By ingeniously using a (imino)coumarin-precursor, three reactive fluorogenic probes of MP, FP, and FMP have been fabricated in a single facile synthetic route. MP and FP are able to respectively act as selective "turn-on" fluorescent probes for detecting Hg 2+ and Fin buffer solution via specific analyte-induced reactions. Linear ranges for the detection of Hg 2+ and Fare 0-10 µM and 0-100 µM with the limits of detection (LODs) of 4.0 × 10 -8 M and 1.14 × 10 -6 M (3δ/slope), respectively. FMP is able to work as a molecular "AND" logic gate-based fluorogenic probe for monitoring the coexistence of Hg 2+ and Fvia a multistep reaction cascade.The analytes-induced sensing mechanisms have been determined by using high-performance liquid chromatography analysis (HPLC). In addition, three probes show negligible toxicity under the experimental conditions, and are successfully used for monitoring Hg 2+ and Fin living cells with good cell permeability. The success of the work demonstrates that ingenious utility of specific analyte-induced reactions and conventional concepts on the appropriate molecular scaffold can definitely deliver tailor-made probes for various intended sensing purposes.

Following the traditional total variational denoising model in removing medical image noise with blurred image texture details, among other problems, an adaptive medical image fractional-order total variational denoising model with an improved sparrow search algorithm is proposed in this study. This algorithm combines the characteristics of fractional-order differential operators and total variational models. The model preserves the weak texture region of the image improvement based on the unique amplitude-frequency characteristics of the fractional-order differential operator. The order of the fractional-order differential operator is adaptively determined by the improved sparrow search algorithm using both the sine search strategy and the diversity variation processing strategy, which can greatly improve the denoising ability of the fractional-order differential operator. The experimental results reveal that the model not only achieves the adaptivity of fractional-order total variable differential order, but also can effectively remove noise, preserve the texture structure of the image to the maximum extent, and improve the peak signal-to-noise ratio of the image; it also displays favorable prospects for applications in medical image denoising.

The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent.