315 research outputs found
A color prediction model for mending materials of the Yuquan Iron Pagoda in China based on machine learning
No full textThis article was originally published in Heritage Science. The version of record is available at: https://doi.org/10.1186/s40494-024-01295-1. © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/ by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.During the restoration of iron cultural relics, the removal of rust from these artifacts is necessary. However, this rust removal process may lead to inconsistent local color on the iron relics. To address this, mending materials are applied to treat the surface, ensuring consistent local color. In the surface treatment of iron cultural relics, a significant challenge lies in modulating the color of these mending materials. The corrosion products of Yuquan Iron Pagoda are mainly Fe3O4, γ-FeO(OH), α-FeO(OH) and α-Fe2O3, with contents of 13.1, 16.1, 40.2 and 30.6%, respectively. Due to their structural stability and suitable color characteristics, Fe3O4 and α-Fe2O3 are selected as the primary raw materials for the repair material. This study employs machine learning methods to predict the color of mending materials corresponding to varying contents of α-Fe2O3, Fe3O4, and epoxy resin. The Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boost Machine (LightGBM) algorithms are utilized to develop the model, and the predictive performance of these three algorithms is compared. XGBoost exhibits the best prediction performance, achieving a square correlation coefficient (R2) of 0.94238 and a mean absolute error (MAE) of 0.68485. Additionally, the SHapley Additive exPlanations (SHAP) method is employed to analyze the most crucial raw material affecting the color of mending materials, which is identified as Fe3O4. The study illustrates the specific process of employing this model by applying it to the surface treatment of the Yuquan Iron Pagoda, demonstrating the practicality of the model. This model can be applied to assist in the surface treatment of other iron cultural relics.This research was supported by the Open Project of Key Scientific Research Base of the National Cultural Heritage Administration for the Protection of Unearthed Wood Lacquerware (2021H10198, 2023H10017)
Tibetan tectonic evolution inferred from spatial and temporal variations in post-collisional magmatism
No full text
Strong chromatic index of claw-free graphs with edge weight seven
No full textLet be a graph and a positive integer. A strong -edge-coloring of is a mapping such that for any two edges and e^' that are either adjacent to each other or adjacent to a common edge, \phi(e)\ne \phi(e^'). The strong chromatic index of is the minimum integer such that has a strong -edge-coloring. The edge weight of is defined to be , where denotes the degree of in . In this paper, we prove that every claw-free graph with edge weight at most has strong chromatic index at most , which is sharp
Maximum weight
No full textLet t be a nonnegative integer and G = (V(G),E(G)) be a graph. For v ∈ V(G), let NG(v) = {u ∈ V(G) \ {v} : uv ∈ E(G)}. And for S ⊆ V(G), we define dS(G; v) = |NG(v) ∩ S| for v ∈ S and dS(G; v) = −1 for v ∈ V(G) \ S. A subset S ⊆ V(G) is called a t-sparse set of G if the maximum degree of the induced subgraph G[S] does not exceed t. In particular, a 0-sparse set is precisely an independent set. A vector-weighted graph is a graph G with a vector weight function , where for each v ∈ V(G). The weight of a t-sparse set S in is defined as . And a t-sparse set S is a maximum weight t-sparse set of if there is no t-sparse set of larger weight in . In this paper, we propose the maximum weight t-sparse set problem on vector-weighted graphs, which is to find a maximum weight t-sparse set of . We design a dynamic programming algorithm to find a maximum weight t-sparse set of an outerplane graph which takes O((t + 2)4n) time, where n = |V(G)|. Moreover, we give a polynomial-time algorithm for this problem on graphs with bounded treewidth
Proving a conjecture on the upper bound of semistrong chromatic indices of graphs
No full textLet be a graph with maximum degree . For a subset
of , we denote by the subgraph of induced by the
endvertices of edges in . We call a semistrong matching if each edge of
is incident with a vertex that is of degree 1 in . Given a
positive integer , a semistrong -edge-coloring of is an edge coloring
using at most colors in which each color class is a semistrong matching of
. The semistrong chromatic index of , denoted by , is the
minimum integer such that has a semistrong -edge-coloring. Recently,
