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Uncovering the Isothermal Transformation Dynamics Behavior and Mechanism of Reversed Austenite in Ph13-8mo Steel

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SSRN 2023年

作者： Cui, Puchang Wang, Yuqi Jia, Changhao Wang, Hongli Bai, Qingqing Zhang, Zhihong Zheng, Huaibei Lai, Zhonghong Liu, Yong Zhu, Jingchuan School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China Chengdu Advanced Metal Materials Industrial Technology Institute Co. Ltd. Chengdu610300 China National Key Laboratory for Precision Hot Processing of Metals Harbin Institute of Technology Harbin150001 China Center for Analysis Measurement and Computing Harbin Institute of Technology Harbin150001 China State Key Laboratory of Metal Material for Marine Equipment and Application Anshan114009 China

The isothermal transformation kinetics and microstructural evolution of the reversed austenite have a significant influence on the properties of PH13-8Mo steel. However, the microscopic phase transformation mechanism during isothermal processes for PH13-8Mo steel needs further examination. Herein, we systematically investigate the isothermal transformation behavior and mechanism of reversed austenite in PH13-8Mo steel. Phase transformation kinetic measurements unravel that the complete elimination of residual austenite can be achieved. Only hierarchical martensitic microstructure without austenite was detected after the solution-cooling treatment at 927 °C with a cooling rate of 10 °C/s, determining the critical martensite start and end temperature to be 176.6 °C, 11.6 °C. The isothermal kinetics of reverted austenite satisfies the Johnson-Mehl-Avrami relationship. The fraction of reversed austenite increases from 0.14% at 510 °C to 8.73% at 593 °C. The lath martensite boundaries with large kernel average misorientation values promote elemental diffusion at the interface. Further, the underlying mechanisms of diffusion-controlled dynamics are elucidated during the isothermal aging process. The interfacial model indicates the growing width of the reversed austenite up to 100 nm and the compositional gradient formed by the elemental diffusion during aging for 0-5 h at 593 °C. The interdiffusion coefficient calculations interpret the diffusion direction of alloying element Ni from martensite to reversed austenite;Cr and Mo may form alloying carbides at the austenite-martensite interface;Al possesses the fastest diffusivity in martensite and austenite phases. The present work unveils the phase transformation kinetics of PH13-8Mo steel, achieving quantitative regulation for maraging stainless steel. © 2023, The Authors. All rights reserved.

关键词： Martensite

Insight into the Microstructure and Micromechanical Behavior and Mechanism of the Constitutional Phase of Fenicral0.63 High Entropy Alloy

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SSRN 2023年

作者： Cui, Puchang Zhou, Fei Wang, Wei Nong, Zhisheng Lai, Zhonghong Liu, Yong Zhu, Jingchuan School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China Harbin Institute of Technology China State Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi’an710072 China School of Materials Science and Engineering Shenyang Aerospace University Shenyang110136 China Center for Analysis Measurement and Computing Harbin Institute of Technology Harbin150001 China National Key Laboratory for Precision Hot Processing of Metals Harbin Institute of Technology Harbin150001 China

The mechanical properties of quaternary Fe-Cr-Ni-Al alloys have been extensively studied due to of their potential application in high temperature structural components. However, the work hardening mechanism originating from the two BCC phase states is not clear. Herein, we systematically investigate the constituent phases and the micromechanical behavior of the FeCrNiAlx (x=0.63, 0.71, 0.77) alloys. Dual-phase microstructural characterizations indicate that the ordered BCC2 phase is enriched in Ni-Al elements and the disordered BCC1 phase is enriched in Fe-Cr elements, giving rise to an increase in the segregation ratio of dual phases. The work-hardening exponents of the BCC1 and BCC2 phases were evaluated to be 0.459 and 0.464, implying the difference of work hardening behavior and coordinate deformation of the two BCC phases. Meanwhile, the constitutive relationships including the modified Ludwik description of the work-hardening behavior of Al0.63 alloy are investigated by nanoindentation measurements. Results show that the outstanding work-hardening response is associated with coordinated deformation of the soft and hard two BCC phases. Furthermore, the first-principles calculation illustrates that the stronger bonding strength of the BCC2 phase contributes to the higher work hardening capability than the BCC1 phase. © 2023, The Authors. All rights reserved.

