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In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted significant interest due to their intriguing physical, chemical, electronic and optical properties. However, the practical applications of TMDs are limited by challenges related to controlling the thickness of atomic layers. Machine learning (ML), a data-driven approach characterized by extensive search capabilities and accurate classification, offers a promising approach to address this limitation. In this study, a prediction model was constructed using four machine learning algorithms, namely XGBoost, Support Vector Machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron (MLP), to explore the growth mechanism of MoS2 material layers prepared through chemical vapor deposition (CVD). Furthermore, the models were evaluated using performance assessment metrics such as recall, specificity, accuracy, and ROC curve. The results showed that the MLP model had the highest prediction accuracy, up to 75%, and an AUC of 0.8. The XGBoost model was used to extract the feature importance of growth parameters, revealing that the temperature of the precursor molybdenum source (MoT), reaction temperature (T), and reaction time (t) were the main factors affecting the growth of MoS2 layers. Finally, we use virtual data to predict the results and delineate the range of each growth condition, with 50% of predicted results as the dividing line. The optimization of growth conditions through machine learning algorithms holds promise for enhancing control over the preparation of MoS2 layers, thereby facilitating the development of electronic and optoelectronic devices. [ABSTRACT FROM AUTHOR]

Purpose Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disease of unknown cause with a variable course. Acute exacerbations of IPF (AE-IPF) is sudden accelerations of the disease or a superimposed idiopathic acute injury significantly reducing lung function. To examine the serum concentrations of Progranulin (PGRN) and activin A in patients with AE-IPF in a pilot study. Methods Twenty-one patients with AE-IPF were compared with 23 patients with stable IPF as a control group. Serum PGRN and activin A levels, arterial blood gas measurements, and lung function were determined in these two groups. Results Peripheral blood PGRN and activin A levels in patients with AE-IPF were 83.7 + 10.0 and 14.2 ± 1.7 ng/ml (mean + SD), respectively; higher than those in the control group 61.0 + 5.8 and 5.8 + 1.0 (p < 0.001). PGRN and activin A levels were significantly negatively correlated with carbon monoxide diffusion capacity r = - 0.857 (p < 0.001) and r = - 0.757 (p < 0.001). Conclusion Progranulin (PGRN) and activin A may be involved in the pathogenesis of AE-IPF. They may be possible markers of disease activity in AE-IPF., Author(s): Tian Xie [sup.1], Lizhen Han [sup.2], Yongxing Chen [sup.1], Haihong Wu [sup.1] Author Affiliations: (1) grid.443397.e, 0000 0004 0368 7493, Department of Pulmonary and Critical Care Medicine, Hainan General [...]

Crop wild relatives offer natural variations of disease resistance for crop improvement. Here, we report the isolation of broad-spectrum powdery mildew resistance gene Pm36, originated from wild emmer wheat, that encodes a tandem kinase with a transmembrane domain (WTK7-TM) through the combination of map-based cloning, PacBio SMRT long-read genome sequencing, mutagenesis, and transformation. Mutagenesis assay reveals that the two kinase domains and the transmembrane domain of WTK7-TM are critical for the powdery mildew resistance function. Consistently, in vitro phosphorylation assay shows that two kinase domains are indispensable for the kinase activity of WTK7-TM. Haplotype analysis uncovers that Pm36 is an orphan gene only present in a few wild emmer wheat, indicating its single ancient origin and potential contribution to the current wheat gene pool. Overall, our findings not only provide a powdery mildew resistance gene with great potential in wheat breeding but also sheds light into the mechanism underlying broad-spectrum resistance.Powdery mildew is a fungal leaf disease that reduces yield and grain quality in susceptible wheat varieties. Here, the authors report the cloning of the wild emmer wheat originated powdery mildew resistance gene Pm36 as a membrane associated tandem kinase and its possible resistance mechanism. [ABSTRACT FROM AUTHOR]

As a promising inorganic thermochromic material, VO2 can achieve modulation in the near-infrared band without affecting the visible light transmittance, and thus has been widely used in smart windows. However, VO2-based thermochromic smart windows still face challenges for real-world applications, such as high phase transition temperatures and poor optical properties. In this paper, a solar-driven and high-performance VO2-based thermochromic smart window composite film was prepared by combining Cu7S4, a photothermal material, with traditional VO2 film through simulation and experiment. Its visible light transmittance in low and high temperature environments is Tlum,l = 60.8% and Tlum,h = 55.9%, and the solar modulation ability is as high as ΔTsol = 12.3%. The composite film could be heated to 72 °C by infrared light irradiation and maintain excellent photothermal heating effects even after 100 cycles of heating and cooling. Due to its excellent photothermal and solar radiation regulation performances, the composite film can be widely used in smart windows. [ABSTRACT FROM AUTHOR]