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Sensors are crucial for measuring combustion temperatures in aerospace and aviation engine testing. However, current sensors have poor oxidation resistance, low impact resistance, limited lifespan, and inadequate temperature measurement accuracy, often resulting in unsatisfactory testing outcomes. New sensor designs are urgently needed to address these issues. We propose a new sensor with advanced materials and technologies, based on the principle of ultrasonic guided wave temperature measurement with magnesium aluminum spinel (MgAl2O4) and magnesium-doped aluminum oxide crystals as ultrasonic waveguides. The design parameters of this sensor's sensitive elements were meticulously crafted. Finite element method simulations were then conducted to assess the impact of groove depth on ultrasonic propagation characteristics. Ultrasonic temperature sensors with spinel and magnesium-doped aluminum oxide were fabricated via the laser heated pedestal growth method. These sensors were calibrated in an oxidative environment, demonstrating a temperature sensitivity of 0.48 m/s·℃ and a repeatability of 95% across a range from 20 ℃ to 1600 ℃. By comparison among the three materials at a constant temperature, the sound velocity of sapphire was the fastest, followed by magnesia-doped alumina, while magnesia-alumina spinel was slowest. Thus, magnesia-alumina spinel can be considered an effective acoustic waveguide material for facile signal acquisition and high-temperature resolution. The proposed sensor design shows promise for applications in environments prone to oxidative erosion and high temperatures, offering an innovative solution for reliable temperature measurement within the harsh environments of aerospace and aviation engines.

Web-based search query data have been recognized as valuable data sources for discovering new influenza epidemics. However, selecting search and query keywords and adopting prediction methods pose key challenges to improving the effectiveness of influenza prediction. In this study, web search data were analyzed and excavated using big data and machine learning methods. The flu prediction model for the southern region of China, considering the impact of influenza transmission across regions and based on various keywords and historical influenza-like illness percentage (ILI%) data, was built (models 1–4) to verify the factors affecting the spread of the flu. To improve the accuracy of the influenza trend prediction, a support vector regression method based on an improved particle swarm optimization algorithm was proposed (IPSO-SVR), which was applied to the influenza prediction model to forecast ILI% in southern China. By comparing and analyzing the prediction results of each model, model 4, using the IPSO-SVR algorithm, exhibited higher prediction precision and more effective results, with its prediction indexes including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) being 0.0596, 0.2441 and 0.1884, respectively. The experimental results show that the prediction precision significantly increased when the IPSO-SVR method was applied to the constructed ILI% model. A new theoretical basis and implementation strategy were provided for achieving more accurate influenza prevention and control in southern China.

The increasingly serious haze problem in China has brought about a growing public awareness of air quality. Precise air quality index (AQI) forecasts play an important role in both controlling air pollution and promoting the sustainable development of human society. However, the randomness, non-stationarity, and irregularity of the AQI series make its classifications very difficult. This paper introduces a time-varying inertia weighting (TVIW) strategy based on a combination of gravitation search algorithm (GSA) and particle swarm optimization (PSO) called the TVIW-PSO-GSA. The TVIW-PSO-GSA is utilized to optimize the penalty parameter C and kernel function parameter γ of a support vector machine (SVM) to create a hybrid TVIW-PSO-GSA-SVM algorithm. Twenty-three benchmark functions, five UCI datasets, and an AQI hierarchical classification example are tested to find that the convergence speed and performance of the TVI-PSO-GSA exceed those of other algorithms, and the TVIW-PSO-GSA-SVM algorithm also achieves higher classification accuracy and efficiency than the PSO-GSA-SVM, GSA-SVM, GA-SVM, or PSO-SVM, which indicates that the TVIW-PSO-GSA-SVM reliably and accurately classifies AQI and UCI datasets.

This paper mainly studies the model design of a thin-film heat-flux sensor (TFHFS), and focuses on the comparison of three dynamic calibration methods. The primary motivation for studying this came from the urgent need for heat-flux dynamic measurements in extreme environments, and the one-sidedness of the dynamic performance evaluation of the corresponding TFHFS. The dynamic theoretical model of the TFHFS was originally established on the principle of a temperature gradient on the basis of a thermal radiation boundary. Then, a novel TFHFS sensor was developed, which can be used at temperatures above 880 °C and has a high sensitivity of 2.0 × 10−5 mV/(W/m2). It can function stably for long durations under a heat-flux density of 3 MW/m2. The steady-state, transient, and frequency calibration of a TFHFS were compared to comprehensively analyze the dynamic characteristics of the TFHFS. The steady-state response time measured by the step excitation method was found to be 0.978 s. The QR decomposition method was applied to the steady-state response experimental model construction, and the fitting degree of a second-order transfer function model obtained was 98.61%. Secondly, the transient response time of the TFHFS was 0.31 ms based on the pulse-excitation method. The transient relationship between the surface temperature and the heat flux, and the pulse-width dependence of the TFHFS transient response time were established. Surprisingly, the response frequency of the TFHFS, about 3000 Hz, was efficiently tested using the frequency response function (FRF), which benefitted from the harmonic characteristics of a periodic square-wave excitation signal. Finally, a comprehensive evaluation of the dynamic performance of the TFHFS was realized.

In this paper, a series of models were established, and based on the Google Flu Trends (GFT) data and Centers for Disease Control (CDC) data. The models include the GFT regression model (model 1), the weighted GFT regression model (model 2), the GFT + CDC regression model (model 3), the CDC regression model (model 4), and the weighted CDC regression model (model 5). All models were utilized to predict and assess influenza activity across ten regions of the United States. The least squares and backpropagation neural network based on the genetic algorithm are used to fit the model parameters, and the error and historical sample fitting accuracy of each model are compared. The results show that models 4 and 5 are superior to other models. To optimize the prediction model, the seasonal characteristics of influenza incidence were investigated, and flu-prediction models for the high-flu season and low-flu season were established. The experimental results show that the prediction model of seasonal influenza is superior to the non-seasonal model. The influenza-like illness values predicted by the seasonal flu model are consistent with information provided by the CDC, suggesting that the results accurately reflect influenza epidemic characteristics and can thus be readily applied for the prevention and control of influenza.

The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control's (CDC's) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu.

In this study, an ultrasonic temperature measurement system was designed with Al2O3 high-temperature ceramic as an acoustic waveguide sensor and preliminarily tested in a high-temperature oxidation environment. The test results indicated that the system can indeed work stably in high-temperature environments. The relationship between the temperature and delay time of 26 °C–1600 °C ceramic materials was also determined in order to fully elucidate the high-temperature oxidation of the proposed waveguide sensor and to lay a foundation for the further application of this system in temperatures as high as 2000 °C.

In this study, an ultrasonic temperature measurement system was designed with Al2O3 high-temperature ceramic as an acoustic waveguide sensor and preliminarily tested in a high-temperature oxidation environment. The test results indicated that the system can indeed work stably in high-temperature environments. The relationship between the temperature and delay time of 26°C-1600°C ceramic materials was also determined in order to fully elucidate the high-temperature oxidation of the proposed waveguide sensor and to lay a foundation for the further application of this system in temperatures as high as 2000 °C. [ABSTRACT FROM AUTHOR]