Comparing a few distributions of transverse momenta in high energy collisions

Transverse momentum spectra of particles produced in high energy collisions are very important due to their relations to the excitation degree of interacting system. To describe the transverse momentum spectra, one can use more than one probability density functions of transverse momenta, which are simply called the functions or distributions of transverse momenta in some cases. In this paper, a few distributions of transverse momenta in high energy collisions are compared with each other in terms of plots to show some quantitative differences. Meanwhile, in the framework of Tsallis statistics, the distributions of momentum components, transverse momenta, rapidities, and pasudorapidities are obtained according to the analytical and Monte Carlo methods. These analyses are useful to understand carefully different distributions in high energy collisions.Comment: 11 pages, 7 figures. Results in Physics, Accepte

Dynamic assessment of curved cable-stayed bridge by model updating

Author's manuscript version. the final published version is available via the publisher at http://dx.doi.org/10.1061/(ASCE)0733-9445(2000)126:2(252). © 2000 American Society of Civil EngineersBridges are indispensable components of the infrastructure of modern society, and their assessment via techniques of structural dynamics is assuming greater importance. This assessment concerns performance of the as-built structure compared to the design and can also extend to the assessment of structural deterioration or damage. Simple validation of numerical results by dynamic testing has met some success; however, feedback from testing into analysis is usually crude, and only recently have systematic techniques been developed that can be supplied to such structures. This paper investigates the application of sensitivity-based model updating technology to the dynamic assessment of the Safti Link Bridge, a curved cable-stayed bridge in Singapore. Based on the measured modal data from prototype testing, the simulated dynamic properties obtained via finite-element analysis have been significantly improved by modification of uncertain structural parameter such as Young’s modulus of concrete and structural geometry

Bridge structural condition assessment using systematically validated finite-element model

Author's manuscript. The final publishe dversion is available from the publisher via http://dx.doi.org/10.1061/(ASCE)1084-0702(2004)9:5(418). Copyright © 2004 American Society of Civil EngineersStructural condition assessment of highway bridges is largely based on visual observations described by subjective indices, and it is necessary to develop methodology for accurate and reliable condition assessment of aging and damaged structures. This paper presents a method using a systematically validated finite element model for quantitative condition assessment of a damaged reinforced concrete bridge deck structure, including damage location and extent, residual stiffness evaluation and loadcarrying capacity assessment. In a trial of the method in a cracked bridge beam, the residual stiffness distribution was determined by model updating thereby locating the damage in the structure. Furthermore the damage extent was identified through a defined damage index and the residual load-carrying capacity was estimated

Civil structure condition assessment by FE model updating: Methodology and case studies

Author's manuscript version. the version of record is available from the publisher via: doi:10.1016/S0168-874X(00)00071-8. Copyright © 2001 Elsevier Science B.V.Development of methodology for accurate and reliable condition assessment of civil structures has become increasingly important. In particular, the finite element (FE) model updating method has been successfully used for condition assessment of bridges. However, the success of applications of the method depends on the analytical conceptualization of complex bridge structures, a well-designed and controlled modal test and an integration of analytical and experimental arts. This paper describes the sensitivity-analysis-based FE model updating method and its application to structure condition assessment with particular reference to bridges, including specific considerations for FE modeling for updating and the model updating procedure for successful condition assessment. Finally, the accuracy analysis of damage assessment by model updating was investigated through a case study. © 2001 Elsevier Science B.V. All rights reserved

Functional characterization of a short peptidoglycan recognition protein from Chinese giant salamander (Andrias davidianus)

This work was supported by the National Natural Science Foundation of China (Grant no. 31302221, 31172408 and 31272666) and Jiangsu Province (Grant no. BK20171274 and BK2011418), and partially by the Opening Project of Jiangsu Key Laboratory of Biochemistry and Biotechnology of Marine Wetland (Grant no. K2016-08). QZ was supported by the “Qinglan” project of Jiangsu province of China.Peer reviewedPublisher PD

Physical Adsorption of Graphene Oxide onto Polymer Latexes and Characterization of the Resulting Nanocomposite Particles

[Image: see text] Polymer/graphene oxide (GO) nanocomposite particles were prepared via heteroflocculation between 140–220 nm cationic latex nanoparticles and anionic GO nanosheets in either acidic or basic conditions. It is demonstrated that nanocomposite particles can be formed using either poly(2-vinylpyridine)-b-poly(benzyl methacrylate) (P2VP–PBzMA) block copolymer nanoparticles prepared by reversible-addition chain-transfer (RAFT)-mediated polymerization-induced self-assembly (PISA), or poly(ethylene glycol)methacrylate (PEGMA)-stabilized P2VP latexes prepared by traditional emulsion polymerization. These two latexes are different morphologically as the P2VP–PBzMA block copolymer latexes have P2VP steric stabilizer chains in their corona, whereas the PEGMA-stabilized P2VP particles have a P2VP core and a nonionic steric stabilizer. Nevertheless, both the P2VP–PBzMA and PEGMA-stabilized P2VP latexes are cationic at low pH. Thus, the addition of GO to these latexes causes flocculation to occur immediately due to the opposite charges between the anionic GO nanosheets and cationic latexes. Control heteroflocculation experiments were conducted using anionic sterically stabilized poly(potassium 3-sulfopropyl methacrylate)-b-poly(benzyl methacrylate) (PKSPMA–PBzMA) and nonionic poly(benzyl methacrylate) (PBzMA) nanoparticles to demonstrate that polymer/GO nanocomposite particles were not formed. The degree of flocculation and the strength of electrostatic interaction between the cationic polymer latexes and GO were assessed using disc centrifuge photosedimentometry (DCP), transmission electron microscopy (TEM), and UV–visible spectrophotometry. These studies suggest that the optimal conditions for the formation of polymer/GO nanocomposite particles were GO contents between 10% and 20% w/w relative to latex, with the latexes containing P2VP in their corona having a stronger electrostatic attraction to the GO sheets

Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits

Variational quantum circuit (VQC) is a promising approach for implementing quantum neural networks on noisy intermediate-scale quantum (NISQ) devices. Recent studies have shown that a tensor-train network (TTN) for VQC, namely TTN-VQC, can improve the representation and generalization powers of VQC. However, the Barren Plateau problem leads to the gradients of the cost function vanishing exponentially small as the number of qubits increases, making it difficult to find the optimal parameters for the VQC. To address this issue, we put forth a new learning approach called Pre+TTN-VQC that builds upon the TTN-VQC architecture by incorporating a pre-trained TTN to alleviate the Barren Plateau problem. The pre-trained TTN allows for efficient fine-tuning of target data, which reduces the depth of the VQC required to achieve good empirical performance and potentially alleviates the training obstacles posed by the Barren Plateau landscape. Furthermore, we highlight the advantages of Pre+TTN-VQC in terms of representation and generalization powers by exploiting the error performance analysis. Moreover, we characterize the optimization performance of Pre+TTN-VQC without the need for the Polyak-Lojasiewicz condition, thereby enhancing the practicality of implementing quantum neural networks on NISQ devices. We conduct experiments on a handwritten digit classification dataset to corroborate our proposed methods and theorems.Comment: 17 pages, 6 figures. In submissio