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Microscale temperature mapping in liquids is of great importance in many areas of research such as microfluidics and biology. Among the current thermometric approaches, optical probing using fluorescence is particularly desirable because of its high spatial resolution and non-invasive nature. Here we report a new microscale two-dimensional (2D) fluorescence thermometry. This method exploits the temperature dependence of rotational molecular motion and its influence on the depolarization of fluorescence light, by measuring the difference in fluorescence intensities excited by orthogonal polarizations. With one charge coupled device (CCD) camera, we get 2D ratiometric mappings of temperature from successively recorded fluorescence images under alternatively polarized excitations. We demonstrate reliable temperature mapping in liquids at sub-1 °C temperature accuracy and sub-10 µm spatial resolution. We also show that the proposed thermometry approach is robust against fluorescence intensity variations, suitable for 2D mapping and of fast readout that is comparable to CCD framerate. Moreover, it is easy to be integrated into microscope systems since only rotation of excitation polarization is needed.

We evaluated the performance of a variety of time series models, that include the harmonic (HR) model, autoregressive (AR) model, linear Gaussian state-space (LGSS) model, cubic spline (SP) model, double logistic (DL) model, and asymmetric Gaussian (AG) model, for reconstructing (all six models) and forecasting (HR, AR, and LGSS models) dense Landsats 5–7 time series based on 4562 samples. To remove the impact of land change and human interventions on data reconstruction and forecasting, this evaluation excluded croplands and samples changed between 2000 and 2011. Results show that the widely used HR model is not a good model for data reconstruction but outperforms other models in data forecasting. The DL and AG models have the best performance in data reconstruction but cannot forecast Landsat observations. The AR and LGSS models shared similar performance in reconstructing Landsat data but are less ideal for data forecasting, particularly for the LGSS model. Integrating the HR (for forecasting) and DL or AG (for reconstruction) is recommended to improve land change detection and land cover classification results. We also evaluated the impact of data density and irregularity on reconstructing and forecasting Landsat observations. When the data density is low (

To improve the poor thermal properties of traditional structural insulated panels (SIPs) for better energy saving effect in the building, composited structural insulated panels (CSIPs) with glass fiber reinforced plastic (GFRP) and cement (GFRC) materials were developed in this study. ANSYS is used to simulate the thermal properties of SIPs and CSIPs, and energy consumption of a house with these creative CSIPs were simulated by EnergyPlus. The results indicated that the zone method and modified zone method are acceptable to compute the R-value of the SIPs and the CSIPs. However, isothermal method and the parallel method are unsuitable to calculate their thermal resistance. A reduction in the stud web area and replacement of the steel web by less conductive materials are two effective ways to improve the thermal performance of SIPs. Furthermore, in terms of energy savings and thermal properties, GFRP SIPs are considerably better than steel SIPs. The conclusions are: the thermal properties of CSIPs with GFRP, GFRC or wood sheathings are nearly the same. However, a tube stud should be preferentially selected considering that its mechanical properties, easy design and construction are better than that of a folded plate. Moreover, GFRP and GFRC sheathings are given priority over wood. The novelty and new contribution of this paper is to design a thermal insulation, mechanical and durable CSIP with GFRP and GFRC, and use ANSYS to calculate its thermophysical properties and use EnergyPlus to simulate the energy consumption of a house with this CSIP.

Let G be an edge-colored connected graph, a path (trail, walk) of G is said to be a proper-path (trail, walk) if any two adjacent edges of it are colored distinctly. If there is a proper-path (trail, walk) between each pair of different vertices of G , then G is called proper-path (trail, walk) connected. The edge-coloring which makes G proper-path (trail, walk) connected is called a proper-path (trail, walk) coloring. The minimum number of colors required in a proper-path (trail, walk) coloring is referred to as the proper-path (trail, walk) connection number of G . In J. Bang-Jensen, T. Bellitto and A. Yeo, Proper-walk connection number of graphs, J. Graph Theory 96(2020) 137–159, the authors investigated the graphs with proper-walk connection number k and suggested to study the stretch of proper-walk coloring. In this note, we consider the stretch of proper-walk (path, trail) coloring and present some tight upper bounds.

Objectives Various probiotics and natural products can help to relieve constipation. This study aimed to explore the constipation-relieving effects and potential mechanism of a combination laxative of konjac glucomannan and probiotics. Methods This study evaluated the gastrointestinal-tract viability of probiotics in vitro. A constipation model was constructed in BALB/c mice, and the efficacies of the combinations verified in terms of their bowel movement–promoting effects, including the first black-stool defecation time and gastrointestinal transit rates of mice. Colonization by the probiotics was determined by quantitative real-time polymerase chain reaction. Hematoxylin-eosin staining, gas chromatography, enzyme-linked immunosorbent assay, quantitative real-time polymerase chain reaction, and Western blot were also used for analysis. Results Lactobacillus paracasei X11 (X11) and L. casei YRL577 (YRL577) had outstanding gastrointestinal-tract viability. Konjac glucomannan (KGM) + X11, Prunus persica + X11, and Prunus persica + YRL577 significantly relieved constipation. In addition, KGM promoted the colonization of X11. Meanwhile, KGM + X11 effectively promoted the metabolism of short-chain fatty acids in mice better than other combinations, and the 5-hydroxytryptamine (5-HT) content in the KGM + X11 group was the highest among all the groups. Therefore, KGM + X11 was selected for further research. The combination laxative promoted the secretion of 5-HT, up-regulated mRNA and protein levels of 5-HT receptor 4 and serotonin transporter via the 5-HT pathway, and effectively relieved constipation. Conclusions The combination laxative konjac glucomannan–probiotic (KGM + X11) promoted defecation in constipated mice, possibly by increasing short-chain fatty acid metabolism and 5-HT hormone release.

We developed a new algorithm for COntinuous monitoring of Land Disturbance (COLD) using Landsat time series. COLD can detect many kinds of land disturbance continuously as new images are collected and provide historical land disturbance maps retrospectively. To better detect land disturbance, we tested different kinds of input data and explored many time series analysis techniques. We have several major observations as follows. First, time series of surface reflectance provides much better detection results than time series of Top-Of-Atmosphere (TOA) reflectance, and with some adjustments to the temporal density, time series from Landsat Analysis Ready Data (ARD) is better than it is from the same Landsat scene. Second, the combined use of spectral bands is always better than using a single spectral band or index, and if all the essential spectral bands have been employed, the inclusion of other indices does not further improve the algorithm performance. Third, the remaining outliers in the time series can be removed based on their deviation from model predicted values based on probability-based thresholds derived from normal or chi-squared distributions. Fourth, model initialization is pivotal for monitoring land disturbance, and a good initialization stability test can influence algorithm performance substantially. Fifth, time series model estimation with eight coefficients model, updated for every single observation, based on all available clear observations achieves the best result. Sixth, a change probability of 0.99 (chi-squared distribution) with six consecutive anomaly observations and a mean included angle