关键词： Strain hardening

Three factors make bulk high-entropy alloys as effective electrocatalysts for oxygen evolution

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材料展望（英文）

材料展望（英文） 2023年第4期 110-120页

作者： Tao Zhang Hui-Feng Zhao Ke-Yan Wang Zhen-Jie Chen Li Li Jing Peng Xu Peng Yong-Jiang Huang Hai-Bin Yu Wuhan National High Magnetic Field Center & School of Physics Huazhong University of Science and TechnologyWuhan 430074People's Republic of China School of Materials Science and Engineering Center of Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001People's Republic of China Faculty of Materials Science and Energy Engineering/Institute of Technology for Carbon Neutrality Shenzhen 518055People's Republic of China Shenzhen Key Laboratory of Energy Materials for Carbon Neutrality Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055People's Republic of China College of Chemistry and Chemical Engineering Hubei UniversityWuhan 430062People's Republic of China

Even in their bulk forms,complex alloys like high-entropy alloys(HEAs)exhibit favorable activity and stability as electrocatalysts for the oxygen evolution reaction(OER).However,the underlying reasons are not yet fully *** a family of Mo-doped CrFeCoNi-based HEAs,we have identified three crucial factors that govern their performance:(ⅰ)homogeneous solid solution phase of HEAs helps to maintain high-valence states of metals;(ⅱ)surface reconstruction results in a hybrid material comprising amorphous domains and percolated crystalline structures;(ⅲ)diversity of active intermediate species(M-O,M-OOH,and,notably,the abundance of superoxide μ-OO),which display stronger adsorption capacity on the reconstructed *** results are revealing due to their resemblance to findings in other families of electrocatalysts for OER,as well as their unique features specific to *** line with these factors,a CrFeCoNiMo0.2 bulk integrated electrode displays a low overpotential of 215 mV,rapid kinetics,and long-term stability of over 90 *** HEAs hold great potential for industrial applications.

关键词： high-entropy alloy solid solution electrocatalysis DFT calculation oxygen evolution reaction

New Strategy in Light-Weight and Ultrastrong Ti40nb15mo30(Nbc)15 Refractory Complex Concentrated Alloy

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SSRN 2024年

作者： Lu, Weihang Liu, Dong Wang, Cunyu An, Qi Ritchie, Robert O. Huang, Lujun Jin, Shenbao Gao, Naonao Sun, Liangbo Geng, Lin School of Materials Science and Engineering Harbin Institute of Technology P. O. Box 433 Harbin150001 China Stake Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin150001 China Department of Engineering Science University of Oxford OxfordOX1 3PJ United Kingdom Department of Materials Science and Engineering University of California BerkeleyCA94720 United States School of Materials Science and Engineering Hebei University of Technology Tianjin300401 China Center of Analysis Measurement and Computing Harbin Institute of Technology Harbin150001 China

Traditional refractory high-entropy alloys (RHEAs) generally exhibit a trade-off between high-temperature strength and light weight. In present work, a novel design strategy based on tailoring element distribution is proposed to achieve excellent high-temperature strength at a density lower than 7 g·cm-3. Specifically, a Ti40Nb15Mo30(NbC)15 composite was designed and prepared by powder metallurgy. The composite is found to be composed of two ultrafine-grained (UFG) phases including a body-centered cubic (bcc) solid-solution phase and a face-centered cubic (fcc) ceramic reinforcement phase (Ti, Nb)C. The as-sintered composite shows a uniform and UFG microstructure where two phases are interconnected. Due to this unique microstructure, the Ti40Nb15Mo30(NbC)15 composite displays the highest specific yield strengths among the surveyed RHEAs, complex concentrated alloys, and metal-matrix composites from 800℃ (243 MPa·g-1·cm3) to 1000℃ (127 MPa·g-1·cm3). The outstanding high-temperature compressive strength was found to be associated with high resistance to dislocation motion and strong dislocation interactions in both the bcc and fcc phases. The phase interface after hot compression remained semi-coherent, vindicating its high stability. The high-density of stable phase interfaces not only retards the dislocation motion due to the large image force near the phase boundary but also induces a high value of activation energy for diffusion. The high activation energy can further achieve significant microstructure stability even after a long-term annealing (36 h) at 1000℃. This work provides new perspectives for the design and application of light and ultrastrong refractory complex concentrated alloys (RCCAs) by comparison to the insufficient strength of many traditional and light RCCAs. © 2024, The Authors. All rights reserved.

