A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data - 12 Urban Agglomerations, China, 2009-2020.

Introduction: The prevalence of unstable and incomplete monitoring data significantly complicates syndromic analysis. Many data interpolation methods currently available demonstrate inadequate effectiveness in overcoming this issue. Methods: To improve the accuracy of interpolation, we propose the integration of the SHapley Additive exPlanation model (SHAP) with the structural equation model (SEM), forming a combined SHAP-SEM approach. A case study is then performed to assess the enhanced performance of this novel model compared to traditional methods. Results: The SHAP-SEM model was utilized to develop an interpolation model employing data from the Chinese respiratory syndrome surveillance database. We executed three distinct experiments to establish the model datasets, comprising a total of 100 replicates. The performance of the model was evaluated using the root mean square error (RMSE), correlation coefficient (r), and F-score. The findings demonstrate that the SHAP-SEM model consistently achieves superior accuracy in data interpolation, which is evident across different seasons and in overall performance. Discussion: We conclude that the SHAP-SEM model demonstrates an exceptional capacity for accurately interpolating volatile and incomplete data. This capability is crucial for developing a comprehensive database that is essential for conducting risk assessments related to syndromes.

Dual-Carbon Phase-Encapsulated Prelithiated SiOx Microrod Anode for Lithium-Ion Batteries.

Among silicon-based anode family for Li-ion battery technology, SiOx, a nonstoichiometric silicon suboxide holds the potential for significant near-term commercial impact. In this context, this study mainly focuses on demonstrating an innovative SiOx@C anode design that adopts a pre-lithiation strategy based on in situ pyrolysis of Li-salt of silsesquioxane trisilanolate without the need for lithium metal or active lithium compounds and creates dual carbon encapsulation of SiOC nanodomains by simply one-step thermal treatment. This ingenious design ensures the pre-lithiation process and pre-lithiation material with high-environmental stability. Moreover, phenyl-rich organosiloxane clusters and polyacrylonitrile polymers are expected to serve as internal and external carbon source, respectively. The formation of an interpenetrating and continuous carbon matrix network would not only synergistically offer an improved electrochemical accessibility of active sites but also alleviate the volume expansion effect during cycling. As a result, this new type of anode delivered a high reversible capacity, remarkable cycle stability as well as excellent high-rate capability. In particular, the L2-SiOx@C material has a high initial coulomb efficienc of 80.4% and, after 500 cycles, a capacity retention as high as 97.5% at 0.5 A g-1 with a reversible specific capacity of 654.5 mA h g-1.

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction.

Failure to blow ash on the heated surface of the boiler will cause a drop in heat transfer rate and even industrial safety accidents. Nowadays, the shortcomings of the fixed soot blowing operation every hour and every shift are significant, which can be improved by high-precision ash accumulation prediction. Therefore, this paper proposes a deep learning model fused with deep feature extraction. First, a dynamic fouling model and a health index-clearness factor (CF) of the heated surface are established. The data preprocessing method reduces unnecessary forecasting difficulty and makes the degradation trend of the CF time series more obvious. In addition, deep feature extraction is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and kernel principal component analysis (KPCA), which completes the multiscale analysis of time series and reduces the training time of deep learning models, and has significant contributions to improving prediction accuracy and reducing time consumption. The adaptive sliding window and the encoder-decoder based on the attention mechanism (EDA) can better mine the internal information of the time series. Compared with long short-term memory (LSTM), taking the 300 MW boiler's various heated surface data sets as an example, multistep forward prediction and different starting point prediction experiments have verified the superiority and effectiveness of the model. Finally, under the variable working condition economizer datasets, the proposed method better completes the predictive maintenance task of the heated surface. The research results provide operational guidance for improving heat transfer rate, energy saving, and reducing consumption.

Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction.

Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods.