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Background: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related nonpharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA--GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results: Considering the MAPE, RMSE, and R squared values, the ARMA--GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions: Our study suggested that the ARMA--GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA--GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts. [ABSTRACT FROM AUTHOR]

Background: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. Methods: In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. Results: Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. Conclusions: This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods. [ABSTRACT FROM AUTHOR]

Porous WO3/TiO2 hollow microspheres were prepared by a spray drying method for photodegradation of methylene blue and phenol. The catalysts were characterized by X-ray diffraction, Field Emission Scanning Electron Microscope, High Resolution Transmission Electron Microscope, N2 adsorption-desorption measurements, Raman spectrometer, UV-Vis Diffuse Reflectance Spectroscopy and Zeta-Meter measurements. The results showed that the tungsten oxides mainly existed in highly dispersed amorphous form on anatase when the loading amount of tungsten oxide was below 3 mol%. The improved photocatalytic activity under UV light irradiation of the WO3/TiO2 catalyst mainly arises from the enhanced charge separation efficiency rather than the improved light absorbance by highly dispersed amorphous tungsten oxides. Highly dispersed amorphous WOx can form a shallowly trapped site due to its similar band structure with TiO2. The strongly electron-withdrawing of tungsten oxide in highly dispersed state facilitates the electron transition between titanium and WOx, and consequently improves the charge separation. The enhanced acidity of catalyst by WOx in reactant environment also improved the charge separation efficiency due to the timely transition of holes and electrons accumulated on TiO2 and WOx, respectively. However, the improved photocatalytic activity under visible light irradiation of the WO3/TiO2 catalyst mainly arises from light harvest. TiO2 containing 3 mol% WO3 displayed the highest photocatalytic activity under UV light irradiation while that containing 4 mol% WO3 present highest activity under visible light irradiation. [ABSTRACT FROM AUTHOR]

Highly sensitive biochemical sensors have acquired paramount importance in the fields of medical diagnosis, environmental monitoring and food safety control, etc. Optical microfiber with sub-wavelength diameter has become an essential platform for exploring fiber-optic biochemical sensors on micro-scale, due to the advantages of compact size, large evanescent field and high optical nonlinearity. In this article, recent advances in microfiber biochemical sensors are reviewed. It begins with the fabrication methods of the microfiber, focusing mostly on the microfiber made from silica. The microfiber structures are roughly classified as optical intensity-modulated sensors and optical phase-modulated sensors according to the demodulation approaches. The fundamental sensing mechanisms of the microfiber structures are explained. This is followed by an overview of microfiber sensors for biochemical detection. The overview is classified into four application areas including biosensors, gas sensors, sensors for non-gaseous chemicals and sensors for environmental parameters. Finally, we conclude the paper with an outlook on challenges and opportunities for microfiber biochemical sensors. [ABSTRACT FROM AUTHOR]