Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

The transition from pluripotent to somatic states marks a critical event in mammalian development, but remains largely unresolved. Here we report the identification of SS18 as a regulator for pluripotent to somatic transition or PST by CRISPR-based whole genome screens. Mechanistically, SS18 forms microscopic condensates in nuclei through a C-terminal intrinsically disordered region (IDR) rich in tyrosine, which, once mutated, no longer form condensates nor rescue SS18−/− defect in PST. Yet, the IDR alone is not sufficient to rescue the defect even though it can form condensates indistinguishable from the wild type protein. We further show that its N-terminal 70aa is required for PST by interacting with the Brg/Brahma-associated factor (BAF) complex, and remains functional even swapped onto unrelated IDRs or even an artificial 24 tyrosine polypeptide. Finally, we show that SS18 mediates BAF assembly through phase separation to regulate PST. These studies suggest that SS18 plays a role in the pluripotent to somatic interface and undergoes liquid-liquid phase separation through a unique tyrosine-based mechanism.

In this paper, the finite-time memory fault detection filter (MFDF) is designed for nonlinear discrete systems with randomly occurring deception attacks, where the phenomenon of the randomly occurring deception attacks is characterised by a Bernoulli distributed random variable with known probability. To be specific, the finite-horizon data are employed to construct the MFDF. The main purpose of this paper is to design the MFDF such that, for all nonlinearities, external disturbances and randomly occurring deception attacks, the resultant augmented system is finite-time stable and attains the H ∞ performance. Accordingly, some sufficient conditions are developed to guarantee the existence of the desired fault detection filter parameters, where the solvability of the addressed problem is verified by the feasibility of certain matrix inequalities. Moreover, in order to show that the MFDF has better detection performance, a non-memory fault detection filter (NMFDF) is constructed to compare with the MFDF. Finally, two numerical simulations are utilised to illustrate the effectiveness of the proposed fault detection strategies and explain the superiority of the proposed MFDF.