A Reaction Between High Mn-High Al Steel and CaO-SiO2-Type Molten Mold Flux: Part I. Composition Evolution in Molten Mold Flux

In order to elucidate the reaction mechanism between high Mn-high Al steel such as twin-induced plasticity steel and molten mold flux composed mainly of CaO-SiO2 during continuous casting process, a series of laboratory-scale experiments were carried out in the present study. Molten steel and molten flux were brought to react in a refractory crucible in a temperature range between 1713 K to 1823 K (1440 A degrees C to 1550 A degrees C) and composition evolution in the steel and the flux was analyzed using inductively coupled plasma atomic emission spectroscopy, X-ray fluorescence, and electron probe microanalysis. The amount of SiO2 in the flux was significantly reduced by Al in the steelthus, Al2O3 was accumulated in the flux as a result of a chemical reaction, 4[Al] + 3(SiO2) = 3[Si] + 2(Al2O3). In order to find a major factor which governs the reaction, a number of factors ((pct CaO/pct SiO2), (pct Al2O3), [pct Al], [pct Si], and temperature) were varied in the experiments. It was found that the above chemical reaction was mostly governed by [pct Al] in the molten steel. Temperature had a mild effect on the reaction. On the other hand, (pct CaO/pct SiO2), (pct Al2O3), and [pct Si] did not show any noticeable effect on the reaction. Apart from the above reaction, the following reactions are also thought to happen simultaneously: 2[Mn] + (SiO2) = [Si] + 2(MnO) and 2[Fe] + (SiO2) = [Si] + 2(FeO). These oxide components were subsequently reduced by Al in the molten steel. Na2O in the molten flux was gradually decreased and the decrease was accelerated by increasing [pct Al] and temperature. Possible reactions affecting the Al2O3 accumulation are summarized.ope

Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

Advances in pultiple-pulse radio-frequency-gradient imaging of solids

Magnetic resonance imaging (MRI) has become the premier tool for the non-destructive evaluation of soft tissue in living systems [1]. Established liquid-state MRI strategies are generally found to be inappropriate for the imaging of rigid solids, because the linewidth for nuclear magnetic resonance in solids is orders-of-magnitude larger than in liquids. Methods currently under development for the NMR imaging of solids either involve the use of very large (fringe-field) magnetic field gradients to encode spatial information over very short periods of time [2], or employ multiple-pulse line-narrowing techniques that prolong a solid’s apparent transverse relaxation time [3–7]. In the latter methods, the magnetic field gradients may be much weaker, but must generally be pulsed synchronously with the line-narrowing sequence. The benefits of implementing this are improved sensitivity and spectroscopic resolution

Health literacy, health status, and healthcare utilization of Taiwanese adults: results from a national survey

Abstract Background Low health literacy is considered a worldwide health threat. The purpose of this study is to assess the prevalence and socio-demographic covariates of low health literacy in Taiwanese adults and to investigate the relationships between health literacy and health status and health care utilization. Methods A national survey of 1493 adults was conducted in 2008. Health literacy was measured using the Mandarin Health Literacy Scale. Health status was measured based on self-rated physical and mental health. Health care utilization was measured based on self-reported outpatient clinic visits, emergency room visits, and hospitalizations. Results Approximately thirty percent of adults were found to have low (inadequate or marginal) health literacy. They tended to be older, have fewer years of schooling, lower household income, and reside in less populated areas. Inadequate health literacy was associated with poorer mental health (OR, 0.57; 95% CI, 0.35-0.91). No association was found between health literacy and health care utilization even after adjusting for other covariates. Conclusions Low (inadequate and marginal) health literacy is prevalent in Taiwan. High prevalence of low health literacy is not necessarily indicative of the need for interventions. Systematic efforts to evaluate the impact of low health literacy on health outcomes in other countries would help to illuminate features of health care delivery and financing systems that may mitigate the adverse health effects of low health literacy.http://deepblue.lib.umich.edu/bitstream/2027.42/78252/1/1471-2458-10-614.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78252/2/1471-2458-10-614.pdfPeer Reviewe

A trial for the use of qigong in the treatment of pre and mild essential hypertension: a study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Hypertension is a risk factor for cardiovascular disease, and the prevalence of hypertension tends to increase with age. Current treatments for hypertension have side effects and poor adherence. Qigong has been studied as an alternative therapy for hypertension; however, the types of qigong used in those studies were diverse, and there have not been many well-designed randomized controlled trials.</p> <p>Our objectives are the following: 1) To evaluate the effects of qigong on blood pressure, health status and hormone levels for pre- or mild hypertension. 2) To test the methodological appropriateness of this clinical trial and calculate a sample size for future randomized trials.</p> <p>Methods</p> <p>Forty subjects with pre- or mild hypertension will be randomized to either the qigong exercise group or the non-treated group. Participants in the qigong group will conduct qigong exercises 5 times per week for 8 weeks, and participants in the non-treated group will maintain their current lifestyle, including diet and exercise. The use of antihypertensive medication is not permitted. The primary endpoint is a change in patient blood pressure. Secondary endpoints are patient health status (as measured by the SF-36 and the MYMOP2 questionnaires) and changes in hormone levels, including norepinephrine, epinephrine, and cortisol.</p> <p>Discussion</p> <p>This study will be the first randomized trial to investigate the effectiveness of qigong exercises for the treatment of pre- and mild hypertension. The results of this study will help to establish the optimal approach for the care of adults with pre- or mild hypertension.</p> <p>Trial registration</p> <p>Clinical Research Information Service KCT0000140</p

Dopaminergic Differentiation of Human Embryonic Stem Cells on PA6-Derived Adipocytes.

Human embryonic stem cells (hESCs) are a promising source for cell replacement therapies. Parkinson's disease is one of the candidate diseases for the cell replacement therapy since the motor manifestations of the disease are associated with the loss of dopaminergic neurons in the substantia nigra pars compacta. Stromal cell-derived inducing activity (SDIA) is the most commonly used method for the dopaminergic differentiation of hESCs. This chapter describes a simple, reliable, and scalable dopaminergic induction method of hESCs using PA6-derived adipocytes. Coculturing hESCs with PA6-derived adipocytes markedly reduces the variable outcomes among experiments. Moreover, the colony differentiation step of this method can also be used for the dopaminergic induction of mouse embryonic stem cells and NTERA2 cells as well