1,956 research outputs found

    CEPC Technical Design Report -- Accelerator

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    International audienceThe Circular Electron Positron Collider (CEPC) is a large scientific project initiated and hosted by China, fostered through extensive collaboration with international partners. The complex comprises four accelerators: a 30 GeV Linac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to 180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar). The Linac and Damping Ring are situated on the surface, while the Booster and Collider are housed in a 100 km circumference underground tunnel, strategically accommodating future expansion with provisions for a Super Proton Proton Collider (SPPC). The CEPC primarily serves as a Higgs factory. In its baseline design with synchrotron radiation (SR) power of 30 MW per beam, it can achieve a luminosity of 5e34 /cm^2/s^1, resulting in an integrated luminosity of 13 /ab for two interaction points over a decade, producing 2.6 million Higgs bosons. Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons, facilitating precise measurements of Higgs coupling at sub-percent levels, exceeding the precision expected from the HL-LHC by an order of magnitude. This Technical Design Report (TDR) follows the Preliminary Conceptual Design Report (Pre-CDR, 2015) and the Conceptual Design Report (CDR, 2018), comprehensively detailing the machine's layout and performance, physical design and analysis, technical systems design, R&D and prototyping efforts, and associated civil engineering aspects. Additionally, it includes a cost estimate and a preliminary construction timeline, establishing a framework for forthcoming engineering design phase and site selection procedures. Construction is anticipated to begin around 2027-2028, pending government approval, with an estimated duration of 8 years. The commencement of experiments could potentially initiate in the mid-2030s

    X4D-SceneFormer: Enhanced scene understanding on 4D point cloud videos through cross-modal knowledge transfer

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    The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences. However, it remains a challenging task due to the sparsity and lack of texture in point clouds. Moreover, the irregularity of point cloud poses a difficulty in aligning temporal information within video sequences. To address these issues, we propose a novel cross-modal knowledge transfer framework, called X4D-SceneFormer. This framework enhances 4D-Scene understanding by transferring texture priors from RGB sequences using a Transformer architecture with temporal relationship mining. Specifically, the framework is designed with a dual-branch architecture, consisting of an 4D point cloud transformer and a Gradient-aware Image Transformer (GIT). The GIT combines visual texture and temporal correlation features to offer rich semantics and dynamics for better point cloud representation. During training, we employ multiple knowledge transfer techniques, including temporal consistency losses and masked self-attention, to strengthen the knowledge transfer between modalities. This leads to enhanced performance during inference using singlemodal 4D point cloud inputs. Extensive experiments demonstrate the superior performance of our framework on various 4D point cloud video understanding tasks, including action recognition, action segmentation and semantic segmentation. The results achieve 1st places, i.e., 85.3% (+7.9%) accuracy and 47.3% (+5.0%) mIoU for 4D action segmentation and semantic segmentation, on the HOI4D challenge, outperforming previous state-of-the-art by a large margin.We release the code at https://github.com/jinglinglingling/X4D</p

    CEPC Technical Design Report -- Accelerator

    No full text
    International audienceThe Circular Electron Positron Collider (CEPC) is a large scientific project initiated and hosted by China, fostered through extensive collaboration with international partners. The complex comprises four accelerators: a 30 GeV Linac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to 180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar). The Linac and Damping Ring are situated on the surface, while the Booster and Collider are housed in a 100 km circumference underground tunnel, strategically accommodating future expansion with provisions for a Super Proton Proton Collider (SPPC). The CEPC primarily serves as a Higgs factory. In its baseline design with synchrotron radiation (SR) power of 30 MW per beam, it can achieve a luminosity of 5e34 /cm^2/s^1, resulting in an integrated luminosity of 13 /ab for two interaction points over a decade, producing 2.6 million Higgs bosons. Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons, facilitating precise measurements of Higgs coupling at sub-percent levels, exceeding the precision expected from the HL-LHC by an order of magnitude. This Technical Design Report (TDR) follows the Preliminary Conceptual Design Report (Pre-CDR, 2015) and the Conceptual Design Report (CDR, 2018), comprehensively detailing the machine's layout and performance, physical design and analysis, technical systems design, R&D and prototyping efforts, and associated civil engineering aspects. Additionally, it includes a cost estimate and a preliminary construction timeline, establishing a framework for forthcoming engineering design phase and site selection procedures. Construction is anticipated to begin around 2027-2028, pending government approval, with an estimated duration of 8 years. The commencement of experiments could potentially initiate in the mid-2030s

