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Supports can widely affect or even dominate the catalytic activity, selectivity, and stability of metal nanoparticles through various metal-support interactions (MSIs). However, underlying principles have not been fully understood yet, because MSIs are influenced by the composition, size, and facet of both metals and supports. Using Ru/TiO 2 supported on rutile and anatase as model catalysts, we demonstrate that metal-support interfacial compatibility can critically control MSI modes and catalytic performances in CO 2 hydrogenation. Annealing Ru/rutile-TiO 2 in air can enhance CO 2 conversion to methane resulting from enhanced interfacial coupling driven by matched lattices of RuO x with rutile-TiO 2 ; annealing Ru/anatase-TiO 2 in air decreases CO 2 conversion and converts the product into CO owing to strong metal-support interaction (SMSI). Although rutile and anatase share the same chemical composition, we show that interfacial compatibility can basically modify metal-support coupling strength, catalyst morphology, surface atomic configuration, MSI mode, and catalytic performances of Ru/TiO 2 in heterogeneous catalysis. Supports can largely affect the catalytic performance of metal nanoparticles, but the underlying principles are not yet fully understood. Here the authors demonstrate that metal-support interfacial compatibility of Ru/TiO 2 can critically control the metal-support interaction modes and the catalytic performances in CO 2 hydrogenation.

Liver disease is a global public problem, and the cost of its therapy is a large financial burden to governments. It is well known that drug therapy plays a critical role in the treatment of liver disease. However, present drugs are far from meeting clinical needs. Lots of efforts have been made to find novel agents to treat liver disease in the past several decades. Acacetin is a dihydroxy and monomethoxy flavone, named 5,7-dihydroxy-4′-methoxyflavone, which can be found in diverse plants. It has been reported that acacetin exhibits multiple pharmacological activities, including anti-cancer, anti-inflammation, anti-virus, anti-obesity, and anti-oxidation. These studies indicate the therapeutic potential of acacetin in liver disease. This review discussed the comprehensive information on the pathogenesis of liver disease (cirrhosis, viral hepatitis, drug-induced liver injury, and hepatocellular carcinoma), then introduced the biological source, structural features, and pharmacological properties of acacetin, and the possible application in preventing liver disease along with the pharmacokinetic and toxicity of acacetin, and future research directions. We systemically summarized the latest research progress on the potential therapeutic effect of acacetin on liver disease and existing problems. Based on the present published information, the natural flavone acacetin is an anticipated candidate agent for the treatment of liver disease.

Image classification and segmentation are important tasks in computer vision. ResNet and U-Net are representative networks for image classification and image segmentation, respectively. Although many scholars used to fuse these two networks, most integration focuses on image segmentation with U-Net, overlooking the capabilities of ResNet for image classification. In this paper, we propose a novel U-ResNet structure by combining U-Net’s convolution–deconvolution structure (UBlock) with ResNet’s residual structure (ResBlock) in a parallel manner. This novel parallel structure achieves rapid convergence and high accuracy in image classification and segmentation while also efficiently alleviating the vanishing gradient problem. Specifically, in the UBlock, the pixel-level features of both high- and low-resolution images are extracted and processed. In the ResBlock, a Selected Upsampling (SU) module was introduced to enhance performance on low-resolution datasets, and an improved Efficient Upsampling Convolutional Block (EUCB*) with a Channel Shuffle mechanism was added before the output of the ResBlock to enhance network convergence. Features from both the ResBlock and UBlock were merged for better decision making. This architecture outperformed the state-of-the-art (SOTA) models in both image classification and segmentation tasks on open-source and private datasets. Functions of individual modules were further verified via ablation studies. The superiority of the proposed U-ResNet displays strong feasibility and potential for advanced cross-paradigm tasks in computer vision.

The contribution of the reverse spillover effect to hydrogen generation reactions is still controversial. Herein, the promotion functions for reverse spillover in the ammonia borane hydrolysis reaction are proven by constructing a spatially separated NiO/Al 2 O 3 /Pt bicomponent catalyst via atomic layer deposition and performing in situ quick X-ray absorption near-edge structure (XANES) characterization. For the NiO/Al 2 O 3 /Pt catalyst, NiO and Pt nanoparticles are attached to the outer and inner surfaces of Al 2 O 3 nanotubes, respectively. In situ XANES results reveal that for ammonia borane hydrolysis, the H species generated at NiO sites spill across the support to the Pt sites reversely. The reverse spillover effects account for enhanced H 2 generation rates for NiO/Al 2 O 3 /Pt. For the CoO x /Al 2 O 3 /Pt and NiO/TiO 2 /Pt catalysts, reverse spillover effects are also confirmed. We believe that an in-depth understanding of the reverse effects will be helpful to clarify the catalytic mechanisms and provide a guide for designing highly efficient catalysts for hydrogen generation reactions. H 2 is regarded as an attractive green fuel and a promising energy carrier for the future. Here, the promotion functions of reverse spillover effects in the H 2 generation reactions over bicomponent catalysts are revealed by performing in situ X ray absorption near edge structure characterization.

Quasi-mode parametric instabilities (PI) can result in significant power loss in RF-plasma interactions. However, theoretical difficulties still remain in the analysis of their saturation mechanisms within the framework of the Wenzel-Kramers-Brillouin (WKB) approximation, mainly due to the uncertainty of the impact of an inhomogeneous plasma. In this article, we present a systematic theoretical model under the WKB framework to analyze the saturation process of quasi-mode PIs, using the spatial gradient of the dielectric function to replace the linear dispersion relation to evaluate the effects of plasma inhomogeneity. The amplification factor of a quasi-mode PI in an inhomogeneous plasma with a finite pump profile is calculated using such a method to analyze the dominant mechanism of saturation.

