A novel chemical probe based on the salamo-Co(II) complex for the highly selective fluorescence detection of tyrosine.

A new and rare Salamo-Co(II) complex probe L-Co was designed and synthesised. The structure of the [Co(L)(μ-OAc)(MeOH)]⋅2HO complex was obtained by X-ray diffraction experiments. Three Co(II) atoms are in a line in the complex, and all Co(II) atoms form a 6-coordinated octahedral configuration.

A nonsymmetrical salamo-like fluorescence chemical sensor for selective identification of Cu and BO ions and practical applications.

An innovative salamo-like fluorescent chemical sensor HL, has been prepared that can be utilized to selectively detect Cu and BO ions. Cu ions can bind to oxime state nitrogen and phenol state oxygen atoms in the chemosensor HL, triggering the LMCT effect leading to fluorescence enhancement. The crystal structure of the copper(II) complex, named as [Cu(L)], has been achieved via X-ray crystallography, and the sensing mechanism has been confirmed by further theoretical calculations with DFT.

A symmetric bis(salamo)-like fluorescent chemosensor for identifying HCO and CO and its application.

In this work, we successfully designed and synthesized a methoxydisubstituted bis(salamo)-type fluorescent chemical sensor BS, which can be applied as a highly sensitive and selective fluorescence probe for HCO and CO detection. The LODs of HCO and CO were experimentally calculated to be 5.4068 × 10 M and 4.

Optimizing the Deep Neural Networks by Layer-Wise Refined Pruning and the Acceleration on FPGA.

To accelerate the practical applications of artificial intelligence, this paper proposes a high efficient layer-wise refined pruning method for deep neural networks at the software level and accelerates the inference process at the hardware level on a field-programmable gate array (FPGA). The refined pruning operation is based on the channel-wise importance indexes of each layer and the layer-wise input sparsity of convolutional layers. The method utilizes the characteristics of the native networks without introducing any extra workloads to the training phase.

Taking advantage of hybrid bioinspired intelligent algorithm with decoupled extended Kalman filter for optimizing growing and pruning radial basis function network.

The growing and pruning radial basis function (GAP-RBF) network is a promising sequential learning algorithm for prediction analysis, but the parameter selection of such a network is usually a non-convex problem and makes it difficult to handle. In this paper, a hybrid bioinspired intelligent algorithm is proposed to optimize GAP-RBF. Specifically, the excellent local convergence of particle swarm optimization (PSO) and the extensive search ability of genetic algorithm (GA) are both considered to optimize the weights and bias term of GAP-RBF.