Reduced Order Model for Chemical Kinetics: A case study with Primordial Chemical Network

Chemical kinetics plays an important role in governing the thermal evolution in reactive flows problems. The possible interactions between chemical species increase drastically with the number of species considered in the system. Various ways have been proposed before to simplify chemical networks with an aim to reduce the computational complexity of the chemical network. These techniques oftentimes require domain-knowledge experts to handcraftedly identify important reaction pathways and possible simplifications. Here, we propose a combination of autoencoder and neural ordinary differential equation to model the temporal evolution of chemical kinetics in a reduced subspace. We demonstrated that our model has achieved a close-to 10-fold speed-up compared to commonly used astro-chemistry solver for a 9-species primordial network, while maintaining 1 percent accuracy across a wide-range of density and temperature.Comment: 10 pages, 8 figures, accepted to the ICML 2022 Machine Learning for Astrophysics worksho

How Online Diaries Persuade Customers — The Role of Narratives

Online diary is a series of reviews in chronological order generated by customers to record their experience over time, which is a new type of online review emerging in the medical beauty industry. This study extends narrative transportation theory to explore the effect of the dynamic structure of online diaries on persuasion. We posit that emotional shift and utilitarian value can positively enhance online diary persuasion through improving transportation, and the relationship between the temporal flow and persuasion is converse U shape. The moderating role of social influence and visual content richness to the main effect is also investigated in this study. We collected real data to test our hypotheses utilizing Natural Language Processing (NLP) method and econometric model. This study is expected to make both theoretical and practical contributions

Single and double stereoselective fluorination of (E)-allylsilanes

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Acyclic allylic monofluorides were prepared by electrophilic fluorination of branched (E)-allylsilanes with Selectfluor. These reactions proceeded with efficient transfer of chirality from the silylated to the fluorinated stereocentre. Upon double fluorination, an unsymmetrical ethyl syn-2,5-difluoroalk-3-enoic ester was prepared, the silyl group acting as an anti stereodirecting group for the two C-F bond forming events.Published versio

Understanding and Measuring Computational Thinking: A Tripartite View of Competence

Computation is critical to social development for information processing in today’s digital world. Computational Thinking (CT), the underlying cognitive functioning of designing computation, has been under heated discussion. However, the conceptualization of CT and its measurements still require improvement when discussing CT as a competence for problem-solving. This study first conceptualizes CT in problem-solving context by identifying the framework of competence based on a tripartite view and applying it to clarify that CT competence consists of CT knowledge, skills, and attitudes. It then aims to develop a self-reported scale for complementing CT measurements based on the tripartite view. This work contributes to CT theories and measures through a more precise conceptual framework and an instrument developed based on it. It enriches IS studies by a new perspective that humans form their cognitive process for problem-solving under digital technologies, especially computational tools

The relationship of circulating fibroblast growth factor 21 levels with pericardial fat: The Multi-Ethnic Study of Atherosclerosis.

Previous small studies have reported an association between circulating fibroblast growth factor 21 (FGF21) levels and pericardial fat volume in post-menopausal women and high cardiovascular disease (CVD) risk patients. In this study, we investigated the relationship of FGF21 levels with pericardial fat volume in participants free of clinical CVD at baseline. We analysed data from 5765 men and women from the Multi-Ethnic Study of Atherosclerosis (MESA) with both pericardial fat volume and plasma FGF21 levels measured at baseline. 4746 participants had pericardial fat volume measured in at least one follow-up exam. After adjusting for confounding factors, ln-transformed FGF21 levels were positively associated with pericardial fat volume at baseline (β = 0.055, p < 0.001). When assessing change in pericardial fat volume over a mean duration of 3.0 years using a linear mixed-effects model, higher baseline FGF21 levels were associated with higher pericardial fat volume at baseline (2.381 cm3 larger in pericardial fat volume per one SD increase in ln-transformed FGF21 levels), but less pericardial fat accumulation over time (0.191 cm3/year lower per one SD increase in ln-transformed FGF21 levels). Cross-sectionally, higher plasma FGF21 levels were significantly associated with higher pericardial fat volume, independent of traditional CVD risk factors and inflammatory markers. However, higher FGF21 levels tended to be associated with less pericardial fat accumulation over time. Nevertheless, such change in pericardial fat volume is very modest and could be due to measurement error. Further studies are needed to elucidate the longitudinal relationship of baseline FGF21 levels with pericardial fat accumulation

Disorder raises the critical temperature of a cuprate superconductor

With the discovery of charge density waves (CDW) in most members of the cuprate high temperature superconductors, the interplay between superconductivity and CDW has become a key point in the debate on the origin of high temperature superconductivity. Some experiments in cuprates point toward a CDW state competing with superconductivity, but others raise the possibility of a CDW-superconductivity intertwined order, or more elusive pair-density wave (PDW). Here we have used proton irradiation to induce disorder in crystals of La$_{1.875}$Ba$_{0.125}$CuO$_4$ and observed a striking 50% increase of $T_\mathrm{c}$ accompanied by a suppression of the CDW. This is in clear contradiction with the behaviour expected of a d-wave superconductor for which both magnetic and non-magnetic defects should suppress $T_\mathrm{c}$. Our results thus make an unambiguous case for the strong detrimental effect of the CDW on bulk superconductivity in La$_{1.875}$Ba$_{0.125}$CuO$_4$. Using tunnel diode oscillator (TDO) measurements, we find evidence for dynamic layer decoupling in PDW phase. Our results establish irradiation-induced disorder as a particularly relevant tuning parameter for the many families of superconductors with coexisting density waves, which we demonstrate on superconductors such as the dichalcogenides and Lu$_5$Ir$_4$Si$_{10}$.Comment: 10 pages, 7 figure

A comparison of admission and worst 24-hour Acute Physiology and Chronic Health Evaluation II scores in predicting hospital mortality: a retrospective cohort study

INTRODUCTION: The Acute Physiology and Chronic Health Evaluation (APACHE) II score is widely used in the intensive care unit (ICU) as a scoring system for research and clinical audit purposes. Physiological data for calculation of the APACHE II score are derived from the worst values in the first 24 hours after admission to the ICU. The collection of physiological data on admission only is probably logistically easier, and this approach is used by some ICUs. This study compares the performance of APACHE II scores calculated using admission data with those obtained from the worst values in the first 24 hours. MATERIALS AND METHODS: This was a retrospective cohort study using prospectively collected data from a tertiary ICU. There were no missing physiological data and follow-up for mortality was available for all patients in the database. The admission and the worst 24-hour physiological variables were used to generate the admission APACHE II score and the worst 24-hour APACHE II score, and the corresponding predicted mortality, respectively. RESULTS: There were 11,107 noncardiac surgery ICU admissions during 11 years from 1 January 1993 to 31 December 2003. The mean admission and the worst 24-hour APACHE II score were 12.7 and 15.4, and the derived predicted mortality estimates were 15.5% and 19.3%, respectively. The actual hospital mortality was 16.3%. The overall discrimination ability, as measured by the area under the receiver operating characteristic curve, of the admission APACHE II model (83.8%, 95% confidence interval = 82.9–84.7) and the worst 24-hour APACHE II model (84.6%, 95% confidence interval = 83.7–85.5) was not significantly different (P = 1.00). CONCLUSION: Substitution of the worst 24-hour physiological variables with the admission physiological variables to calculate the admission APACHE II score maintains the overall discrimination ability of the traditional APACHE II model. The admission APACHE II model represents a potential alternative model to the worst 24-hour APACHE II model in critically ill nontrauma patients