Selective Convergence to Atropisomers of a Porphyrin Derivative Having Bulky Substituents at the Periphery.

Four kinds of possible atropisomers of a porphyrin derivative (1), having mesityl groups at one of the o-positions of each meso-aryl group, can be selectively converged to targeted atropisomers among the four isomers (αααα, αααß, αßαß, and ααßß) under appropriate conditions for each atropisomer. For example, protonation and subsequent neutralization of a free base porphyrin (H2-1) induces a convergence reaction to the αßαß atropisomer, H2-1-αßαß, from an atropisomeric mixture. The αααα isomer, H2-1-αααα, was also obtained by heating a solution of H2-1 in CHCl3 in 60% isolated yield, probably owing to a template effect of the solvent molecule. Remarkably, when an atropisomeric mixture of its zinc complex, Zn-1, was heated at 70 °C in a ClCH2CH2Cl/MeOH mixed solvent, crystals composed of only Zn-1-αααα were formed. The hydrophobic space formed by the four mesityl groups in the αααα isomer can be used for repeatable molecular encapsulation of benzene, and the encapsulation structure was elucidated by powder X-ray diffraction analysis. Heating the solid of an atropisomeric mixture of Zn-1 to 400 °C afforded the ααßß isomer almost quantitatively. On the other hand, the solid of H2-1-αααα can be converted by heating, successively to H2-1-αααß at 286 °C and then to H2-1-ααßß at 350 °C.

A dynamic novel approach for bid/no-bid decision-making.

The process of bid/no-bid decision-making is su bjected to uncertainty and influence of complex criteria. This paper proposed an application of the integration of rough sets (RS) and improved general regression neural network (GRNN) based on niche particle swarm optimization (NPSO) algorithm for tendering decision making. The decision table of RS and the attribution reduction was processed by MIBARK algorithm to simply the samples of GRNN. In order to improve the general regression neural network (GRNN) network performance, the niche particle swarm optimization (NPSO) was used to optimize the spread parameter σ of GRNN neural network, then a novel Bid/no-bid decision model was established based on RS and NPSO-GRNN neural network algorithm. The applicability of the proposed model was tested using real cases in Beijing. The results indicate that NPSO-GRNN algorithm has an advantage such as in prediction accuracy and generalization ability. The proposed decision support system approach is useful to help manager to make better Bid/no-bid decisions in uncertain construction markets, so they can take steps to prevent bid distress.

Inhibitory mechanisms in single negative priming from ignored and briefly flashed primes: the key role of the inter-stimulus interval.

The influence of interstimulus intervals (ISIs) on priming effects was investigated using a single-prime negative priming (NP) paradigm. In all experiments, a brief (16ms), centrally displayed prime (a Chinese character, to be ignored) appeared, followed by a pattern mask and then a centrally displayed target (another semantically related or unrelated Chinese character); the task required semantic categorization (animate/inanimate) of the target. An ISI could occur either between prime and mask (Experiments 1 and 5) or between mask and target (Experiments 2-4). The results revealed NP when a 470ms ISI occurred between prime and mask (Experiments 1 and 5) and when a 700ms ISI occurred between mask and target (Experiments 3 and 4). In contrast, when a long prime-target SOA was maintained but the mask-target ISI was shortened, NP disappeared (Experiment 4). The results indicated that a persisting mask/distractor (without ISI) located in the same position as the following target interfered with the buildup of inhibition, but an ISI between prime and mask or mask and target eliminated this interference, and that inhibition processes induced by an ignore instruction were implemented faster with an ISI placed between prime and mask than with an ISI placed between mask and target.