Frost Growth Detection Using Capacitive Sensor

Frost buildup on surfaces could be an undesired situation in many applications. In refrigeration and heat pump system, typically, frost grows on the fin surface of the heat exchanger due to different environmental/operational conditions. On one hand, it can block the air flow and increase air-side pressure drop; on the other hand, can increase the thermal resistance and deteriorate heat transfer performance. As a result, frost buildup can significantly reduce the system’s COP. Therefore, most systems encountered frost buildup run the defrost cycle. The frost growth process is affected by many factors, such as environmental conditions (air humidity, temperature, flow rate), operational conditions (working fluids, saturated temperature), heat exchangers (structures, fin type and fin surface wettability) et. al.. All those factors are coupled together, which makes frost growth a very complex dynamic process with variable spatial distribution of its characteristic parameters. It is very important to dynamically detect frost growth for both effective defrost control and precise frost modelling. In this work, a capacitive sensor for frost detection has been developed, which consists of three parts as shown in Figure 1(a): 1) commercial capacitive to digital converter (FDC2214 from Texas Instruments and the resolution of the reading is 0.0001pF), 2) PCB connector and 3) fabricated electrodes. The fabricated copper electrode is attached to the PCB connector, which is mounted to the capacitive to digital converter and connected to the computer by a USB interface. Capacitance variation can be measured when the target properties changes. The interdigital electrodes has a high sensitivity and were fabricated by lithophotography, using copper laminates/ deposited copper thin layer as shown in Figure 1(b) The sensitivity can be affected by metallization ratios, width and thickness of the insulation layer, which are also explored in this work. The frost grows on a cold plate which is placed in the wind tunnel with a controlled air temperature, humidity and flow rate. The electrode of the capacitive sensor is located beside the side wall of the cold plate, as shown in Figure 1(c). The frost growth process can be detected and reflected by the capacitance variation of the sensor, as shown in Figure 2, the capacitance variation can reflect different stage of the frost growth period, starting from condensation to mature growth. Images are also captured by a CCD camera to calibrate the signal. This work demonstrates the dynamic frost growth detection at the first time and could play a significant role to understanding frost growth mechanism and defrost control strategy

Experimental Study of Condensation Heat Transfer of R134a on Oil-infusion Surfaces

Dropwise condensation, since first recognized in 1930, has stimulated interest because its heat transfer coefficient (HTC) is much higher than film condensation. For some applications, not only a higher heat transfer performance is desired, but also the retention of the fluids on the surface can be a big issue. For example, the refrigerant retention in some enhanced tube can block the contact of the vapor-solid interface and increase the thermal resistance; it also can increase the charge of refrigerant because certain amount of refrigerant could not go through the system cycle. Many efforts were dedicated to modifying the surface and promote dropwise condensation, and most research focus on the condensation of water vapor. It is very challenging to promote dropwise condensation for working fluids with a lower surface tension than water, such as refrigerant. Research have been conducted on dropwise condensation for low surface tension fluids using oil-infusion surface, which is promoted by the contact of drop to the liquid-vapor interface instead of solid-vapor interface. However, the effectiveness and efficiency of the oil-infusion surface is still a critical challenge, and the heat transfer mechanism of dropwise condensation with such liquid-liquid interface stays unclear. In this work, condensation of R134a on oil-immerged surfaces is investigated. Heat transfer coefficient is measured, and formation of the condensate is observed using a high speed camera. Two cavity surfaces of different porous scale are examined, of which, one is nanoscale pores and another is microscale pores Mineral oil of low miscibility to R134a is soaked to be saturated in the cavity prior to the experiment. All experiments were conducted under saturated condition of ambient temperature (around 22 °C) in a pressure chamber. The subcool level of the condensation is 10 °C. Images of the local condensation formation is analyzed and heat transfer coefficient is also compared for different surfaces. The duration of the oil-infusion surface is also tested for both surfaces

LGR5, a novel functional glioma stem cell marker, promotes EMT by activating the Wnt/β-catenin pathway and predicts poor survival of glioma patients

Abstract Background Tumor recurrence, the chief reason for poor prognosis of glioma, is largely attributed to glioma stem cells (GSCs) and epithelial-mesenchymal transition (EMT). However, the mechanisms among them remain unknown. Here, we determined whether leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5), known as a stem cell marker for colon cancer and gastric cancer, can serve as a novel GSC marker involved in EMT and a therapeutic target in glioma. Methods Stemness properties were examined in FACS-isolated LGR5+/LGR5− cells. Reported stem cell markers, EMT and the Wnt/β-catenin pathway were examined in stable LGR5 knockdown or overexpressed GSCs by Western Blot. The treatment experiment was performed in an intracranial orthotopic xenograft model by knockdown of LGR5 or by using the Wnt/β-catenin pathway inhibitor Wnt-C59. LGR5 expression was determined in 268 glioma specimens by immunohistochemistry. Results LGR5+ cells possessed stronger stemness properties compared to LGR5− cells. The expression of SOX2, Nanog, CD133, CD44, CD24 and EpCAM was modulated by LGR5. Both LGR5 knockdown and Wnt-C59 reduced tumor invasion and migration and blocked EMT by inhibiting the Wnt/β-catenin pathway in vitro and suppressed the intracranial orthotopic xenograft growth and prolonged the survival of xenograft mice in vivo. Moreover, LGR5 was positively correlated with Ki67, N-cadherin and WHO grade and negatively correlated with IDH1. Glioma patients with high expression of LGR5 showed significantly poorer prognosis. Conclusions LGR5 is a new functional GSC marker and prognostic indicator that can promote EMT by activating the Wnt/β-catenin pathway and would thus be a novel therapeutic target for glioma

Effect of Enrichment Items on the Physiology and Behavior of Sows in the Third Trimester of Pregnancy

Modern intensive pig breeding harms animal welfare, which is especially noticeable for pregnant sows kept in confinement stalls. This study aimed to evaluate the effects of enrichment items on the movement and physiological parameters of sows in the third trimester of pregnancy. A total of 30 large white pregnant sows were randomly divided into three equal treatment groups (n = 10): control, pine wood, and scented wood groups. Interestingly, compared with the control group, the sows in the pine wood or scented wood groups showed less ventral lying and more lateral lying behavior (p < 0.01), coupled with significant reduction in the frequency of scratching and sham-chewing (p < 0.01), but with no significant difference in the degree of preference for these enrichment items (p > 0.05). Additionally, the sows in the pine wood or scented wood groups also decreased significantly in the concentration of immunoglobulin A (IgA) (p < 0.01) and the concentration of tumor necrosis factor-α (TNF-α) (p < 0.05) throughout the late pregnancy period. Overall, adding enrichment items to confinement stalls can alleviate the chronic stress and the stereotypic behavior of sows, suggesting their potential to reduce welfare compromise

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study ¹

Background: influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical 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