    X4D-SceneFormer: Enhanced scene understanding on 4D point cloud videos through cross-modal knowledge transfer

    No full text
    The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences. However, it remains a challenging task due to the sparsity and lack of texture in point clouds. Moreover, the irregularity of point cloud poses a difficulty in aligning temporal information within video sequences. To address these issues, we propose a novel cross-modal knowledge transfer framework, called X4D-SceneFormer. This framework enhances 4D-Scene understanding by transferring texture priors from RGB sequences using a Transformer architecture with temporal relationship mining. Specifically, the framework is designed with a dual-branch architecture, consisting of an 4D point cloud transformer and a Gradient-aware Image Transformer (GIT). The GIT combines visual texture and temporal correlation features to offer rich semantics and dynamics for better point cloud representation. During training, we employ multiple knowledge transfer techniques, including temporal consistency losses and masked self-attention, to strengthen the knowledge transfer between modalities. This leads to enhanced performance during inference using singlemodal 4D point cloud inputs. Extensive experiments demonstrate the superior performance of our framework on various 4D point cloud video understanding tasks, including action recognition, action segmentation and semantic segmentation. The results achieve 1st places, i.e., 85.3% (+7.9%) accuracy and 47.3% (+5.0%) mIoU for 4D action segmentation and semantic segmentation, on the HOI4D challenge, outperforming previous state-of-the-art by a large margin.We release the code at https://github.com/jinglinglingling/X4D</p

    CEPC Technical Design Report -- Accelerator

    No full text
    International audienceThe Circular Electron Positron Collider (CEPC) is a large scientific project initiated and hosted by China, fostered through extensive collaboration with international partners. The complex comprises four accelerators: a 30 GeV Linac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to 180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar). The Linac and Damping Ring are situated on the surface, while the Booster and Collider are housed in a 100 km circumference underground tunnel, strategically accommodating future expansion with provisions for a Super Proton Proton Collider (SPPC). The CEPC primarily serves as a Higgs factory. In its baseline design with synchrotron radiation (SR) power of 30 MW per beam, it can achieve a luminosity of 5e34 /cm^2/s^1, resulting in an integrated luminosity of 13 /ab for two interaction points over a decade, producing 2.6 million Higgs bosons. Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons, facilitating precise measurements of Higgs coupling at sub-percent levels, exceeding the precision expected from the HL-LHC by an order of magnitude. This Technical Design Report (TDR) follows the Preliminary Conceptual Design Report (Pre-CDR, 2015) and the Conceptual Design Report (CDR, 2018), comprehensively detailing the machine's layout and performance, physical design and analysis, technical systems design, R&D and prototyping efforts, and associated civil engineering aspects. Additionally, it includes a cost estimate and a preliminary construction timeline, establishing a framework for forthcoming engineering design phase and site selection procedures. Construction is anticipated to begin around 2027-2028, pending government approval, with an estimated duration of 8 years. The commencement of experiments could potentially initiate in the mid-2030s

    Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey

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    Background: Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid excretion, which leads to hyperuricemia. To decrease hyperuricemia-related CVD, this study aimed to identify the risk of hyperuricemia in people taking aspirin.Method: The original data of this cross-sectional study were obtained from the National Health and Nutrition Examination Survey between 2011 and 2018. Participants who filled in the “Preventive Aspirin Use” questionnaire with a positive answer were included in the analysis. Six machine learning algorithms were screened, and eXtreme Gradient Boosting (XGBoost) was employed to establish a model to predict the risk of hyperuricemia.Results: A total of 805 participants were enrolled in the final analysis, of which 190 participants had hyperuricemia. The participants were divided into a training set and testing set at a ratio of 8:2. The area under the curve for the training set was 0.864 and for the testing set was 0.811. The SHapley Additive exPlanations (SHAP) method was used to evaluate the performances of the modeling. Based on the SHAP results, the feature ranking interpretation showed that the estimated glomerular filtration rate, body mass index, and waist circumference were the three most important features for hyperuricemia in individuals taking aspirin. In addition, triglyceride, hypertension, total cholesterol, high-density lipoprotein, low-density lipoprotein, age, race, and smoking were also correlated with the development of hyperuricemia.Conclusion: A predictive model established by XGBoost algorithms can potentially help clinicians make an early detection of hyperuricemia risk in people taking low-dose aspirin

