Blends of Bio-Based Poly(Limonene Carbonate) with Commodity Polymers

In this study, blends of the bio-based poly(limonene carbonate) (PLimC) with different commodity polymers are investigated in order to explore the potential of PLimC toward generating more sustainable polymer materials by reducing the amount of petro- or food-based polymers. PLimC is employed as minority component in the blends. Next to the morphology and thermal properties of the blends the impact of PLimC on the mechanical properties of the matrix polymers is studied. The interplay of incompatibility and zero-shear melt viscosity contrast determines the blend morphology, leading for all blends to a dispersed droplet morphology for PLimC. Blends with polymers of similar structure to PLimC (i.e., aliphatic/aromatic polyester) show the best performance with respect to mechanical properties, whereas blends with polystyrene or poly(methyl methacrylate) are too brittle and polyamide 12 blends show very low elongations at break. In blends with Ecoflex (poly(butylene adipate-co-terephthalate)) and Arnitel EM400 (copoly(ether ester)) with poly(butylene terephthalate) hard and polytetrahydrofuran soft segments) a threefold increase in E-modulus can be achieved, while keeping the elongation at break at reasonable high values of ≈200%, making these blends highly interesting for applications.     

药物相互作用研究在新药研发和审评决策中的应用

药物相互作用改变了剂量效应关系，可能会降低疗效或增加毒性，是临床应用中合并用药治疗时重要的考虑因素。预测具有临床意义的药物相互作用是药物研发过程中获益风险评估的重要环节。本文概述了药物研发过程中药物相互作用研究的目的和意义，体内和体外研究的主要内容；梳理分析了2020年国家药品监督管理局（National Medical Products Administration, NMPA）和美国食品药品监督管理局（Food and Drug Administration, FDA）批准上市的新药药物相互作用研究情况，旨在为我国药物研发过程中药物相互作用研究及其监管审评提供参考。     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

Optimization of room-temperature TCR of polycrystalline La0.9-xSrxK0.1MnO3 ceramics by Sr adjustment

High-density La_0.9-xSr_xK_0.1MnO₃ ceramics (LSKMO, A-site = La, Sr and K, 0 ≤ x ≤ 0.25) are successfully fabricated by using facile sol-gel method. Electrical properties are performed by using combination of phenomenological percolation (PP) model, double exchange (DE) mechanism, and Jahn-Teller (JT) effect. Moreover, X-ray diffraction and scanning electron microscopy are employed to analyze the structure and morphology of LSKMO ceramics. Valence states and ionic stoichiometry are assessed by using X-ray photoemission spectrometry. Results reveal that Sr²⁺ ions, substituting La³⁺ ions, significantly influenced DE mechanism and JT effect. In addition, Sr-doping plays essential role in improving electrical properties of LSKMO ceramics. At optimal doping content of x = 0.09, peak temperature coefficient of resistance (TCR) of the resistivity is found to be 11.56% K^?1 at 297.15 K, which is optimal TCR for A-site K-occupied perovskite manganese oxides. These results confirm that polycrystalline LSKMO ceramics render high room-temperature TCR values due to Sr-doping.     

Revealing the high sensitivity in the metal toinsulator transition properties of the pulsed laser deposited VO2 thin films

Vanadium dioxide (VO₂) is known as a typical 3d-orbital transition metal oxide exhibiting the metal-to-insulator-transition (MIT) property near room temperature. However, their electronic applications have been challenged by the quality and uniformity of VO₂ thin films. In this work, we demonstrate the high sensitivity in the valence charge of vanadium and the MIT properties of the VO₂ thin films to the deposition temperature. This observation indicates the necessity to eliminate the inhomogeneity in the temperature distribution of substrate during the vacuum-deposition process of VO₂. In addition, a high thermoelectric power factor (PF, e.g., exceeding 1 μWcm⁻¹K⁻²) was achieved in the metallic phase of the VO₂ thin films and this value is comparable to typical organic or oxide thermoelectric materials. We believe this high PF enriches the potential functionality in thermoelectric energy conversions beyond the existing electronic applications of the current vacuum-grown VO₂ thin films.     

