“Reporting or Interpreting?”—A Discoursal Study of Broadcasts on NBA Games in China

From the perspective of empirical discourse analysis, this paper identifies the site broadcasters’ roles and cognitive blending process in NBA (National Basketball Association) broadcasts in China. The authors find that NBA broadcasters chiefly interpret the information they have obtained from sports sites and interviews with the coaches and players, employing various interpreting strategies, such as commentary, amplification, supplementation and restructure. Cognitively, the language that NBA broadcasters applied reveals their cognitive blending process of interpreting techniques, strategies, sports knowledge and attitudes towards the games, of who take up different roles to fulfill different communicating purposes, all of which project various cognitions on NBA games. Despite the fact that one role might make certain linguistic behaviors prevail over the others, especially their interpreting role, NBA site broadcasters coordinate it with other roles properly through which they present different levels of translational and constructional schematicity, thus yielding a coherent and constructional working mode of NBA broadcasting practice in China

Fault diagnosis using an improved fusion feature based on manifold learning for wind turbine transmission system

In this paper, a novel fault diagnosis method based on vibration signal analysis is proposed for fault diagnosis of bearings and gears. Firstly, the ensemble empirical mode decomposition (EEMD) is used to decompose the vibration signal into several subsequences, and a multi-entropy (ME) is proposed to make up the fusion features of the vibration signal. Secondly, an improved manifold learning algorithm, local and global preserving embedding (LGPE), is applied to compress the high-dimensional fusion feature set into a two-dimension feature set. Finally, according to the clustering accuracy of different feature set, the fault classification and diagnosis can be performed in the reduced two-dimension space. The performance of the proposed technique is tested on the fault of wind turbine transmission system. The application results indicate that the proposed method can achieve high accuracy of fault diagnosis

Elucidating Charge Separation Dynamics in a Hybrid Metal–Organic Framework Photocatalyst for Light-Driven H\u3csub\u3e2\u3c/sub\u3e Evolution

Metal–organic frameworks (MOFs) have emerged as novel scaffolds for artificial photosynthesis due to their unique capability in incorporating homogeneous photosensitizer and catalyst to their robust heterogeneous matrix. In this work, we report the charge separation dynamics between molecular Ru-photosensitizer and Pt-catalyst, both of which were successfully incorporated into a Zr-MOF that demonstrates excellent activity and stability for light-driven H2 generation from water. Using optical transient absorption (OTA) spectroscopy, we show that charge separation in this hybrid MOF occurs via electron transfer (ET) from Ru-photosensitizer to Pt-catalyst. Using Pt L3-edge X-ray transient absorption (XTA) spectroscopy, we observed the intermediate reduced Pt site, directly confirming the formation of charge separated state due to ET from Ru-photosensitizer and unraveling their key roles in photocatalysis

Floquet Engineering of Magnetism in Topological Insulator Thin Films

Dynamic manipulation of magnetism in topological materials is demonstrated here via a Floquet engineering approach using circularly polarized light. Increasing the strength of the laser field, besides the expected topological phase transition, the magnetically doped topological insulator thin film also undergoes a magnetic phase transition from ferromagnetism to paramagnetism, whose critical behavior strongly depends on the quantum quenching. In sharp contrast to the equilibrium case, the non-equilibrium Curie temperatures vary for different time scale and experimental setup, not all relying on change of topology. Our discoveries deepen the understanding of the relationship between topology and magnetism in the non-equilibrium regime and extend optoelectronic device applications to topological materials

Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM

In order to enhance the capability of feature extraction and fault classification of bearings, this study proposes a feature extraction approach based on dual-tree complex wavelet transform (DTCWT) and permutation entropy (PE), using the fuzzy c means clustering (FCM) to identify fault types. The vibration signal of bearings can be decomposed into several wavelet components with DTCWT which can describe the local characteristics of vibration signals accurately. And the PE of each wavelet component, which can describe the complexity of a time series, is calculated to be regarded as the fault features. Then forming the standard clustering centers by the FCM, we defined a standard using the Hamming approach degree to evaluate the classification results in the FCM. In order to verify the effectiveness of the proposed approach, compared with two other typical signal analysis methods: ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD), through extracting fault features, it required to identify the fault types and severities under variable operating conditions. The experimental results demonstrate that the proposed approach has a better accuracy and performance to diagnose a bearing fault under different fault severities and variable operating conditions. The proposed approach is suitable for a fault diagnosis due to its good ability to the feature extraction and fault classification