Data assimilation and pollution forecasting in Burgers' equation with model error function

This article presents a correction method for a better resolution of the problem of estimating and predicting pollution, governed by Burgers' equations. The originality of the method consists in the introduction of an error function into the system's equations of state to model uncertainty in the model. The initial conditions and diffusion coefficients, present in the equations for pollution and concentration, and also those in the model error equations, are estimated by solving a data assimilation problem. The efficiency of the correction method is compared with that produced by the traditional method without introduction of an error function.Three test cases are presented in this study in order to compare the performances of the proposed methods. In the first two tests, the reference is the analytical solution and the last test is formulated as part of the “twin experiment”.The numerical results obtained confirm the important role of the model error equation for improving the prediction capability of the system, in terms of both accuracy and speed of convergence.     

Efficient Syntheses of Traumatic Lactone and Rhizobialide

Herein, we report the total synthesis of traumatic lactone and rhizobialide by utilizing allenoic acid to construct the lactone ring. The key starting materials, allenoic acids, could be prepared by the ATA (allenation of terminal alkynes) of a terminal alkyne with an aldehyde that contained a protected hydroxyl group followed by hydrolysis. Importantly, the asymmetric synthesis could be realized just by replacing racemic diphenylprinol with (R)- or (S)-diphenylprinol to deliver the optically active allenoate.     

Hexanuclear 3d − 4f metal–organic cages assembled from a carboxylic acid‐functionalized tris‐triazamacrocycle for highly selective fluorescent sensing of picric acid

Two Ln^III ions are sandwiched by dinuclear Co^II building blocks derived from a tris‐triazamacrocyclic ligand bearing pendant carboxylic acid functionality, 1,3,5‐tris((4,7‐bis(2‐carboxyethyl)‐1,4,7‐triazacyclonon‐1‐yl)methyl)‐benzene (H₆L), giving rising to two nanoscale heterometallic metal–organic cages formulated as [Co₄Ln₂(LH_2.5)₂(H₂O)₄]·(ClO₄)₆·NO₃·nH₂O [Ln = Dy, n = 12 ( 1 ); Ln = Yb, n = 9 ( 2 )], whose internal cavity accommodates a guest NO₃^? anion. Their hexanuclear cage‐like architectures are maintained both in solution and solid states as confirmed by mass spectrum as well as X‐ray diffraction experiments. These two cages display ligand‐based fluorescence emissions and therefore both were chosen to be operated as fluorescent chemosensors for the detection of nitroaromatic compounds. Attractively, these metal–organic cages allow highly selective and sensitive detection of picric acid (PA) over other nitroaromatics in solution and suspension, and the fluorescence resonance energy transfer (FRET) between the cage probes and PA is mainly responsible for the remarkable detection efficiency.     

Synthesis,characterization, and electrochemistry of monophosphine‐containing diiron propane‐1,2‐dithiolate complexes related to the active site of [FeFe]‐hydrogenases

Five monophosphine‐substituted diiron propane‐1,2‐dithiolate complexes as the active site models of [FeFe]‐hydrogenases have been synthesized and characterized. Reactions of complex [Fe₂(CO)₆{μ‐SCH₂CH(CH₃)S}] ( 1 ) with a monophosphine ligand tris(4‐methylphenyl)phosphine, diphenyl‐2‐pyridylphosphine, tris(4‐chlorophenyl)phosphine, triphenylphosphine, or tris(4‐fluorophenyl)phosphine in the presence of the oxidative agent Me₃NO·2H₂O gave the monophosphine‐substituted diiron complexes [Fe₂(CO)₅(L){μ‐SCH₂CH(CH₃)S}] [L = P(4‐C₆H₄CH₃)₃, 2 ; Ph₂P(2‐C₅H₄N), 3 ; P(4‐C₆H₄Cl)₃, 4 ; PPh₃, 5 ; P(4‐C₆H₄F)₃, 6 ] in 81%–94% yields. Complexes 2 – 6 have been characterized by elemental analysis, spectroscopy, and X‐ray crystallography. In addition, electrochemical studies revealed that these complexes can catalyze the reduction of protons to H₂ in the presence of HOAc.     

