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Phased microphone array technique has received much attention for the localization of sound sources. In this paper, this technique is applied to investigate the airfoil self-noise based on a Computational Aeroacoustics (CAA) database. Two dimensional Direct Numerical Simulation (DNS) is conducted of flow over NACA0012 airfoil at low angles of attack and a moderate Reynolds number. Both the mean flow and acoustics results agree well with previous experimental and numerical data. The numerical data then served as an input for the noise identification and localization process. Three beamfoming algorithms are considered. The results of the phased array calculations show that the tonal noise is the dominant source for NACA0012 airfoil operating at low angles of attack and a moderate Reynolds number. With angle of attack increasing, the tonal sources move upstream and the noise strength is enhanced.

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Why does the Earth rotate? At present, it is impossible to use an experimental device to show which forces cause planets (such as Earth) to rotate in the solar system. Therefore, we developed a device to observe the rotation of a permanent magnet in a magnetic field to achieve an understanding of rotational force. A permanent magnet rotating under the action of a DC motor is installed on Motor Shaft; a permanent magnet designed to rotate in a magnetic field is placed in a circular container with water and floats on the surface of the water. Using the above setup, experimental methods and procedures based on this research can be used to observe the rotational behaviour of a permanent magnet in a magnetic field, understand the reason for its rotation, and determine the strength of the rotational force of the permanent magnet in the magnetic field.

In large-scale complex knowledge graphs (KGs) for knowledge reasoning, most existing methods ignore the hierarchical features of KGs, limiting their efficiency in recommender systems, information retrieval, and intelligent Q&A systems by their discrete nature. Based on the translation-based knowledge graph embedding method, this paper proposes a hierarchical embedding method for large-scale complex KGs, which encodes the semantics of entity representations through continuous bag-of-word (CBOW) and convolutional neural network (CNN) models. At the same time, the hierarchies are used as regularization terms to constrain the learning of entity embeddings. After that, the model joint loss function is designed to combine the descriptive loss function of the entity and the hierarchical loss function through the equilibrium coefficients, and the model optimal equilibrium coefficients are determined adaptively. Finally, we conducted experiments on the benchmark dataset and the constructed domain dataset, and the experimental results show that our method can capture meaningful hierarchical information, which is superior to representative knowledge graph reasoning methods and improves the performance of link prediction.

Why does the Earth rotate? At present, several theories on Earth rotation remain hypotheses. Hence, the aim of this study was to obtain experimental evidence of the relationship between the rotational force and magnetic field so that we can use experimental devices to demonstrate the rotation relationship among the planets and the sun. Each permanent magnet rotating under the action of an external force is installed on the shaft of DC motor; each magnetic ball designed to rotate in a magnetic field is placed in the center of a hollow sphere that can float on the water. Using the above setup, the experimental methods and procedures based on this research can be used to observe the rotation behaviour of a permanent magnet in a magnetic field, understand the reason for its rotation, and determine the strength of the rotational force of the permanent magnet in the magnetic field.