Predicting wood value in Queensland Caribbean pine plantations using a decision support system

A decision support system has been developed in Queensland to evaluate how changes in silvicultural regimes affect wood quality, and specifically the graded recovery of structural timber. Models of tree growth, branch architecture and wood properties were developed from data collected in routine Caribbean pine plantations and specific silvicultural experiments. These models were incorporated in software that simulates the conversion of standing trees into logs, and the logs into boards, and generates detailed data on knot location and basic density distribution. The structural grade of each board was determined by simulating the machine stress-grading process, and the predicted graded recovery provided an indicator of wood value. The decision support system improves the basis of decision-making by simulating the performance of elite genetic material under specified silvicultural regimes and by predicting links between wood quality and general stand attributes such as stocking and length of rotation.

A Nonlinear System Under Combined Periodic and Random Excitation

The anharmonic oscillator under combined sinusoidal and white noise excitation is studied using the Gaussian closure approximation. The mean response and the steady-state variance of the system is obtained by the WKBJ approximation and also by the Fokker Planck equation. The multiple steadystate solutions are obtained and their stability analysis is presented. Numerical results are obtained for a particular set of system parameters. The theoretical results are compared with a digital simulation study to bring out the usefulness of the present approximate theory.

HIGH-Tc superconductivity in perovskite oxides of the Y-Ba-Cu-O system

Several biphasic compositions of the type Y3-xBa3+xCu6O14 show an onset of superconductivity in the 90-115K range, attaining zero resistance in the 70-85K range. The phase responsible for superconductivity in these compositions is a perovskite oxide of composition YBa2Cu3 O7. This oxide annealed in oxygen shows the onset of superconductivity at 120K and zero resistance at 87K. YBa2,Cu3O7 shows the highest Meissner effect of all oxide superconductors. The superconducting behaviour of the two perovskite oxides, Y0.95Ba1.95,Cu3O7 and Y1.05Ba1.95Cu3O7 show interesting features; a marked decrease in resistivity is observed from room temperature itself in the former oxide with zero resistance at 89K. Electron microscopy and infrared spectra of these oxides are briefly discussed.

A hierarchical system of learning automata that can learn die globally optimal path

Systems of learning automata have been studied by various researchers to evolve useful strategies for decision making under uncertainity. Considered in this paper are a class of hierarchical systems of learning automata where the system gets responses from its environment at each level of the hierarchy. A classification of such sequential learning tasks based on the complexity of the learning problem is presented. It is shown that none of the existing algorithms can perform in the most general type of hierarchical problem. An algorithm for learning the globally optimal path in this general setting is presented, and its convergence is established. This algorithm needs information transfer from the lower levels to the higher levels. Using the methodology of estimator algorithms, this model can be generalized to accommodate other kinds of hierarchical learning tasks.