Fabrication of Microporous Metal–Organic Frameworks in Uninterrupted Mesoporous Tunnels: Hierarchical Structure for Efficient Trypsin Immobilization and Stabilization

Hierarchically porous metal–organic frameworks (HP-MOFs) are promising in various applications. Most reported HP-MOFs are prepared based on the generation of mesopores in microporous frameworks, and the formed mesopores are connected by microporous channels, limiting the accessibility of mesopores for bulky molecules. A hierarchical structure is formed by constructing microporous MOFs in uninterrupted mesoporous tunnels. Using the confined space in as-prepared mesoporous silica, highly dispersed metal precursors for MOFs are coated on the internal surface of mesoporous tunnels. Ligand vapor-induced crystallization is employed to enable quantitative formation of MOFs in situ, in which sublimated ligands diffuse into mesoporous tunnels and react with metal precursors. The obtained hierarchically porous composites exhibit record-high adsorption capacity for the bulky molecule trypsin. The thermal and storage stability of trypsin is improved upon immobilization on the composites.     

Synthesis of Axially Chiral Styrenes through Pd-Catalyzed Asymmetric C−H Olefination Enabled by an Amino Amide Transient Directing Group

The atroposelective synthesis of axially chiral styrenes remains a formidable challenge due to their relatively lower rotational barriers compared to the biaryl atropoisomers. Herein, we describe the construction of axially chiral styrenes through Pd^II-catalyzed atroposelective C−H olefination, using a bulky amino amide as a transient chiral auxiliary. Various axially chiral styrenes were produced with good yields and high enantioselectivity (up to 95 % yield and 99 % ee). Carboxylic acid derivatives of the resulting axially chiral styrenes showed superior enantiocontrol over the biaryl counterparts in Co^III-catalyzed enantioselective C(sp³)−H amidation of thioamide. Mechanistic studies suggest that C−H cleavage is the enantioselectivity-determining step.     

组合式十字形钢管混凝土柱偏压力学性能试验研究

设计了一种由槽钢和方形钢管拼焊形成的组合式十字形钢管混凝土柱,将其灵活地布置在框架结构的中节点,可使柱肢与填充墙等厚,有效地提高建筑使用面积。共制作了6根组合式十字形钢管混凝土柱试件,考虑了偏心距和长细比两种变化参数。通过对其进行偏心受压试验研究,考察了试件的破坏形态和荷载-挠度曲线,并分析了其在不同偏心距和长细比下的荷载-应变曲线发展规律。结果表明:组合式十字形钢管混凝土柱中钢管和槽钢对混凝土的约束作用强,表现为较高的延性系数;偏心距或长细比越大,试件的极限承载力及弹性刚度越小,且偏心距越大延性越好,长细比对延性影响不显著;在受拉侧纵向应变基本上符合平截面假定,在受压侧纵向应变不符合平截面假定。     

Modeling and simulation of weld line location and properties during injection molding based on viscoelastic constitutive equation

Rheologica Acta - Weld lines are one of the typical quality issues of injection molded parts. They are unavoidable even for moderate complex products. To explore the formation of weld lines in...     

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions

Distance weighted discrimination (DWD) is an appealing classification method that is capable of overcoming data piling problems in high-dimensional settings. Especially when various sparsity structures are assumed in these settings, variable selection in multicategory classification poses great challenges. In this paper, we propose a multicategory generalized DWD (MgDWD) method that maintains intrinsic variable group structures during selection using a sparse group lasso penalty. Theoretically, we derive minimizer uniqueness for the penalized MgDWD loss function and consistency properties for the proposed classifier. We further develop an efficient algorithm based on the proximal operator to solve the optimization problem. The performance of MgDWD is evaluated using finite sample simulations and miRNA data from an HIV study.     

Pharmacophore features for machine learning in pharmaceutical virtual screening

Molecular Diversity - Methods of three-dimensional molecular alignment generally treat all pharmacophore features equally when superimposing. However, some pharmacophore features can be more...