关键词： High strength alloys

Unveiling the Precipitate Evolutions and Relationships between the Nano-Precipitates and Mechanical Properties in Ph13-8mo Stainless Steel

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SSRN 2022年

作者： Cui, Puchang Yu, Shifan Zhou, Fei Wang, Hongli Bai, Qingqing Zhang, Zhihong Zheng, Huaibei Lai, Zhonghong Liu, Yong Zhu, Jingchuan School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China National Key Laboratory for Precision Hot Processing of Metals Harbin Institute of Technology Harbin150001 China Center for Analysis Measurement and Computing Harbin Institute of Technology Harbin150001 China State Key Laboratory for Environment-friendly Energy Materials School of Materials Science and Engineering Southwest University of Science and Technology Mianyang621010 China Chengdu Advanced Metal Materials Industrial Technology Institute Co. Ltd. Chengdu610300 China State Key Laboratory of Metal Material for Marine Equipment and Application Anshan114009 China

Precipitation hardening maraging steels possess of great importance with attractive properties in various industrial applications. However, a major confusion about the fine precipitate evolutions and the relationships between the strengthened particles and mechanical properties of PH13-8Mo steels under various heat treatment conditions remains to be further investigated. To shed light on the direct relationships between the fine nano-phase and mechanical properties, a combination of thermodynamics predictions and experimental measurements were carried out to model the issues. The investigation results reveal that the coherent NiAl nano-precipitates distributed on the lath-like martensite matrix firstly presented a steady growth and then a significant coarsening of approximately 9 nm at 593 °C for 5 h. The acicular or block-like diffusion-controlled reverted austenite enriched in Ni element with a tendency of growth was clarified, which maintains the K-S orientation relationship with martensite matrix after diverse aging treatments. The current studied PH13-8Mo steel displays comparable mechanical properties despite over-aging conditions, and the sharp drops in hardness and the steady increment in impact energy were systematically examined. The excellent work hardening behaviors of the present steel were modeled, which indicates that the modified Ludwik model displays significantly improved agreement with the experimental data than the widely accepted Hollomon model. The five strengthening mechanism models are discussed and the C-O-M model exhibits high consistency with the experimental data with a value of 652 MPa. The growth of nano-precipitates maybe prohibits the dislocations from cutting the particles, which promotes the hardening behaviors. This work offers a valuable reference for further quantitively experimental illustrates. © 2022, The Authors. All rights reserved.

关键词： Coarsening

Breaking the strength-ductility trade-off in metastable β21s alloy via high silicon content and heterogeneous lamellar architecture

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International Journal of Plasticity 2025年 190卷

作者： Hao Ding Xiping Cui Xiuwen Ren Jiamu Liu Zhiqi Wang Yuanyuan Zhang Naonao Gao Yihong He Wei Ye Kanghe Jiang Mao Liu Rui Zhang Xiangxin Zhai Junfeng Chen Lin Geng Lujun Huang School of Materials Science and Engineering Harbin Institute of Technology Harbin 150001 PR China Hunan Aviation Powerplant Research Institute Aero Engine (Group) Corporation of China Zhuzhou 412002 PR China Center for Analysis Measurement and Computing Harbin Institute of Technology Harbin 150001 PR China School of Materials Science and Engineering Northeastern University Shenyang 110167 PR China School of Materials Science and Engineering Fuzhou University Fuzhou 350116 PR China

