Coarse-grained molecular dynamics simulation of thermal and mechanical behaviors of rocksalt

Coarse-grained molecular dynamics (CG-MD) is a multiscale method for concurrently coupling atomic subdomain and continuum subdomain in a nano/micro material system. In this study, we first present the theoretical framework of multiscale material modeling. Based on that, we derived two numerical algorithms: force-based coarse-grained molecular dynamics (FB-CG-MD) and stiffness-based coarse-grained molecular dynamics (SB-CG-MD). In the first case, we investigate the effect of mesh sizes on accuracy through the bending of a magnesium oxide (MgO) bar. The interatomic interaction of MgO is described by Coulomb–Buckingham potential. The result obtained by classical MD simulation is considered as the standard solution. From CG-MD simulations, we compare the results of different mesh sizes with the standard solution and the error tells the accuracy of both FB-CG-MD and SB-CG-MD. In the second case, we study the heat conduction problem of an MgO specimen, which is subdivided into two subdomains and each subdomain can be either atomic subdomain or continuum subdomain. One subdomain is controlled at a desired temperature through the use of Upgraded Nosé–Hoover Thermostat, which eliminates unphysical phenomena due to reference frame translation and/or rotation when one uses the original Nos é–Hoover thermostat. The other subdomain is free from temperature control. In the CG-MD model, the temperatures of continuum subdomain and atomic subdomain are determined by nodal velocities and atomic velocities, respectively. Our results show that the thermal energy transfers successfully from atomic subdomain to atomic subdomain, as well as from continuum subdomain to continuum subdomain. It is shown that both -FB-CG-MD and SB-CG-MD reduce significantly the number of degrees of freedom in the material system and provide reliable results and improve numerical efficiency

The impact of U.S. quick service on the health and patronage of Chinese urban consumers.

Over the last decade there has been a rapid development of United States quick service restaurant companies such as KFC and McDonalds in China. Increasingly urban Chinese consumers patronize these restaurants as a way to experience American culture. For some it is becoming a part of their eating pattern. Recent health studies have demonstrated that nutritional diseases are increasing in China. This study accessed urban Chinese consumers' perceptions about U.S. quick service restaurants and their knowledge about the nutritional value that U.S. quick service food can provide. This study revealed that Chinese consumers' perceptions and knowledge about U.S. quick service impacts their patronage. Additionally, the study determined correlation between consumer patronage and reported health status as well as consumers' length of patronage negative influence on their health status. The results of this study will help U.S. quick service restaurants in educating consumers on nutrition and improving the menus

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1

SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge

Effectiveness of small-angle episiotomy on incisional laceration rate, suturing time, and incisional bleeding in primigravida: A meta-analysis

ObjectiveTo investigate the effect of small-angle lateral perineal incision on postoperative perineal rehabilitation in primiparous women.MethodThe Cochrane Library, PubMed, Embase, CINAHL, CNKI, WanFang, VIP, and the Chinese Biomedical Literature Database were searched for randomized controlled trials (RCTs) on the effect of small-angle episiotomy on postoperative maternal perineal wound rehabilitation in puerpera until April 3, 2022. Two researchers independently performed literature screening, data extraction and evaluation of risk of bias in the included literature, and statistical analysis of the data was performed using RevMan 5.4 and Stata 12.0 software.ResultA total of 25 RCTs were included, with a total sample of 6,366 cases. Meta-analysis results showed that the use of small-angle episiotomy reduced incisional tearing [OR = 0.32, 95% CI (0.26, 0.39)], shortened incisional suture time [MD = −4.58 min, 95% CI (−6.02, −3.14)] and reduced incisional bleeding [MD = −19.08 mL, 95% CI (−19.53, −18.63)], with statistically significant differences (all p < 0.05). There was no significant difference in the rate of severe laceration between the two groups [OR = 2.32, 95% CI (0.70, 7.70), p > 0.05].ConclusionThe use of a small-angle episiotomy during vaginal delivery can reduce the incision tear rate without increasing the incidence of severe perineal laceration, while shortening the incisional suturing time and reducing incisional bleeding. It can be used clinically according to birth canal conditions of the maternal, the intrauterine condition of the fetus and maternal needs.Systematic Review RegistrationPROSPERO International Prospective Register of Systematic Reviews [CRD42022369698]; [https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=369698]