Background
Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed.
Methods
The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo.
Results
For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively.
Conclusions
The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.
An
electrochemical (EC) method has been successfully applied to
regulate the optical properties of nanocrystals, such as reducing
their gain threshold by EC doping and enhancing their photoluminescence
intensity by EC filling of trap states. However, the processes of
EC doping and filling are rarely reported simultaneously in a single
study, hindering the understanding of their underlying interactions.
Here, we report the spectroelectrochemical (SEC) studies of quasi-two-dimensional
nanoplatelets (NPLs), intending to clarify the above issues. EC doping
is successfully achieved in CdSe/CdZnS core/shell NPLs, with red-shifted
photoluminescence and a reversal of the emission intensity trend.
The injection of extra electrons (holes) into the conduction (valence)
band edges needs high bias voltages, while the passivation/activation
process of trap states with the shift of Fermi level starts at lower
EC potentials. Then, we explore the role of excitation light conditions
in these processes, different from existing SEC research studies.
Interestingly, increasing the laser power density can hinder EC electron
injection, whereas decreasing the excitation energy evades the passivation
process of trap states. Moreover, we demonstrate that EC control strategies
can be used to realize color display and anti-counterfeiting applications
via simultaneously tailoring the photoluminescence intensity of red-
and green-emitting NPLs.
Because of their excellent optical and electrical properties, doped carbon quantum dots (CQDs) are expected to be applied in novel film optoelectronic devices such as light-emitting diodes and solar cells....
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