Lu\v{z}ar, Mockov\v{c}iakov\'a and Sot\'ak conjectured that for any connected graph except the complete bipartite graph
. In this paper, we settle this conjecture by proving that
each such graph other than a cycle on vertices has a semistrong edge
coloring using at most colors.Comment: 20 pages, 9 figure
Developmental history of neurorestoratology
No full textHongyun Huang,1,2 Lin Chen,3,4 Paul R Sanberg5 1Institute of Neurorestoratology, General Hospital of Armed Police Forces, Beijing, People's Republic of China; 2Beijing Rehabilitation Hospital of Capital Medical University, Beijing, People's Republic of China; 3Tsinghua University Yuquan Hospital, Beijing, People's Republic of China; 4Medical Center, Tsinghua University, Beijing, People's Republic of China; 5Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL, USA Abstract: The aim of neurorestoratology is to restore, promote and maintain the integrity of impaired or lost neuronal functions and/or structures, using novel cell-based comprehensive neurorestorative strategies. The purpose of this review is to briefly introduce the developing history of neurorestoratology, which includes neurorestorative strategies, the basis of central nervous system neurorestorable theory, communities in the field of neurorestoratology, and journals related to neurorestoratology. Keywords: neurorestoration, neurorestorative strategies, CNS neurorestorable theor
Subwavelength spinning of particles in vector cosine-Gaussian field with radial polarization
No full textA new type of radially polarized (RP) cosine-Gaussian (CG) field is proposed. Through the analytical model, it is found that such RP CG beam exhibits completely different focusing properties from the reported RP plane waves. More importantly, a stable three-dimensional trap of Rayleigh particle accompanied by a subwavelength spin motion can be easily achieved using this RP CG beam.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Optic
2016 yearbook of neurorestoratology
No full textHongyun Huang,1,2 Gengsheng Mao,1 Shiqing Feng,3 Lin Chen2,4 1Institute of Neurorestoratology, General Hospital of Armed Police Forces, 2Cell Therapy Center, Beijing Hongtianji Neuroscience Academy, Beijing, 3Department of Orthopedic, Tianjin Medical University General Hospital, Tianjin, 4Department of Neurosurgery, Tsinghua University Yuquan Hospital, Beijing, People’s Republic of China Abstract: Neurorestoratology, a new interdisciplinary field, has gradually become a popular clinical discipline. Physicians and scientists in the neurorestoration field have discovered new pathogeneses of nervous system diseases and damage, explored new neurorestorative mechanisms, and obtained improving neurorestorative effects in clinical trials (or therapies). This paper summarizes the major progress achieved over the past year. Keywords: yearbook, neurorestoratology, pathogenesis, neural repair and regeneration, central nervous system disease, neurorestorative mechanisms, neurorestorative strategie
Corporate Social Responsibility (CSR) Activity for Service Brand: Effects of CSR-Brand Fit, Consumer-CSR Fit on Skepticism and Brand Loyalty
No full textBecause of the widespread CSR practice, the quality and outcome of CSR activities vary, and the tendency of consumers to become suspicious of CSR activities is increasing. This paper intends to examine how the antecedents “CSR-brand fit” and “consumer-CSR fit” impact on consumers’ skepticism and how skepticism influences service brand loyalty. A questionnaire is designed with the case “Meituan’s Green Mountain Project” and conducted to examine the relationship of different variables. Adopting survey data methodology, there are 91 Chinese Meituan users participated in the survey, and significant results were generated through simple linear regression analysis in SPSS. The result indicates that the increase in CSR-brand fit and consumer-CSR fit can reduce consumers’ skepticism, and there is no significant relationship between skepticism and brand loyalty. Thus, it is found through the results that enhancing consumers’ perception of the congruity between brand image and CSR activities, and improving the perceived fit between CSR activities and personal relevance, can contribute to minimizing consumers’ skepticism. In this case, the author proposes practical solutions with the functional theory of attitudes and the self-image congruence model. The solutions guide practitioners to pay attention to the compatibility, fit, and logic of content when planning CSR activities, with the aim of reducing the level of skepticism, leading to an effective outcome for brands
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