NOMENCLATURE D = diameter of the central circle d = diameter of the peripheral circles γ = ratio of diameters (d/D) Slippage occurs when a screw cannot be driven because the user's power or torque is not transmitted due to plastic deformation of the screw head socket or driver tip. In this study, we evaluated a new cloverleaf-shaped screw head to minimize the slippage of medical screws. L-clover and S-clover heads were selected as new candidates and compared with triangular, Torx ® , and hexagonal screw heads. For each head, the polar moment of inertia was calculated, the stress distribution was analyzed using a finite element method, and torsion tests of physical specimens were performed to analyze their slippage characteristics. The hexagonal head, which is used most commonly in medical screws, had the lowest resistance to slippage. However, the L-clover head had the best features for minimizing the slippage of medical screws.

Due to the influence of factors such as strong music specialization, complex music theory knowledge, and various variations, it is difficult to identify music features. We have developed a music characteristic identification system using the Internet-based method. The physical sensing layer of our designed system deploys audio sensors on various coordinates to capture the raw audio signal and performs audio signal processing and analysis using the TMS320VC5402 digital signal processor; the Internet transport layer places audio sensors at various locations to capture the raw audio signal. The TMS320VC5402 digital signal processor is used for audio signal diagnosis and treatment. The network transport layer transmits the finished audio signal to the data base of song signal in the application layer of the system; the song characteristic analysis block in the application layer adopts dynamics. The music characteristic analysis block in the applied layer adopts dynamic time warping algorithm to acquire the maximal resemblance between the test template and the reference template to achieve music signal characteristic identification and identify music tunes and music modes based on the identification results. The application layer music feature analysis block adopts dynamic time regularization algorithm and mel-frequency cepstrum coefficient to achieve music signal feature recognition and identify music tunes and music patterns based on the recognition results. We have verified through experiments, and the results show that the system operates consistently, can obtain high-quality music samples, and can extract good music characteristics.

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