Secondary metabolites from the wood bark of Durio zibethinus and Durio kutejensis

Phytochemical exploration of a wood bark extract from Durio zibethinus afforded two new triterpenoids, namely, methyl 27-O-trans-caffeoylcylicodiscate (1) and methyl 27-O-cis-caffeoylcylicodiscate (2), a new phenolic, 1,2-diarylpropane-3- ol (3), and seven known compounds, fraxidin, eucryphin, boehmenan, threo-carolignan E, (-)-(3R, 4S)-4-hydroxymellein, methyl protocatechuate, and (+)-(R)-de-O-methyllasiodiplodin (4). In addition, chemical analysis of a wood bark extract from Durio kutejensis yielded the new triterpenes 3 beta-O-trans-caffeoyl-2R-hydroxyolean-12-en-28-oic acid (5) and 3 beta-trans-caffeoyl-2R-hydroxytaraxest-12-en-28-oic acid (6) together with four known compounds, maslinic acid, arjunolic acid, 2,6-dimethoxy-p-benzoquinone, and fraxidin. The structures of all compounds were determined on the basis of spectroscopic data.

Neural control of a batch distillation

The purpose of the work reported here was to investigate the application of neural control to a common industrial process. The chosen problem was the control of a batch distillation. In the first phase towards deployment, a complex software simulation of the process was controlled. Initially, the plant was modelled with a neural emulator. The neural emulator was used to train a neural controller using the backpropagation through time algorithm. A high accuracy was achieved with the emulator after a large number of training epochs. The controller converged more rapidly, but its performance varied more widely over its operating range. However, the controlled system was relatively robust to changes in ambient conditions.