Transparent niobate glass-ceramics for optical limiting

The construction of nonlinear optical materials featuring asymmetric transmission of light is of great technological importance for various applications, including optical switching and optical power limiting. A significant challenge is the scalable fabrication of material candidates with good photochemical stability, high optical transmittance, and excellent optical limiting performance. Here, we present a nanocrystallization avenue for constructing hybrid optical limiting materials that exhibit ultrafast and robust optical limiting performance. The experimental results show that the controllable relaxation of a niobate glass may lead to the clustering of Nb-O units and contracting of the bandgap. It results in the notable improvement in nonlinear optical properties, including the enhanced saturation irradiance (380 GW/cm²), doubly increased nonlinear coefficient, and decreased limiting threshold (200 GW/cm²). Our results suggest a promising material that exhibits promising applications for protecting eyes and sensitive components from laser-induced damage.     

简易棋盘格图像标定系统应用

棋盘格图像在摄像机标定中有非常广泛的应用,对于复杂环境且测量精度要求非常高的场合,会需要特定的且有一定复杂度的算法,但通常情况下,使用这样的标定系统是没有必要的,且会让初次应用的人觉得困难。这里提供一套应用简单,快速,对棋盘格图像具有旋转不变性,良好的鲁棒性,且对非复杂环境具有高精度的标定系统。所有算法均是在前辈们的算法上稍作改变而来,应用效果非常好。     

Visual Detection of Adenosine Triphosphate by Taylor Rising: A Simple Point-of-Care Testing Method Based on Rolling Circle Amplification

Rapid and sensitive point-of-care testing (POCT) is an extremely critical mission in practical applications, especially for rigorous military medicine, home health care, and in the third world. Here, we report a visual POCT method for adenosine triphosphate (ATP) detection based on Taylor rising in the corner of quadratic geometries between two rod surfaces. We discuss the principle of Taylor rising, demonstrating that it is significantly influenced by contact angle, surface tension, and density of the sample, which are controlled by ATP-dependent rolling circle amplification (RCA). In the presence of ATP, RCA reaction effectively suppresses Taylor-rising behavior, due to the increased contact angle, density, and decreased surface tension. Without addition of ATP, untriggered RCA reaction is favorable for Taylor rising, resulting in a significant height. With this proposed method, visual sensitive detection of ATP without the aid of other instruments is realized with only a 5 μL droplet, which has good selectivity and a low detection limit (17 nM). Importantly, this visual method provides a promising POCT tool for user-friendly molecular diagnostics.     

Analysis of Phytochemical Composition of Camellia oleifera Oil and Evaluation of its Anti-Inflammatory Effect in Lipopolysaccharide-Stimulated RAW 264.7 Macrophages

In China, Camellia oleifera oil (COO) is not only a common edible oil but also a traditional remedy widely applied to ameliorate a variety of illnesses associated with inflammation, such as mouth ulcers, thrush, eczema, etc. However, there has been a lack of relevant biological research on the anti-inflammatory capacity of COO, and the specific bioactive lipid phytochemicals contributing to the anti-inflammatory effect need further research. In this study, the RAW 264.7 macrophages model was used to investigate the anti-inflammatory capacity of COO. Our data showed that 33–200 μg/mL COO markedly inhibited the lipopolysaccharide lipopolysaccharide (LPS)-stimulated nitric oxide (NO.) secretion via the suppression of Nos2 and Cox-2 expression. The enzyme immunoassay confirmed that COO also exhibited a strong suppressive effect on the expression of proinflammatory cytokines such as Tnf-α and Il-6. To further explore the correlation between the anti-inflammatory effects and the lipid phytochemicals in COO, 10 samples were collected and screened for their chemical compositions. It was interestingly demonstrated that the polyphenol extracts of COO play a vital role in its anti-inflammatory properties. In addition, an oil-in-water (O/W) emulsion-based system was also developed to deliver the liposoluble COO into the cells, and the feasibility of this system was confirmed. Our research confirms the anti-inflammatory potential of COO and highlights that the main functional ingredient is polyphenol extracts. This may provide a scientific basis for the comprehensive utilization and development of COO and related functional foods.     

工业脱硝技术应用现状及展望

选择性催化还原法(SCR)、选择性非催化还原法(SNCR)、碱液吸收法、酸吸收法、O_3氧化吸收法以及等离子体活化法是目前工业脱硝技术中的主要技术。详细论述了上述脱硝技术的原理、工艺流程及发展前景,分析对比了上述工业脱硝技术的脱硝率、优缺点及应用现状,预测判断了中国工业脱硝技术的未来发展方向。     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.