    An intelligent recommendation method for coal mine accident case via ontology knowledge service

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    Abstract Coal mine accidents, for example, water leakage and collapse, can damage the circuit system, and they in turn can affect the stable operation of the coal mine. Therefore, it is necessary to identify the causes of coal mine accidents and reduce the number of accidents in coal mines. Government and enterprises have already accumulated numerous coal mine accident cases. To summarize the characteristics and rules of accidents, survey the deep cause of the accident, avoid the recurrence of similar accidents, and early pre‐control of risk source, there is a need to analyze the correlation in the accident characteristics. Then, with the help of ontology knowledge service model, we address this issue in this article. For guaranteeing the inference efficiency of ontology knowledge, we propose a semiautomatic construction method for coal mine accident cases to construct ontology. Here, the reliability of ontology construction and the professionalism of domain knowledge provide a feasible approach to ontology learning using structured and semi‐structured data sources. Furthermore, the weighted Word2vec and spectral clustering method are combined, an intelligent recommendation algorithm with accident ontology is accordingly presented, while presenting a local optimal distance calculation similarity strategy. This method can serve as an assistant to help users mine similar coal mine accident cases. Finally, the experimental results show that after comparing with some other popular methods, the proposed approach can achieve a satisfactory performance on the coal mine dataset with an accuracy of 99.47%, the precision of 98.92%, and the F1‐score of 99.35

    Interface structure and tensile failure behaviour of novel SiCf/Ti–Ti2AlNb hybrid laminated composite material

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    Inspired by the microstructures of shells, SiC fibers (SiCf) are introduced into Ti layers, creating hybrid layers of SiCf/Ti, which are combined with Ti2AlNb foils to prepare multilayer SiCf/Ti–Ti2AlNb hybrids in a hot-pressed sintering instrument. The solid-phase bonding interface, mainly including the α+β biphasic structures and B2-rich phases, guarantees an ideal interface connection between the SiCf/Ti and Ti2AlNb layers after the sintering. Herein, the SiC fibers-made SiCf/Ti–Ti2AlNb presents a higher tensile strength and elasticity modulus on the tensile instrument of Model No. Byes (2010), compared with Ti/Ti2AlNb. During the tensile process, the brittle fractures of SiCf first occur, and the loads are transferred to the bonding interface of the Ti2AlNb and Ti layers, generating the crack propagation and resulting in SiCf/Ti–Ti2AlNb fracture. This is mainly because the crack is extended in succession to the bonding interface that is composed of brittle phases such as O and α2, as well as ductile phases such as α, β and B2. This produces the ductile and brittle ruptures at the bonding interface, forming the ductile-brittle mixture fractures at the SiCf/Ti and Ti2AlNb interfaces. In the case of vertical fractures related to the fiber length, brittle fractures are observed on the SiC fibers, which produce high forces that affect their bonding interfaces, resulting in axial interstices and transverse fractures. These obtained results demonstrate a novel method for structural design and fabrication of the innovative composite materials

    UHPLC-MS/MS for plasma lamotrigineanalysis and comparison with a homogenousenzyme immunoassay: supplementary data

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    Aims: To develop and validate a UHPLC-MS/MS method for lamotrigine (LTG) analysis in human plasmaand evaluate its agreement with a homogenous enzyme immunoassay (HEIA). Materials & methods: TheUHPLC-MS/MS method was developed and validated according to the USFDA/EMA guidelines. A Bland–Altman plot was used to evaluate the agreement between UHPLC-MS/MS and HEIA. Results: Samples werepretreated with one-step protein precipitation and separated in 2.6 min. The intra- and inter-day biasand imprecisions were -15.8 to 15.0% and less than 11.17%, respectively. The recovery and matrix factorwere 98.30 to 111.97%. The mean overestimation of UHPLC-MS/MS compared with HEIA was 21.57%.Conclusion: A rapid, sensitive and robust UHPLC-MS/MS method for plasma LTG analysis was developedand validated and was a 21.57% overestimation compared with HEIA.</p

    CEPC Technical Design Report -- Accelerator