Theoretical investigation into coordination and selectivity of uranyl-unilateral benzotriazole salophens (X = O/S) for R/S-triadimefons

The design of new uranyl-ligands (uranyl-Ls) is of great significance for the separation and utilization of uranium. In this paper, the triazole group was introduced into uranyl-salophen (uranyl-S) to form new asymmetric uranyl-unilateral benzotriazole salophen (uranyl-UBS); we further replaced two oxygen atoms of uranyl-UBS with two sulfur atoms to generate uranyl-unilateral benzotriazole thio-salophen (uranyl-UBTS) as a new receptor. Then, we comprehensively explored coordination models of uranyl-UBS and uranyl-UBTS with R/S-triadimefons (R/S-TDFs) enantiomers as the guests using density functional theory calculations at the B3LYP//RECP/6-311G** level; we then investigated enantioselectivity recognition of the new receptors to the guests R/S-TDFs. The results indicated that the uranium atoms of the receptors uranyl-S, uranyl-UBS and uranyl-UBTS could coordinate with the carbonyl oxygens in guests R/S-TDFs to form complexes of guest-receptors R/S-TDFs-uranyl-Ls that exhibited two stable V-shaped structures with quite different properties. It was found that the coordination ability to the guests R/S-TDFs is uranyl-UBTS > uranyl-UBS > uranyl-S, while the enantioselectivity for the guests is uranyl-UBTS > uranyl-S > uranyl-UBS and, when the receptor is the same, R-TDF has stronger coordination ability than S-TDF. These results provide information and theoretical supports for the experiments of asymmetric uranyl-UBS with R/S-TDFs, and produce a reference for further exploring the coordination characteristics of asymmetric uranyl-salophen with the triazole derivatives.     

Pd based on 2-Aminopyrimidine and 1H-benzo[d]imidazol-2-amine functionalizedFe3O4 nanoparticles as novel recyclable magnetic nanocatalysts for Ullmann coupling reaction

In this study, Pd based on 2-Aminopyrimidine and 1H-benzo[d]imidazol-2-amine functionalized Fe₃O₄ magnetic nanoparticles [(Pd-APM-PSi-Fe₃O₄) and (Pd-BIA-PSi-Fe₃O₄)] was designed and used for the synthesis of di aryl ether by Ulmann cross-coupling reactions. Ulmann reaction performed with mixing of the arylhalides and phenol derivatives in DMF solvent. The prepared catalysts were characterized with various analytical techniques such as FT-IR, XRD, TGA, SEM, TEM, EDX, ICP and VSM. Pd-APM-PSi-Fe₃O₄ and Pd-BIA-PSi-Fe₃O₄ catalysts demonstrated good to excellent yields catalytic efficiency for Ulmann reactions in comparison with to commercial palladium catalysts. The catalyst is easily recycled and reused without loss of the catalytic activity. The combined merits of reusable catalyst conditions make the condensation with safe operation, no leaching of pd into environment, low pollution, rapid access to products and simple workup. Also, these novel magnetic nanocatalysts are superior to the industry standard Pd in every relevant aspect. They feature a way higher initial activity, a much more convenient separation, better recycling, and less contamination of the products. Last but not least, they can be very easily prepared from commercially available Fe₃O₄ nanoparticles using standard laboratory equipment.     

Reconstruction of stability for Gaussian spatial solitons in quintic–septimal nonlinear materials under $${{varvec{mathcal {P}}}}{varvec{mathcal {T}}}$$ P T -symmetric potentials

Nonlinear Dynamics - Gaussian spatial soliton solutions of both the constant-coefficient and variable-coefficient (2 + 1)-dimensional nonlinear Schrödinger equations in...     

Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System

Kinetic energy(KE) functional is crucial to speed up density functional theory calculation. However, deriving it accurately through traditional physics reasoning is challenging. We develop a generally applicable KE functional estimator for a one-dimensional (1D) extended system using a machine learning method. Our end-to-end solution combines the dimensionality reduction method with the Gaussian process regression, and simple scaling method to adapt to various 1D lattices. In addition to reaching chemical accuracy in KE calculation, our estimator also performs well on KE functional derivative prediction. Integrating this machine learning KE functional into the current orbital free density functional theory scheme is able to provide us with expected ground state electron density.     

An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function

As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the estimation of the three angles (i.e., roll, yaw, and pitch) as separate facets, which disregard their interplay as well as differences and thus share the same parameters for all layers; and (2) the discontinuity in angle estimation definitely reduces the accuracy. To solve these two problems, a THESL-Net (tiered head pose estimation with self-adjust loss network) model is proposed in this study. Specifically, first, an idea of stepped estimation using distinct network layers is proposed, gaining a greater freedom during angle estimation. Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply adds the classification and regression losses. Subsequently, a self-adjustment constraint on the loss function is applied, making the angle estimation more consistent. Finally, to examine the influence of different angle ranges on the proposed model, experiments are conducted on three popular public benchmark datasets, BIWI, AFLW2000, and UPNA, demonstrating that the proposed model outperforms the state-of-the-art approaches.     

Multi-Image Encryption Algorithm Based on Cascaded Modulation Chaotic System and Block-Scrambling-Diffusion

To address the problem of a poor security image encryption algorithm based on a single chaotic map, this paper proposes a cascade modulation chaotic system (CMCS) that can generate multiple chaotic maps. On this basis, a multi-image encryption algorithm with block-scrambling-diffusion is proposed using CMCS. The algorithm makes full use of the features of CMCS to achieve the effect of one encryption at a time for images. Firstly, the key-value associated with the plaintexts is generated using a secure hash algorithm-512 (SHA-512) operation and random sequence, and the three images are fully confused by the double scrambling mechanism. Secondly, the scrambled image is converted into a bit-level matrix, and the pixel values are evenly distributed using the bit-group diffusion. Finally, the non-sequence diffusion of hexadecimal addition and subtraction rules is used to improve the security of the encryption algorithm. Experimental results demonstrate that the encryption algorithm proposed in this paper has a good encryption effect and can resist various attacks.     

依状态独立的随机环境中的马氏链

本主要讨论了依状态独立的随机环境中的马氏链，并严格地证明了依状态独立的随机环境中的马氏链，如果在环境不退化的一般情形下，不是时齐马氏链，而环境退化必然是马氏链这个结论。