Novel energy-saving window coating based on F doped TiO2 nanocrystals with enhanced NIR shielding performance

To reduce the energy consumption of cooling in the hot summer days, searching for novel NIR shielding materials for buildings is of great value. In this report, monodispersed F doped TiO₂ nanocrystals with an average size of 8.6 nm were synthesized as novel solar shielding materials for energy-saving windows. All the products adopted an anatase TiO₂ structure. After doping of F ions, the morphology of TiO₂ was transformed from an irregular shape to a pseudospherical shape. The Raman shift and XPS depth analysis confirmed the successful doping of F⁻ ions into the lattice oxygen sites in the TiO₂ structure. The introduction of F⁻ ions generated free electrons and bulk Ti³⁺ in TiO₂ crystals, which activated a localized surface plasmon resonance (LSPR) absorption in the NIR region. Correspondingly, the NIR shielding performance of the TiO₂ films improved with increasing F doping amounts. The NIR shielding value of the films increased from 1.3% to 43.2% when the molar ratio of F to Ti increased from 0 to 0.3. The reason can be attributed to the enhanced NIR absorption induced by the increased electron concentration after doping of fluorine ions. The F–TiO₂ films showed superior visible transmittance (90.1–96.7%). Moreover, the F–TiO₂ films lowered the indoor temperature of the heat box by 5.3 °C in the thermal tests. Overall, the prepared F–TiO₂ nanocrystals show a great potential to be used for energy-saving windows.     

Structural and Functional Characterization of the ABCC6 Transporter in Hepatic Cells: Role on PXE,Cancer Therapy and Drug Resistance

Pseudoxanthoma elasticum (PXE) is a complex autosomal recessive disease caused by mutations of ABCC6 transporter and characterized by ectopic mineralization of soft connective tissues. Compared to the other ABC transporters, very few studies are available to explain the structural components and working of a full ABCC6 transporter, which may provide some idea about its physiological role in humans. Some studies suggest that mutations of ABCC6 in the liver lead to a decrease in some circulating factor and indicate that PXE is a metabolic disease. It has been reported that ABCC6 mediates the efflux of ATP, which is hydrolyzed in PPi and AMP; in the extracellular milieu, PPi gives potent anti-mineralization effect, whereas AMP is hydrolyzed to Pi and adenosine which affects some cellular properties by modulating the purinergic pathway. Structural and functional studies have demonstrated that silencing or inhibition of ABCC6 with probenecid changed the expression of several genes and proteins such as NT5E and TNAP, as well as Lamin, and CDK1, which are involved in cell motility and cell cycle. Furthermore, a change in cytoskeleton rearrangement and decreased motility of HepG2 cells makes ABCC6 a potential target for anti-cancer therapy. Collectively, these findings suggested that ABCC6 transporter performs functions that modify both the external and internal compartments of the cells.     

Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

不同盐沼湿地类型脱氮除磷效能及影响因素分析

采用沉水植物表面流湿地(沉水组)、挺水植物表面流湿地(挺水组)和浮床湿地(浮床组)3种盐沼湿地对长江口近岸低污染水体进行脱氮除磷效能的研究。结果表明,HRT为3 d时,水组、挺水组、浮床组对NO3^--N的去除率在高温时段分别为79.9%±13.2%、71.8%±15.2%、77.2%±13.2%,中温时段分别为39.4%±13.7%、31.5%±8.5%、18.4%±16.6%,低温时段分别为15.6%±14.6%、19.7%±8.6%、2.%5±8.6%。沉水组和挺水组对TP的去除率受温度影响较小,分别为66.4%±32.4%、55.5%±29.4%;而浮床组除磷效果受温度影响较大。当HRT缩短为1.5 d时,3组湿地系统在高温时段仍可达到相近的脱氮除磷效果,在中低温时段脱氮除磷效果都有不同程度的下降。