The synergistic nature of bicomponent catalysts remains a challenging issue, due to the difficulty in constructing well-defined catalytic systems. Here we study the origin of synergistic effects in CoO x -Pt catalysts for selective hydrogenation by designing a series of closely contacted CoO x Pt/TiO 2 and spatially separated CoO x /TiO 2 /Pt catalysts by atomic layer deposition (ALD). For CoO x /TiO 2 /Pt, CoO x and platinum are separated by the walls of titania nanotubes, and the CoO x -Pt intimacy can be precisely tuned. Like CoO x Pt/TiO 2 , the CoO x /TiO 2 /Pt shows higher selectivity to cinnamyl alcohol than monometallic TiO 2 /Pt, indicating that the CoO x -Pt nanoscale intimacy almost has no influence on the selectivity. The enhanced selectivity is ascribed to the increased oxygen vacancy resulting from the promoted hydrogen spillover. Moreover, platinum-oxygen vacancy interfacial sites are identified as the active sites by selectively covering CoO x or platinum by ALD. Our study provides a guide for the understanding of synergistic nature in bicomponent and bifunctional catalysts. The development of catalysts with high activity and selectivity for hydrogenation remains a challenge. Here the authors report cobalt oxide-platinum catalysts with increased oxygen vacancy resulting from the promoted hydrogen spillover with platinum-oxygen vacancies as active sites.

This study presents fractional-order Kalman filers for linear fractional-order systems with colored noises using Tustin generating function. A continuous-time fractional-order system with the fractional-order colored process noise is discretized by Tustin generating function. The augmented vector consists of the state and the colored noise is offered to construct an augmented system based on the discretized state equation of a fractional-order system and the colored process noise. The Tustin fractional-order Kalman filter is designed based on the augmented system to obtain the state estimation, effectively. Besides, the colored noise involved in the measurement of a continuous-time fractional-order system is also discussed, and the corresponding Tustin fractional-order Kalman filter is provided in this study. Two illustrative examples are given to verify the effectiveness of Tustin fractional-order Kalman filters for the colored process and measurement noises.

Laser heating of gold nanospheres (GNS) is increasingly prevalent in biomedical applications due to tunable optical properties that determine heating efficiency. Although many geometric parameters (i.e. size, morphology) can affect optical properties of individual GNS and their heating, no specific studies of how GNS aggregation affects heating have been carried out. We posit here that aggregation, which can occur within some biological systems, will significantly impact the optical and therefore heating properties of GNS. To address this, we employed discrete dipole approximation (DDA) simulations, Ultraviolet–Visible spectroscopy (UV–Vis) and laser calorimetry on GNS primary particles with diameters (5, 16, 30 nm) and their aggregates that contain 2 to 30 GNS particles. DDA shows that aggregation can reduce the extinction cross-section on a per particle basis by 17–28%. Experimental measurement by UV–Vis and laser calorimetry on aggregates also show up to a 25% reduction in extinction coefficient and significantly lower heating (~ 10%) compared to dispersed GNS. In addition, comparison of select aggregates shows even larger extinction cross section drops in sparse vs. dense aggregates. This work shows that GNS aggregation can change optical properties and reduce heating and provides a new framework for exploring this effect during laser heating of nanomaterial solutions.

In this paper, the fractal derivative is introduced into a neural network-based discrete-time fractional-order PID controller in two areas, namely, in the controller’s structure and in the parameter optimization algorithm. The first use of the fractal derivative is to reconstruct the fractional-order PID controller by using the Hausdorff difference and Hausdorff sum derived from the Hausdorff derivative and Hausdorff integral. It can avoid the derivation of the Gamma function for the order updating to realize the parameter and order tuning based on neural networks. The other use is the optimization of order and parameters by using Borges derivative. Borges derivative is a kind of fractal derivative as a local fractional-order derivative. The chain rule of composite function is consistent with the integral-order derivative. It is suitable for updating the parameters and the order of the fractional-order PID controller based on neural networks. This paper improves the neural network-based PID controller in two aspects, which accelerates the response speed and improves the control accuracy. Two illustrative examples are given to verify the effectiveness of the proposed neural network-based discrete-time fractional-order PID control scheme with fractal derivatives.

Although the association between CVD and various cancers has been extensively studied, its relationship with NMSC remains ambiguous. Previous studies have shown that cardiovascular disease (CVD) is an independent risk factor for tumorigenesis. However, the relationship between CVD and non-melanoma skin cancer (NMSC) is unclear. The aim of this study is to investigate the potential relationship between CVD and NMSC and whether obesity and inflammation mediate the association. 7424 participants from the National Health and Nutrition Examination Survey (NHANES) from 2015 to 2018 were included. Diagnosis of CVD and NMSC was determined by questionnaire combined with self-reported. Inflammatory markers and obesity indices assessed were SIRI, SII, BMI, and WWI. Logistic regression and Pearson correlation analyses were applied to investigate the relationship between the above key variables. Logistic regression results showed that CVD was a risk factor for NMSC (OR: 1.83, 95% CI: 1.01 ~ 3.34, p = 0.048); however, there was no statistically significant association between CVD subgroups and NMSC. In addition, SIRI, BMI, and WWI partially mediated the association between CVD and NMSC (p < .001), but SII did not alter the relationship (p > 0.05). Bootstrap test confirmed the stability of the results of the mediation analysis. CVD increases the risk of developing NMSC, and obesity and inflammation partially mediate the relationship. Weight loss and control of inflammation may be beneficial in reducing the prevalence of CVD and NMSC.