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Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R2 were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R2 in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R2 in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. [ABSTRACT FROM AUTHOR]

Optoelectronic synapses are considered to be important cornerstones in the construction of neuromorphic computing systems because of their low power consumption, high operating speeds, and high scalability. In this work, we demonstrate an optoelectronic synaptic device based on a ZnO/HfOx heterojunction in which optical potentiation/electrical depression behaviors and nonvolatile high current state can be implemented. The heterojunction device exhibits conductance evolution with high linearity. The excellent optoelectronic memristive behavior of the device can be attributed to the interface barrier between ZnO and HfOx, which hinders the recombination of photo-excited electron–hole pairs to increase the carrier lifetime, and realizes the nonvolatile high current state. More importantly, the artificial vision system based on optoelectronic synaptic devices can achieved a high recognition accuracy of 96.1%. Our work provides a feasible pathway toward the development of optoelectronic synaptic devices for use in high-performance neuromorphic vision systems. [ABSTRACT FROM AUTHOR]

This paper investigated the influence of water-borne epoxy resin emulsion (WER) dosage on the properties of modified emulsified asphalt and analyzed the modification mechanism of WER on emulsified asphalt. Water-borne epoxy modified emulsified asphalt was used to prepare MS-2 micro-surfacing mixture, and dispersed demulsifier was introduced to improve the construction performance of the mixture. The best content of each component and the best asphalt -aggregate ratio were obtained through orthogonal test. The results show that 6% water-borne epoxy emulsion asphalt has the best compatibility and storage stability. With the increase of water-borne epoxy content, the low-temperature performance of water-borne epoxy emulsion asphalt becomes worse and the anti-aging performance increases. The optimum asphalt-aggregate ratio of MS-2 micro-surfacing is 7.5%. When the added water is 3%, the cement content is 1%, and the dispersed demulsifier content is 0.25%, the prepared micro-surfacing has good water damage resistance, rutting resistance, tensile strength and cohesiveness, which can meet the normal use requirements of pavement. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a growing emphasis on investigating the role of immunotherapy in cancer treatment. However, it faces challenges such as limited efficacy, a diminished response rate, and serious adverse effects. Nanozymes, a subset of nanomaterials, demonstrate boundless potential in cancer catalytic therapy for their tunable activity, enhanced stability, and cost-effectiveness. By selectively targeting the metabolic vulnerabilities of tumors, they can effectively intensify the destruction of tumor cells and promote the release of antigenic substances, thereby eliciting immune clearance responses and impeding tumor progression. Combined with other therapies, they synergistically enhance the efficacy of immunotherapy. Hence, a large number of metabolism-regulating nanozymes with synergistic immunotherapeutic effects have been developed. This review summarizes recent advancements in cancer immunotherapy facilitated by nanozymes, focusing on engineering nanozymes to potentiate antitumor immune responses by disturbing tumor metabolism and performing synergistic treatment. The challenges and prospects in this field are outlined. We aim to provide guidance for nanozyme-mediated immunotherapy and pave the way for achieving durable tumor eradication.