Identification of Extreme Temperature Fluctuation in Blast Furnace Based on Fractal Time Series Analysis

In this study, we aim to estimate the density distribution for the return intervals of extreme temperature fluctuation in blast furnace during iron making process. We first identified the fractal feature of the data based on R/S analysis and also calculated the Hurst coefficient. Secondly, based on the fractal feature of the data, we estimated a stretched exponential distribution of the return intervals of extreme temperature fluctuation. Finally, in order to test the result, we applied this method to the data of two blast furnaces, and compared with the traditional kernel density estimation method. The comparison was based on 100,000 times K-S test. The comparison results showed that the fractal time series estimation provides a greater fitness than traditional estimation method since it has no rejection in K-S test. With this method, the density of return intervals of unexpected temperature fluctuation can be estimated. This can be applied as a tool of temperature control and also can be applied as a tool to evaluate the efficiency of the control system

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 Heterogeneity of Investors Based on Multi-fractal Features with Ultra-High Frequency Data

In the financial markets, the heterogeneity of investors is mostly focusing on very different underlying assets. However, there is one specific heterogeneity need to be discussed, that is the heterogeneity represented by investors who are investing in very similar underlying assets. In another word, whether there is a method to quantitatively describe the heterogeneity when investors have the same expectation in the future. In order to detect this kind of heterogeneity we introduced multi-fractal feature values and select SSE (Shanghai Security Exchange) 50 Index and its derivatives, SSE 50 Index ETF (Exchanged Tradable Fund) and SSE 50 Index Future to research on this topic, since these three underlying assets presented very similar fluctuation during the same period. With the static scenario analysis and dynamic analysis we successfully find that the multi-fractal feature values are not only able to detect this heterogeneity but also be able to describe it quantitatively. The false nearest point test had shown that the multi-fractal values are necessary and rational in this process. The other advantage by introducing multi-fractal values is that they are quantitative numbers which could be applied to models directly

Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis

Put forward a novel combination forecasting method (M-ARIMA-BP) that could make a more accurate and concise prediction of stock market based on wavelet multiresolution analysis. This innovative method operated by parsing of the low-frequency trend series and the high-frequency volatility series of stock market and gives an insight into the price series. Using the daily closing price data of SSE (Shanghai Stock Exchange) Composite Index and Shenzhen Component Index as samples, compared with conventional wavelet prediction model, ARIMA model, and BP neural network model, the empirical results show that the new algorithm M-ARIMA-BP can improve the accuracy of volatility forecasting and perform better in predicting prices rising and falling

Computer-Aided Diagnosis of Alzheimer’s Disease via Deep Learning Models and Radiomics Method

This paper focused on the problem of diagnosis of Alzheimer’s disease via the combination of deep learning and radiomics methods. We proposed a classification model for Alzheimer’s disease diagnosis based on improved convolution neural network models and image fusion method and compared it with existing network models. We collected 182 patients in the ADNI and PPMI database to classify Alzheimer’s disease, and reached 0.906 AUC in training with single modality images, and 0.941 AUC in training with fusion images. This proved the proposed method has better performance in the fusion images. The research may promote the application of multimodal images in the diagnosis of Alzheimer’s disease. Fusion images dataset based on multi-modality images has higher diagnosis accuracy than single modality images dataset. Deep learning methods and radiomics significantly improve the diagnosing accuracy of Alzheimer’s disease diagnosis

Performance Analysis on the Small-Scale Reusable Launch Vehicle

According to the symmetrical characteristics of a new type of Reusable Launch Vehicle (RLV) in the recovery phase, we studied the basic aerodynamic model data of Starship and the aerodynamic data with rudder deflection, and the causes of its aerodynamic coefficients are expounded. At the same time, we analyzed its stability and maneuverability. According to the flying quality requirements, the lateral-directional model of Starship in the return phase at a high angle of attack is analyzed. Finally, we analyzed the lateral heading stability and control deviation of Starship by using the criterion and nonlinear open-loop simulations. The results show that the Starship has pitching and rolling stability, but it only has heading stability in some ranges of angle of attack, and there is no heading stability at a conventional large angle of attack. At the same time, after modal analysis and comparison of flight quality, it can be seen that the longitudinal long-period model of the starship degenerates into a real root and it is stable and convergent. The lateral heading roll mode is at level 2 flight quality, the helical mode is at level 1 flight quality, and the Dutch roll mode diverges, which needs to be stabilized and controlled later

Performance Analysis on the Small-Scale Reusable Launch Vehicle

According to the symmetrical characteristics of a new type of Reusable Launch Vehicle (RLV) in the recovery phase, we studied the basic aerodynamic model data of Starship and the aerodynamic data with rudder deflection, and the causes of its aerodynamic coefficients are expounded. At the same time, we analyzed its stability and maneuverability. According to the flying quality requirements, the lateral-directional model of Starship in the return phase at a high angle of attack is analyzed. Finally, we analyzed the lateral heading stability and control deviation of Starship by using the criterion and nonlinear open-loop simulations. The results show that the Starship has pitching and rolling stability, but it only has heading stability in some ranges of angle of attack, and there is no heading stability at a conventional large angle of attack. At the same time, after modal analysis and comparison of flight quality, it can be seen that the longitudinal long-period model of the starship degenerates into a real root and it is stable and convergent. The lateral heading roll mode is at level 2 flight quality, the helical mode is at level 1 flight quality, and the Dutch roll mode diverges, which needs to be stabilized and controlled later

The Role of EGFR/PI3K/Akt/cyclinD1 Signaling Pathway in Acquired Middle Ear Cholesteatoma

Cholesteatoma is a benign keratinizing and hyper proliferative squamous epithelial lesion of the temporal bone. Epidermal growth factor (EGF) is one of the most important cytokines which has been shown to play a critical role in cholesteatoma. In this investigation, we studied the effects of EGF on the proliferation of keratinocytes and EGF-mediated signaling pathways underlying the pathogenesis of cholesteatoma. We examined the expressions of phosphorylated EGF receptor (p-EGFR), phosphorylated Akt (p-Akt), cyclinD1, and proliferating cell nuclear antigen (PCNA) in 40 cholesteatoma samples and 20 samples of normal external auditory canal (EAC) epithelium by immunohistochemical method. Furthermore, in vitro studies were performed to investigate EGF-induced downstream signaling pathways in primary external auditory canal keratinocytes (EACKs). The expressions of p-EGFR, p-Akt, cyclinD1, and PCNA in cholesteatoma epithelium were significantly increased when compared with those of control subjects. We also demonstrated that EGF led to the activation of the EGFR/PI3K/Akt/cyclinD1 signaling pathway, which played a critical role in EGF-induced cell proliferation and cell cycle progression of EACKs. Both EGFR inhibitor AG1478 and PI3K inhibitor wortmannin inhibited the EGF-induced EGFR/PI3K/Akt/cyclinD1 signaling pathway concomitantly with inhibition of cell proliferation and cell cycle progression of EACKs. Taken together, our data suggest that the EGFR/PI3K/Akt/cyclinD1 signaling pathway is active in cholesteatoma and may play a crucial role in cholesteatoma epithelial hyper-proliferation. This study will facilitate the development of potential therapeutic targets for intratympanic drug therapy for cholesteatoma