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Highly transparent indium-free multilayers of TiO2/Cu/TiO2 were obtained by means of annealing. The effects of Cu thickness and annealing temperature on the electrical and optical properties were investigated. The critical thickness of Cu mid-layer with optimal electrical and optical properties was 10 nm, with the figure of merit reaching as high as 5 × 10−3 Ω−1. Partial crystallization of the TiO2 layer enhanced the electrical and optical properties upon annealing. Electrothermal experiments showed that temperatures of more than 100 °C can be reached at a heating rate of 2 °C/s without any damage to the multilayers. The experimental results indicate that reliable transparent TiO2/Cu/TiO2 multilayers can be used for electrothermal application.

In this study, we proposed an adaptive sparse constrained least squares linear spectral mixture model (SCLS-LSMM) to map wetlands in a sophisticated scene. It includes three procedures: (1) estimating the abundance based on sparse constrained least squares method with all endmembers in the spectral library, (2) selecting “active” endmember combinations for each pixel based on the estimated abundances and (3) estimating abundances based on the linear spectral unmixing algorithm only with the adaptively selected endmember combinations. The performances of the proposed SCLS-LSMM on wetland vegetation communities mapping were compared with the traditional full constrained least squares linear spectral mixture model (FCLS-LSMM) using HJ-1A/B hyperspectral images. The accuracy assessment results showed that the proposed SCLS-LSMM obtained a significantly better performance with a systematic error (SE) of –0.014 and a root-mean-square error (RMSE) of 0.087 for Reed marsh, and a SE of 0.004 and a RMSE of 0.059 for Weedy meadow, compared with the traditional FCLS-LSMM. The proposed methods improved the unmixing accuracies of wetlands’ vegetation communities and have the potential to understand the process of wetlands’ degradation under the impacts of climate changes and permafrost degradation.

This study investigated the feasibility of applying a polyurethane-bonded mixture (PUM) on the pavement overlay for construction or maintenance. Styrene butadiene styrene (SBS) asphalt and epoxy asphalt, the two most common and widely adopted mix designs for highway steel bridge pavement, were selected for a comparative study. First, the performance evaluations and failure analysis of the asphalt mixture and PUM were investigated comprehensively, in terms of low-temperature property, rutting, fatigue, and freeze-thaw resistance. The results showed that the dynamic stability of PUM was over 30,000 cycles/mm and fatigue life was over one million cycles under 800 με. The tensile strength ratio (TSR) value was higher than 90%, which also indicated an excellent freeze-thaw resistance. Then, bond and shear strengths between the steel plate and the mixture were evaluated for the interlayer of the steel bridge pavement structure. X-ray CT scan analysis was adopted to visualize the internal structure of various mixtures, and the average void diameter of PUM was 0.569 mm. Finally, a statistical analysis was conducted for Kendall consistency and Grey correlation. This research established that PUM is a cost-effective and long-lasting solution for pavement overlay on highway steel-bridge. [ABSTRACT FROM AUTHOR]

The alpine tree line ecotone, reflecting interactions between climate and ecology, is very sensitive to climate change. To identify tree line responses to climate change, including intensity and local variations in tree line advancement, the use of Landsat images with long-term data series and fine spatial resolution is an option. However, it is a challenge to extract tree line data from Landsat images due to classification issues with outliers and temporal inconsistency. More importantly, direct classification results in sharp boundaries between forest and non-forest pixels/segments instead of representing the tree line ecotone (three ecological regions—tree species line, tree line, and timber line—are closely related to the tree line ecotone and are all significant for ecological processes). Therefore, it is important to develop a method that is able to accurately extract the tree line from Landsat images with a high temporal consistency and to identify the appropriate ecological boundary. In this study, a new methodology was developed based on the concept of a local indicator of spatial autocorrelation (LISA) to extract the tree line automatically from Landsat images. Tree line responses to climate change from 1987 to 2018 in Wuyishan National Park, China, were evaluated, and topographic effects on local variations in tree line advancement were explored. The findings supported the methodology based on the LISA concept as a valuable classifier for assessing the local spatial clusters of alpine meadows from images acquired in nongrowing seasons. The results showed that the automatically extracted line from Landsat images was the timber line due to the restriction in spatial autocorrelation. The results also indicate that parts of the tree line in the study area shifted upward vertically by 50 m under a 1 °C temperature increase during the period from 1987 to 2018, with local variations influenced by slope, elevation, and interactions with aspect. Our study contributes a novel result regarding the response of the alpine tree line to global warming in a subtropical region. Our method for automatic tree line extraction can provide fundamental information for ecosystem managers.