The metastable β21S titanium alloy faces significant challenges in practical applications due to its insufficient yield strength and restricted uniform elongation. While silicon addition has proven effective in enhancing mechanical properties of titanium alloys, conventional wisdom restricts Si content to ≤0.5 wt.% to avoid embrittlement from coarse silicide formation. This study challenges this paradigm through innovative alloy design, incorporating 0.9 wt.% Si combined with isothermal treatment and hot extrusion to create a heterogeneous lamellar structured (HLS) β21S-Si alloy. Our approach achieves dual microstructural control: isothermal pretreatment induces ∼10 nm nanowire silicide precursors that refine final precipitates to 230 nm (from 700 nm in conventional processing), while subsequent extrusion disrupts continuous grain boundary silicides and constructs a well-defined heterogeneous lamellar architecture comprising recrystallized and substructured lamellae. The optimized HLS β21S-Si exhibits remarkable mechanical performance, demonstrating a 1035 MPa yield strength (10% enhancement) and 12% uniform elongation (8 × improvement) compared to baseline β21S. Multiscale characterization combining SEM-DIC and first-principles calculations reveals a unique sequential work-hardening mechanism: heterogeneous deformation-induced (HDI) hardening dominates early stages, followed by silicon-promoted cross-slip activity, culminating in stress-induced ω phase transformation during advanced deformation. This synergistic interplay of microstructure-engineered deformation mechanisms establishes a new pathway for overcoming the persistent strength-ductility trade-off in metastable β-Ti alloys, with significant implications for aerospace applications demanding high-performance structural materials.

关键词： Metastable beta-Ti alloy High Si content Heterogeneous lamellar structure Deformation mechanisms

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

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arXiv

arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

Why is the Winner the Best?

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Why is the Winner the Best?

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

Correction: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

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Journal of neuroengineering and rehabilitation 2024年第1期21卷 71页

作者： M Encarna Micó-Amigo Tecla Bonci Anisoara Paraschiv-Ionescu Martin Ullrich Cameron Kirk Abolfazl Soltani Arne Küderle Eran Gazit Francesca Salis Lisa Alcock Kamiar Aminian Clemens Becker Stefano Bertuletti Philip Brown Ellen Buckley Alma Cantu Anne-Elie Carsin Marco Caruso Brian Caulfield Andrea Cereatti Lorenzo Chiari Ilaria D'Ascanio Bjoern Eskofier Sara Fernstad Marcel Froehlich Judith Garcia-Aymerich Clint Hansen Jeffrey M Hausdorff Hugo Hiden Emily Hume Alison Keogh Felix Kluge Sarah Koch Walter Maetzler Dimitrios Megaritis Arne Mueller Martijn Niessen Luca Palmerini Lars Schwickert Kirsty Scott Basil Sharrack Henrik Sillén David Singleton Beatrix Vereijken Ioannis Vogiatzis Alison J Yarnall Lynn Rochester Claudia Mazzà Silvia Del Din Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne UK. Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine The University of Sheffield Sheffield UK. Laboratory of Movement Analysis and Measurement Ecole Polytechnique Federale de Lausanne Lausanne Switzerland. Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen Nürnberg Erlangen Germany. Center for the Study of Movement Cognition and Mobility Neurological Institute Tel Aviv Sourasky Medical Center Tel Aviv Israel. Department of Biomedical Sciences University of Sassari Sassari Italy. Department of Electronics and Telecommunications Politecnico di Torino Turin Italy. National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC) Newcastle University and The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. Robert Bosch Gesellschaft für Medizinische Forschung Stuttgart Germany. The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. School of Computing Newcastle University Newcastle Upon Tyne UK. Barcelona Institute for Global Health (ISGlobal) Barcelona Spain. Universitat Pompeu Fabra Barcelona Catalonia Spain. CIBER Epidemiología y Salud Pública (CIBERESP) Madrid Spain. Insight Centre for Data Analytics University College Dublin Dublin Ireland. School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland. Department of Electrical Electronic and Information Engineering «Guglielmo Marconi» University of Bologna Bologna Italy. Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV) University of Bologna Bologna Italy. Grünenthal GmbH Aachen Germany. Department of Neurology University Medical Center Schleswig-Holstein Campus Kiel Kiel Germany. Sagol School of Neuroscien