Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Analysis of bubble flow in metallurgical operations using multivariate statistical techniques

The behaviour of bubbles within metallurgical vessels is important to crucial aspects of their operations, such as mass transfer, heat transfer and splash generation. Physical models have been used to investigate different aspects of bubbling and provide data for verification of mathematical models. In industry, spout eye area, which is formed while the gas escapes from the liquid surface during bottom gas stirring process, has been used to monitor the process. Vibration signals on the wall of vessels have also been measured in industry to monitor the gas flow. Sound signals of the bubbling have been correlated with different behaviour of the gas bubbles inside the bath, such as bubble formation, distortion, coalescence, volumetric oscillation and detachment. It is clear that these three types of signal, i.e. image from the disturbed top surface, sound of the bubbling and vibration on the wall of the vessel are all generated from the same physical process, and indicate some aspect of the bubbling phenomena. This study focuses on the investigation of the combined effect of all these three types of signals, which were collected simultaneously in well controlled cold model experiments, based on multivariate statistical analysis techniques. The aim of this study is to investigate the possibility of monitoring bubble flow with a combined signal, which depends on the variables that can be reliably measured, and if possible, how to simplify this combined signal from the large data base which carries all the information of the process. Cold modelling experiments were performed to establish techniques to analyse all these three types of signals simultaneously and quickly. A cylindrical cold model with a diameter of 420 mm and height of 500 mm, based on both dimensional and dynamic similarity criteria, was used to collect different types of signals simultaneously over a wide range of flowing conditions. The depth of the water bath which simulates steel was kept at 210 mm, and motor oil, which simulated slag, the height varied from 5 mm to 20 mm. Pressured gas were injected from the bottom of the vessel through a nozzle with a diameter of 3 mm, and the volume flow rate varied from 2.0 l/min to 20.0 l/min. Images of the disturbed top surface and sound of the bubbling signals were collected by a digital video camera installed above the vessel and vibration signals were collected by an accelerometer installed on the wall of the vessel. The size of the spout eye area was calculated by a threshold technique developed in this study, which takes approximately 0.1 second to analyse each frame of the image files in average, and the sound and vibration signals were pre-treated in both time domain and frequency domain. Principal Component Analysis (PCA) technique was applied in this study to investigate the data base collected from the cold model experiments. The results from PCA demonstrated that the three types of signals are highly correlated and can be combined into one latent variable, which explains most (about 86%) of the total variation of the cold model experiments, and this latent variable can indicate the stirring process inside the bath effectively, because there exists a clear linear relationship (R2=0.96) between this latent variable (dominant principal component) and stirring power which was calculated from the same cold model data. Since there are established relationships between the overall mass transfer coefficients and inclusion removal rate with stirring power, it should be possible to predict the metallurgical operations inside the bath using this latent variable. The possibility of indicating the stirring process by just one or two channels of signals was further investigated and the PCA results showed that the signals from just one channel can only provide limited indication of the system, however, the combined signal from sound and vibration can capture most of the variation of the process (about 88% of the total variation except the image signals), and there is also a clear linear relationship (R2=0.95) between stirring power and the latent variable which combined the signals from sound and vibration. This finding suggests new type of sensor can be developed, which can be applied to monitor the gas stirring process and provide a feedback signal for the control system, based on combination of vibration and sound signals alone. This finding is particularly important for the pyrometallurgical operations where it is difficult to install a digital camera above the vessel. The relationship between stirring power and Froude number, which is generally applied as a dynamic criteria for the gas bubbling phenomena, was investigated and the results showed that there is a clear linear relationship between the combined signal and Froude number specifically defined. A new variable 'BF (bubbling factor)' was defined in this study based on the combined signal of sound intensity and vibration magnitude. PCA results based on the cold model data showed that BF can capture most variation of the process (91.3%), and a strong linear relationship was found between bubbling factor and stirring power (R2=0.92), which demonstrate that BF can monitor the bubble flow effectively and can be applied to predict the metallurgical operations inside the bath. Additionally, statistical analysis based on the cold model data showed that the sampling period can be reduced to 2.0 seconds to collect sufficient information about the stirring process inside the bath, which means that the bubbling factor can give a feed back signal every two seconds. These findings should facilitate the development of online sensors that monitor the stirring process quickly and effectively, based on sound and vibration signals from the process

