关键词： Stochastic models

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The evolution of lithium-ion battery recycling

Nature Reviews Clean Technology

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Nature Reviews Clean Technology 2025年第1期1卷

作者： Xiaotu Ma Zifei Meng Yan Wang Marilena Velonia Bellonia Jeffrey Spangenberger Gavin Harper Eric Gratz Elsa Olivetti Renata Arsenault Mechanical and Materials Engineering Department Worcester Polytechnic Institute Worcester MA USA Technology and Policy Program Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA ReCell Center Argonne National Laboratory Lemont IL USA School of Metallurgy and Materials University of Birmingham Birmingham UK The Faraday Institution Harwell Science and Innovation Campus Didcot UK Ascend Elements Inc. Westborough MA USA Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA USA Ford Ion Park Battery Center of Excellence Romulus MI USA

Demand for lithium-ion batteries (LIBs) is increasing owing to the expanding use of electrical vehicles and stationary energy storage. Efficient and closed-loop battery recycling strategies are therefore needed, which will require recovering materials from spent LIBs and reintegrating them into new batteries. In this Review, we outline the current state of LIB recycling, evaluating industrial and developing technologies. Among industrial technologies, pyrometallurgy can be broadly applied to diverse electrode materials but requires operating temperatures of over 1,000 °C and therefore has high energy consumption. Hydrometallurgy can be performed at temperatures below 200 °C and has material recovery rates of up to 93% for lithium, nickel and cobalt, but it produces large amounts of wastewater. Developing technologies such as direct recycling and upcycling aim to increase the efficiency of LIB recycling and rely on improved pretreatment processes with automated disassembly and cleaner mechanical separation. Additionally, the range of materials recovered from spent LIBs is expanding from the cathode materials recycled with established methods to include anode materials, electrolytes, binders, separators and current collectors. Achieving an efficient recycling ecosystem will require collaboration between recyclers, battery manufacturers and electric vehicle manufacturers to aid the design and automation of battery disassembly lines.

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