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A new method of multi-body system (MS) dynamics, named discrete time transfer matrix method of multi-body system (MS-DT-TMM), has been developed to study multi-rigidbody system dynamics in recent years. When using this method, global dynamics equations of the system are not needed and the orders of involved system matrices are always very small. By defining new state vectors and developing new transfer matrices for a multi-rigid-flexible-body system (MRFS) to expand the MS-DT-TMM, a new method, named discrete time transfer matrix method of MRFS (MRFS-DT-TMM), has been developed to study MRFS dynamics in this article. If MRFS-DT-TMM is used, as an MS-DT-TMM, the global dynamics equations of the systems are not needed in the study of MRFS dynamics, the orders of involved matrices are always very small, and the computational speed is high irrespective of the size of an MRFS. This method is simple, straightforward, and practical, and provides a powerful tool for MRFS dynamics. Formulations of the method as well as numerical examples of MRFS dynamics are given to validate the method.

The multibody system transfer matrix method (MSTMM), a novel dynamics approach developed during the past three decades, has several advantages compared to conventional dynamics methods. Some of these advantages include avoiding global dynamics equations with a system inertia matrix, utilizing low-order matrices independent of system degree of freedom, high computational speed, and simplicity of computer implementation. MSTMM has been widely used in computer modeling, simulations, and performance evaluation of approximately 150 different complex mechanical systems. In this paper, the following aspects regarding MSTMM are reviewed: basic theory, algorithms, simulation and design software, and applications.Future research directions and generalization to more applications in various fields of science, technology, and engineering are discussed.

Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.