Neural-net-based predictive modeling of spout eye size in steelmaking

Gas stirring is commonly used in pyrometallurgical vessels to enhance mass and heat transfer and to promote impurity removal. In the case of secondary steelmaking, the spout eye area is caused by the escape of the gas from the top of the smelt where the liquid steel is directly exposed to the air, and oxygen can be picked up through the spout eye area that can reduce the quality of steel. Thus, controlling the size of the spout eye area is very important to improving the quality of the steel and to keeping the consistency of the product. The set of prevailing models to predict spout eye size are based on specific practically difficult variables, e.g., height of slag in hot upper layer of vessels and gas flow rate at nozzle exit. Recently, the cold model results showed that the stirring process can be conveniently monitored by the signals such as (1) the image signal from the top of the vessel, (2) the sound of the stirring process, and (3) the vibration on the wall of the vessel. This article outlines the key details of a novel research investigation using neural-network-based predictive modeling such as general regression neural networks (GRNN) with genetic adaptive calibrations. Predictive capacities and generalization potentials of five model constructs (i.e., with different sets of input parameters) were explored, and the neural net modeling yielded encouraging outcomes, e.g., (1) excellent goodness-of-fit generalization measures including high values of correlation and R 2 validation parameters (e.g., r = 0.921 and R 2 = 0.845 in a model validation), and (2) low values of root mean square of errors (e.g., 3.034). Overall, the research outlined in this article demonstrates that the spout eye size can be effectively predicted by predictive neural net modeling with convenient and practically measurable variables such as sound and vibration observations on the steelmaking vessels. These results have only been demonstrated for a cold model of the process, and further work is required to show that this approach can be extended to industrial operations

Online analysis of bubbling phenomena

Bubbling is commonly used in the chemical and mineral refining processes to enhance temperature and chemical composition homogeneity, and control mass transfer and heat transfer. In the case of high temperature operations, it is very difficult to measure directly the effect of stirring on the system and thus the control of such processes is dominated by manual procedures. In the case of bottom bubbling processes involving two fluids (e.g. steel and slag in ladle refining operations), the disturbance of the surface of the top layer ('spout eye') provides a visual indication of the bubbling process and is accompanied by sound and vibration signals that all originate from the same phenomena and are clearly closely related to each other. In this study, we construct an experimental rig using five depths of the secondary layer (5-25mm) and 10 flow gas rates (2-20lpm) to simulate the secondary steelmaking process. Images from the top view, sound and vibration signals were collected simultaneously and they were manipulated into a state matrix after pre-treatment. The state matrix carries all the information which can be reliably measured from the bubbling phenomena. In this study, we propose to analyse the matrix using the PCA technique as means to reduce the dimensions of the matrix and hopefully produce a useful control signal

Influence of Real-Time Heating on Mechanical Behaviours of Rocks

The rock mechanical properties under the effect of high temperature present a great significance on underground rock engineering. In this paper, the mechanical properties of sandstones, marbles, and granites under real-time heating were investigated with a servo-controlled compression apparatus. The results show that mechanical behaviours of all the three types of rocks are influenced by real-time heating to different degrees. Due to thermal cracking, the uniaxial compressive strengths decrease as the heating temperature rises from room temperature to 400°C. Above 400°C, the sandstone exhibits a significant increase in UCS because of the sintering reaction. The sintering enlarges the contact area and friction between crystal grains in the sandstone, which strengthens the bearing capacity. For marbles, the UCS continues to decrease from 400°C to 600°C due to thermal cracking. However, the carbonate in the marble begins to decompose after 600°C. The generated particles would fill the cracks in the marble and increase the strength. For granites, their UCS presents a sharp decline after 400°C because of thermal cracking. For all rock elastic modulus, they present a decreasing trend, and this indicates that the rock’s ability to resist deformation gradually weakens under the effect of temperature. In general, rock mechanical behaviours under real-time heating differ from those in normal situations, and use of the parameters presented here is important for underground rock engineering related to high temperature and can improve the precision in theoretical and numerical analysis

Resolving the Controversial Existence of Silicene and Germanene Nanosheets Grown on Graphite

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