As most residents spend more than 90% of their time in buildings, acceptable and reasonable control of both indoor thermal comfort and air quality is imperative to ensure occupants’ health status and work productivity. However, current control strategies generally take either thermal comfort or indoor air quality as a single loop, rather than the concurrent control of two. To analyze their mutual influence, this study investigated the performance of three multi-control approaches, i.e., proportional integral derivative (PID) control of thermal comfort and a fixed outdoor air ratio, PID control of thermal comfort and design outdoor air rate, and PID control of thermal comfort and occupancy-based demand-controlled ventilation. As a pilot study, three typical control methods were implemented to a multi-zone building via OpenModelica modeling. The results indicate that indoor air temperature can be well-maintained under three control methods, however, the CO2 concentration under the fixed outdoor air ratio was over 1000 ppm, leading to poor indoor air quality. The control strategy with the design outdoor air rate could not properly ensure the CO2 concentration, due to the over-ventilated or under-ventilated phenomena, subsequently resulting in unnecessary energy waste. The occupancy-based demand controlled ventilation could maintain the CO2 concentration under the set-point with an intermediate power energy utilization.

LiDAR (Light Detection and Ranging)-based individual tree crown reconstruction is a challenge task due to the variable canopy morphologies and the penetrating properties of LiDAR to tree crown surfaces. Traditional methods, including LiDAR-derived rasterization, low-pass filtering smooth algorithm, and original triangular irregular network (TIN) model, have difficulties in balancing morphological accuracy and model smoothness. To address this issue, a scene-based TIN was generated with three steps based on the local scene principle. First, local Delaunay triangles were formed through connecting neighboring point sets. Second, key control points within each local Delaunay triangle, including steeple, inverted tip, ridge, saddle, and horseshoe shape control points, were extracted by analyzing multiple local scenes. These key points were derived to determine the fluctuations of forest canopies. Third, the scene-based TIN model was generated using the control points as nodes. Visual analysis indicates the new model can accurately reconstruct different canopy shapes with a relatively smooth surface, and statistical analysis of individual trees confirms that the overall error of the new model is smaller than others. Especially, the scene-based TIN derived raster reduced the average error to 0.18 m, with a standard deviation of 0.41, while the average errors of LiDAR-derived raster, low-pass filtered smooth raster, and original TIN derived raster have average errors of 0.96, 2.05, and 1.00 m, respectively. The local scene-based control point extraction also reduces data storage due to the elimination of redundant points, and furthermore the different point densities on different objects are beneficial for canopy segmentation.

Pyridinesulfonamide is an important fragment which has a wide range of applications in novel drugs. R- and S-isomers of 5-bromo-2-chloro-N-(1-phenylethyl)pyridine-3-sulfonamide have been synthesized, and the stereostructures have been researched. Single crystals of both compounds were obtained for X-ray analysis, and the absolute configurations (ACs) have been further confirmed by electronic circular dichroism (ECD), optical rotation (OR) and quantum chemical calculations. The crystal structures and calculated geometries were extremely similar, which permitted a comparison of the relative reliabilities of ACs obtained by ECD analyses and theoretical simulation. In addition, the effect of stereochemistry on the PI3Kα kinase and anticancer activity were investigated. Compounds 10a and 10b inhibit the activity of PI3Kα kinase with IC50 values of 1.08 and 2.69 μM, respectively. Furthermore, molecular docking was performed to analyze the binding modes of R- and S-isomers.

Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently observed images, the accuracies are often low, with mixed pixels in coarse-resolution imagery. In this paper, we used the eight-day, 500 m MODIS products (MOD09A1) to test the feasibility of crop unmixing in the U.S. Midwest, an important bioenergy land use region. With all MODIS images acquired in 2007, the 46-point Normalized Difference Vegetation Index (NDVI) time series was extracted in the study region. Assuming the phenological pattern at a pixel is a linear mixture of all crops in this pixel, a spatially constrained phenological mixture analysis (SPMA) was performed to extract crop percent covers with endmembers selected in a dynamic local neighborhood. The SPMA results matched well with the USDA crop data layers (CDL) at pixel level and the Crop Census records at county level. This study revealed more spatial details of energy crops that could better assist bioenergy decision-making in the Midwest.

Simian retrovirus subtype 8 (SRV-8) infections have been reported in cynomolgus monkeys (Macaca fascicularis) in China and America, but its pathogenicity and immunogenicity are rarely reported. In this work, the SRV-8-infected monkeys were identified from the monkeys with anemia, weight loss, and diarrhea. To clarify the impact of SRV-8 infection on cynomolgus monkeys, infected monkeys were divided into five groups according to disease progression. Hematoxylin (HE) staining and viral loads analysis showed that SRV-8 mainly persisted in the intestine and spleen, causing tissue damage. Additionally, the dynamic variations of blood routine indexes, innate and adaptive immunity, and the transcriptomic changes in peripheral blood cells were analyzed during SRV-8 infection. Compared to uninfected animals, red blood cells, hemoglobin, and white blood cells were reduced in SRV-8-infected monkeys. The percentage of immune cell populations was changed after SRV-8 infection. Furthermore, the number of hematopoietic stem cells decreased significantly during the early stages of SRV-8 infection, and returned to normal levels after antibody-mediated viral clearance. Finally, global transcriptomic analysis in PBMCs from SRV-8-infected monkeys revealed distinct gene expression profiles across different disease stages. In summary, SRV-8 infection can cause severe pathogenicity and immune disturbance in cynomolgus monkeys, and it might be responsible for fatal virus-associated immunosuppressive syndrome. [ABSTRACT FROM AUTHOR]