Dynamic Calibration of a Thin-Film Heat-Flux Sensor in Time and Frequency Domains.

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.

Ultrasonic Al2O3 Ceramic Thermometry in High-Temperature Oxidation Environment.

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.

[Monitoring of Curing Temperature of Pouring Explosive Based on Fiber Bragg Grating].

For the first time, we real time measured released reaction heat between the binder and the curing agent in the curing process of cast explosive using fiber Bragg grating. In order to obtain the temperature in the process of pouring explosive casting real time and accurately, we designed the temperature monitoring system based on fiber Bragg grating. Given the risk of explosive component, long curing time and the requirements of constant temperature, a suitable measurement method for direct real-time monitoring has not been found. In recent years, due to its superior characteristics, fiber Bragg grating is widely used in the field of communication and sensing. We will make the collected reflection wavelength to convert real-time temperature displaying, utilizing linear relationship between fiber Bragg grating and temperature. Through WDM technology, seven grating points are written in two optical fibers to measure at the same time, and distribution trend of explosives internal temperature can be displayed in real time by multi-point distributed measurement. The curved design of the sensor not only improves the connection between sensor and jumper, but also benefits to place in oven. The txt data is made to draw a graph using origin software, and the changes in temperature in the curing process are displayed intuitively. The results show that this method is simple and high-precision, and meets the testing requirements of curing temperature of explosives.

Development and validation of a prediction model for rebound hyperbilirubinemia: a Chinese neonatal cohort study.

Background: Rebound hyperbilirubinemia (HBB) is still present in as high as 10% of newborn babies. However, the applicability of established prediction models for rebound HBB to Chinese newborns is unclear. This study aimed to establish a model to predict HBB rebound after phototherapy among Chinese neonates. Methods: A retrospective cohort study was conducted on 1,035 HBB infants receiving phototherapy. Rebound HBB was defined as total serum bilirubin (TSB) returning to or above the American Academy of Pediatrics (AAP) phototherapy threshold within 72 hours after the end of phototherapy. The predictive effects of previously published two- and three-variable scores were verified. Neonates were randomly assigned in a 6:4 ratio to the training (n=621) group and the testing (n=414) group. All variables in the training set were used to select predictors by least absolute shrinkage and selection operator (LASSO) regression analysis. The internal validation of the prediction model was performed using the testing set. The model's predictive performance was evaluated by area under the curve (AUC), accuracy, sensitivity, and specificity, each with 95% confidence intervals (CIs). Receiver operating characteristic (ROC) and calibration curves were constructed to evaluate the discrimination ability and fitting effect of the prediction model, respectively. Results: Rebound HBB was observed in 210 patients (20.3%). The AUC for the two- and three-variable scores were 0.498 (95% CI: 0.455-0.540) and 0.498 (95% CI: 0.457-0.540), respectively. Predictive factors for the risk of rebound HBB included formula feeding (>3 times/day), standard phototherapy irradiation time, TSB levels and age at termination of phototherapy, neonatal weight, and differences between TSB levels at the phototherapy termination and phototherapy threshold. The prediction model's AUC was 0.935 (95% CI: 0.911-0.958), the sensitivity was 0.880 (95% CI: 0.809-0.950), the specificity was 0.831 (95% CI: 0.790-0.871), and the accuracy was 0.841 (95% CI: 0.805-0.876). Conclusions: The established model performed well in predicting rebound risk among Chinese infants with HBB, which may be beneficial in treating and managing HBB in infants.

Ultrasonic thermometric measurement system for solid rocket combustion chambers.

Solid rocket motor (SRM) temperature is an important physical parameter for which there is no reliable in situ measurement device, apart from a thermocouple, for such a high temperature environment apart. In this study, an ultrasonic temperature measurement system was designed with an iridium-rhodium-40% alloy waveguide. Laboratory experiments showed that the device obtained ultrasonic signals up to 1800 °C with a temperature fitting curve from room temperature to 1800 °C. The thermometer also operated stably under high temperature and produced a repeatable calibration curve, at 97% repeatability. An error band was obtained with 95% confidence. At temperatures above 1000 °C, sensitivity gradually increased to a maximum of 0.0035 µs/°C. A corresponding application structure was established for an SRM before subjecting the sensor to a temperature test experiment. The temperature-time curve obtained detected a peak temperature at 1744 °C.

Regional level influenza study based on Twitter and machine learning method.

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.