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When calculating the CWSI, previous researchers usually used canopy temperature and atmospheric temperature at the same time. However, it takes some time for the canopy temperature (Tc) to respond to atmospheric temperature (Ta), suggesting the time-lag effects between Ta and Tc. In order to investigate time-lag effects between Ta and Tc on the accuracy of the CWSI inversion of photosynthetic parameters in winter wheat, we conducted an experiment. In this study, four moisture treatments were set up: T1 (95% of field water holding capacity), T2 (80% of field water holding capacity), T3 (65% of field water holding capacity), and T4 (50% of field water holding capacity). We quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis. Based on the time-lag parameter, we modified the CWSI theoretical and empirical models and assessed the impact of time-lag effects on the accuracy of the CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation in the CWSI after time-lag correction. The results show that: (1) The time-lag parameter calculated using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis are 44–70, 32–44, 42–58, and 76–97 min, respectively. (2) The CWSI empirical model corrected by the time-lag mutual information method has the highest correlation with photosynthetic parameters. (3) GA-SVM has the highest prediction accuracy for the CWSI empirical model corrected by the time-lag mutual information method. Considering time lag effects between Ta and Tc effectively enhanced the correlation between CWSI and photosynthetic parameters, which can provide theoretical support for thermal infrared remote sensing to diagnose crop water stress conditions. [ABSTRACT FROM AUTHOR]

Ultrasonic rolling is an effective technique for enhancing surface integrity, and surface integrity is closely related to fatigue performance. The process parameters of ultrasonic rolling critically affect the improvement of surface integrity. This study proposes an optimization method for process parameters by combining machine learning (ML) with the NSGA-II. Five ML models were trained to establish relationships between process parameters and surface residual stress, hardness, and surface roughness by incorporating feature augmentation and physical information. The best-performing model was selected and integrated with NSGA-II for multi-objective optimization. Ultrasonic rolling tests based on a uniform design were performed, and a dataset was established. The objective was to maximize surface residual stress and hardness while minimizing surface roughness. For test specimens with an initial surface roughness of 0.54 µm, the optimized process parameters were a static pressure of 900 N, a spindle speed of 75 rpm, a feed rate of 0.19 mm/r, and rolling once. Using optimized parameters, the surface residual stress reached −920.60 MPa, surface hardness achieved 958.23 HV, surface roughness reduced to 0.32 µm, and contact fatigue life extended to 3.02 × 107 cycles, representing a 52.5% improvement compared to untreated specimens and an even more significant improvement over without parameter optimization. [ABSTRACT FROM AUTHOR]

Diabetic wound microenvironment has extremely complex continuous hyperglycemia, hypoxia, bacterial infection, and reactive oxygen species accumulation, which lead to slow wound healing. In order to overcome these limitations, glucose oxidase (GOX) and catalase (CAT) were loaded into zinc sulfide (ZnS) particle spheres to construct a high glucose microenvironment responsive treatment strategy with good biocompatibility. When they encounter hyperglycemia and oxidative wound microenvironment at the wound site, the presence of GOX and CAT will be activated by glucose to undergo an effective local cascade catalytic cycle reaction, which decomposes glucose and H2O2 into gluconic acid and O2. They not only reduce the toxic side effects of GOX, improve the hyperglycaemic wound microenvironment, lower the local pH, maintain, and alleviate O2 depletion problems but also trigger the ZnS particle spheres to convert the high glucose microenvironment, which is not conducive to tissue and cell survival, into a favorable environment (zinc gluconate and H2S gas). These products can not only synergistically accelerate cell migration and proliferation in a high glucose environment and play a role in starvation and inhibition of killing bacteria but also enhance the activity of enzyme proteins and achieve long-term effective treatment. [ABSTRACT FROM AUTHOR]

Currently, advanced biomaterial design solutions often have more than two kinds of nanotechnology design strategies, but there is no suitable classification to describe these designs systematically. Based on the material design ideas and the modes of implementing functions, this article exemplifies and proposes a new nanotechnology classification that includes physical properties, the chemical reactions that respond to the microenvironment and bio-inspired incorporation. If two or more nanotechnology designs in the same classification are to be integrated into the same biological material, it is necessary to analyze the integration conflict between the designs. With the development of big data, this classification method may help researchers and artificial intelligence to realize automated integration of multiple designs and provide new material nanotechnology design integration solutions.

Quantitative analysis of harmful patina components on the surface of bronzeware is crucial as patina can corrode artifacts. Surface-enhanced Raman spectroscopy (SERS) has a common application in surface analysis. However, there exists an absence of documented investigations concerning the quantitative analysis of detrimental patina, particularly the chloride patina. To achieve high sensitivity in targeting harmful patina, we report a fabrication method for constructing Ag/Au alloys in the pores and on the surface of UiO-66 (zirconium 1,4-dicarboxybenzene metal–organic framework) nanomaterials, resulting in Ag/Au/UiO-66 nanoparticle monolayers formed by self-assembly at the oil–water interface. By incorporating them onto the surface of PVA hydrogel, we obtained SERS sensor devices with excellent flexibility, outstanding optical transparency, and ultrahigh activity. These sensor devices efficiently sample irregular surfaces and detect micrometer-scale specimen. They significantly enhance the Raman characteristic peaks of measured objects, facilitating precise analysis of the patina composition on the surface of bronze artifacts. The Raman peak at 118 cm–1 was boosted from 367 to 11,909 for detecting harmful patina, and the enhancement factor of the SERS substrate was calculated to be 3.6 ×1010 for detecting the Raman peak of rhodamine 6G (R6G), which could be easily detected at 610 cm–1 at a concentration of 10–13 mol/L (M). The Raman peaks at 610 cm–1 from the SERS substrate could also be readily detected. By grafting the organic fluorescent group amino-MQAE onto the surface of Ag/Au/UiO-66 nanoparticles (NPs), the content of harmful patina on the surface of bronze artifacts treated for different durations was indirectly determined. These results indicate the successful construction of the Ag/Au alloy on the surface of UiO-66 NPs, resulting in SERS sensor devices with excellent flexibility, optical transparency, and ultrahigh sensitivity. [ABSTRACT FROM AUTHOR]

In the service process of gears, premature fatigue failure or fracture of gears is often caused by poor surface performance. 20CrNiMo steel is a commonly used material for gears. Laser-shock peening (LSP), carburizing treatment (CT), and hybrid modification of carburizing treatment and laser-shock peening (LSP + CT) were carried out to improve the performance of 20CrNiMo steel. The hardness, residual stress, microstructure, subgrain size, and toughness of the samples were analyzed following various modification methods. It was observed that the properties of the composite-modified gradient structure materials achieved through carburizing and laser-shock peening were superior to those modified using single methods. After the composite treatment of carburizing and laser shocking, the samples exhibited the most significant increase in hardness, up to 916HV0.1, with a surface layer experiencing residual compressive stress as low as −635 MPa. Simultaneously, a gradient microstructure was formed on the surface layer, with 80% of the crystallites being in the nanoscale range. Furthermore, the toughness was notably enhanced. Experimental results confirm the improvement in the properties of 20CrNiMo samples, resulting in the creation of a functionally graded material through the composite treatment of carburizing and laser shocking. [ABSTRACT FROM AUTHOR]