The Large Hadron-Electron Collider at the HL-LHC

The Large Hadron-Electron Collider (LHeC) is designed to move the field of deep inelastic scattering (DIS) to the energy and intensity frontier of particle physics. Exploiting energy-recovery technology, it collides a novel, intense electron beam with a proton or ion beam from the High-Luminosity Large Hadron Collider (HL-LHC). The accelerator and interaction region are designed for concurrent electron-proton and proton-proton operations. This report represents an update to the LHeC's conceptual design report (CDR), published in 2012. It comprises new results on the parton structure of the proton and heavier nuclei, QCD dynamics, and electroweak and top-quark physics. It is shown how the LHeC will open a new chapter of nuclear particle physics by extending the accessible kinematic range of lepton-nucleus scattering by several orders of magnitude. Due to its enhanced luminosity and large energy and the cleanliness of the final hadronic states, the LHeC has a strong Higgs physics programme and its own discovery potential for new physics. Building on the 2012 CDR, this report contains a detailed updated design for the energy-recovery electron linac (ERL), including a new lattice, magnet and superconducting radio-frequency technology, and further components. Challenges of energy recovery are described, and the lower-energy, high-current, three-turn ERL facility, PERLE at Orsay, is presented, which uses the LHeC characteristics serving as a development facility for the design and operation of the LHeC. An updated detector design is presented corresponding to the acceptance, resolution, and calibration goals that arise from the Higgs and parton-density-function physics programmes. This paper also presents novel results for the Future Circular Collider in electron-hadron (FCC-eh) mode, which utilises the same ERL technology to further extend the reach of DIS to even higher centre-of-mass energies.Peer reviewe

Perspective: Machine Learning in Design for 3D/4D Printing

International audienceAbstract 3D/4D printing offers significant flexibility in manufacturing complex structures with a diverse range of mechanical responses, while also posing critical needs in tackling challenging inverse design problems. The rapidly developing machine learning (ML) approach offers new opportunities and has attracted significant interest in the field. In this perspective paper, we highlight recent advancements in utilizing ML for designing printed structures with desired mechanical responses. First, we provide an overview of common forward and inverse problems, relevant types of structures, and design space and responses in 3D/4D printing. Second, we review recent works that have employed a variety of ML approaches for the inverse design of different mechanical responses, ranging from structural properties to active shape changes. Finally, we briefly discuss the main challenges, summarize existing and potential ML approaches, and extend the discussion to broader design problems in the field of 3D/4D printing. This paper is expected to provide foundational guides and insights into the application of ML for 3D/4D printing design

Machine learning and sequential subdomain optimization for ultrafast inverse design of 4D-printed active composite structures

International audienceShape transformations of active composites (ACs) depend on the spatial distribution and active response of constituent materials. Voxel-level complex material distributions offer a vast possibility for attainable shape changes of 4D-printed ACs, while also posing a significant challenge in efficiently designing material distributions to achieve target shape changes. Here, we present an integrated machine learning (ML) and sequential subdomain optimization (SSO) approach for ultrafast inverse designs of 4D-printed AC structures. By leveraging the inherent sequential dependency, a recurrent neural network ML model and SSO are seamlessly integrated. For multiple target shapes of various complexities, ML-SSO demonstrates superior performance in optimization accuracy and speed, delivering results within second(s). When integrated with computer vision, ML-SSO also enables an ultrafast, streamlined design-fabrication paradigm based on hand-drawn targets. Furthermore, ML-SSO empowered with a splicing strategy is capable to design diverse lengthwise voxel configurations, thus showing exceptional adaptability to intricate target shapes with different lengths without compromising the high speed and accuracy. As a comparison, for the benchmark three-period shape, the finite element method and evolutionary algorithm (EA) method was estimated to need 227 days for the inverse design; the ML-EA achieved design in 57 min; the new ML-SSO with splicing strategy requires only 1.97 s. By further leveraging approximate symmetries, the highly efficient ML-SSO is employed to design active shape changes of 4D-printed lattice structures. The new ML-SSO approach thus provides a highly efficient tool for the design of various 4D-printed, shape-morphing AC structures

Cold-programmed shape-morphing structures based on grayscale digital light processing 4D printing

Abstract Shape-morphing structures that can reconfigure their shape to adapt to diverse tasks are highly desirable for intelligent machines in many interdisciplinary fields. Shape memory polymers are one of the most widely used stimuli-responsive materials, especially in 3D/4D printing, for fabricating shape-morphing systems. They typically go through a hot-programming step to obtain the shape-morphing capability, which possesses limited freedom of reconfigurability. Cold-programming, which directly deforms the structure into a temporary shape without increasing the temperature, is simple and more versatile but has stringent requirements on material properties. Here, we introduce grayscale digital light processing (g-DLP) based 3D printing as a simple and effective platform for fabricating shape-morphing structures with cold-programming capabilities. With the multimaterial-like printing capability of g-DLP, we develop heterogeneous hinge modules that can be cold-programmed by simply stretching at room temperature. Different configurations can be encoded during 3D printing with the variable distribution and direction of the modular-designed hinges. The hinge module allows controllable independent morphing enabled by cold programming. By leveraging the multimaterial-like printing capability, multi-shape morphing structures are presented. The g-DLP printing with cold-programming morphing strategy demonstrates enormous potential in the design and fabrication of shape-morphing structures

Data from: Characterization of cytoplasmic viscosity of hundreds of single tumor cells based on micropipette aspiration

Background: Cytoplasmic viscosity (μc) is a key biomechanical parameter for evaluating the status of cellular cytoskeletons. Previous studies focused on white blood cells, but the data of cytoplasmic viscosity for tumor cells were missing. Methodology: Tumor cells (H1299, A549 and drug-treated H1299 with compromised cytoskeletons) were aspirated continuously through a micropipette at a pressure of -10 kPa or -5 kPa where aspiration lengths as a function of time were obtained and translated to cytoplasmic viscosity based on a theoretical Newtonian fluid model. Quartile coefficients of dispersion were quantified to evaluate the distributions of cytoplasmic viscosity within the same cell type while neural network based pattern recognitions were used to classify different cell types based on cytoplasmic viscosity. Results: The single-cell cytoplasmic viscosity with three quartiles and the quartile coefficient of dispersion were quantified as 16.7 Pa•S, 42.1 Pa•S, 110.3 Pa•S and 74% for H1299 cells at -10 kPa (ncell=652), 144.8 Pa•S, 489.8 Pa•S, 1390.7 Pa•S, and 81% for A549 cells at -10 kPa (ncell=785), 7.1 Pa•S, 13.7 Pa•S, 31.5 Pa•S, and 63% for CD-treated H1299 cells at -10 kPa (ncell=651) and 16.9 Pa•S, 48.2 Pa•S, 150.2 Pa•S, and 80% for H1299 cells at -5 kPa (ncell=600), respectively. Neural network based pattern recognition produced successful classification rates of 76.7% for H1299 vs. A549, 67.0% for H1299 vs. drug-treated H1299 and 50.3% for H1299 at -5 kPa and -10 kPa. Conclusion: Variations of cytoplasmic viscosity were observed within the same cell type and among different cell types, suggesting the potential role of cytoplasmic viscosity in cell status evaluation and cell type classification