Gauge invariance induced relations and the equivalence between distinct approaches to NLSM amplitudes

In this paper, we derive generalized Bern-Carrasco-Johansson relations for color-ordered Yang-Mills amplitudes by imposing gauge invariance conditions and dimensional reduction appropriately on the new discovered graphic expansion of Einstein-Yang-Mills amplitudes. These relations are also satisfied by color-ordered amplitudes in other theories such as color-scalar theory, bi-scalar theory and nonlinear sigma model (NLSM). As an application of the gauge invariance induced relations, we further prove that the three types of BCJ numerators in NLSM , which are derived from Feynman rules, Abelian Z-theory and Cachazo-He- Yuan formula respectively, produce the same total amplitudes. In other words, the three distinct approaches to NLSM amplitudes are equivalent to each other.Comment: 40pages, 2 figure

Glass Forming Ability in Pr-(Cu, Ni)-Al Alloys

Glass forming ability (GFA) in the Pr-rich Pr-(Cu, Ni)-Al alloys at or near the eutectic points was systematically studied. It was found that the GFA in the pseudo-ternary alloys of Pr-(Cu, Ni)-Al is higher than that of the ternary alloys of Pr-Cu-Al. Two eutectic compositions in Pr-(Cu, Ni)-Al alloys were found by DSC, namely, Pr₆₈(Cu₀.₅Ni₀.₅)₂₅Al₇ and Pr₅₂(Cu₀.₅Ni₀.₅)₂₅Al₂₃ (at %). The later one shows better GFA than the first one. However, the best GFA was obtained at an off-eutectic composition of Pr₅₄(Cu₀.₅Ni₀.₅)₃₀Al₁₆, which can be formed in fully amorphous rod with diameter of 1.5 mm by copper mould casting. The deviation of the best GFA composition from the eutectic point [Pr₆₈(Cu₀.₅Ni₀.₅)₂₅Al₇] was explained in terms of the asymmetric coupled eutectic zone and the higher glass transition temperature Tg on the hypereutectic side.Singapore-MIT Alliance (SMA

Mining heterogeneous information graph for health status classification

In the medical domain, there exists a large volume of data from multiple sources such as electronic health records, general health examination results, and surveys. The data contain useful information reflecting people’s health and provides great opportunities for studies to improve the quality of healthcare. However, how to mine these data effectively and efficiently still remains a critical challenge. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. By based on analytics of massive data in the National Health and Nutrition Examination Survey, the study builds a classification model to classify patients’health status and reveal the specific disease potentially suffered by the patient. This paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. Moreover, this research contributes to the healthcare community by providing a deep understanding of people’s health with accessibility to the patterns in various observations

InP-Based Antimony-Free MQW Lasers in 2-3 μm Band

Mid-infrared semiconductor lasers in the wavelength range of 2-3 µm have aroused increasing interests as they are highly desired for a wide range of applications ranging from medical diagnostics to environmental sensing. Access to this wavelength range was mainly achieved by antimony-containing compound semiconductor structures on GaSb substrates. Besides, InP-based InxGa1-xAs (x>0.53) type-I multiple quantum well laser is a promising antimony-free approach in this band. The emission wavelength can be tailored to the 2-3 µm band by increasing the indium composition in the quantum wells. During the demonstration of this kind of lasers, controlling the strain and keeping fair structural quality is the main obstacle

A novel classification method combining adaptive local iterative filtering with singular value decomposition for fault diagnosis

As a novel time-frequency analysis method, adaptive local iterative filtering (ALIF) can decompose the time series into several stable components which contain the main fault information. In addition, the amplitude of singular value obtained by singular value decomposition (SVD) can reflect the energy distribution. Naturally, there are certain differences in the energy produced by different faults such as the broken tooth, wearing and normal. Thus, a novel method of mechanical fault classification method based on adaptive local iterative filtering and singular value decomposition is proposed in this paper. Firstly, ALIF method decomposed the original vibration signal into a number of stable components to establish an initial feature vector matrix. Then, the singular values energy corresponding to the feature matrix is employed as a criterion to identify various faults. Compared with the conventional EMD method by simulation experiments, ALIF method has obvious superiority in solving modal aliasing, which is more conducive to the advanced analysis. In this paper, the proposed method is employed to extract the fault information of rolling bearing fault signals from Case Western Reserve University Bearing Data Center. To further verify the effectiveness of the method, the case study is conducted at Drivetrain Diagnostics Simulator. To further illustrate the effectiveness of the method, the results obtained by this method are compared with EMD and EEMD. The results indicated the proposed method performs better in the classification of different mechanical faulty modes