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Recently, we have developed optimal harvesting policies based on profit optimization in random varying environments. Namely, we have considered a logistic stochastic differential equation growth model, with the purpose of discussing the use of variable versus constant effort harvesting policies in terms of the expected accumulated discounted profit during a finite time interval. Using realistic parameters, we have concluded that there is only a slight reduction in profit when choosing the applicable constant effort policy instead of the variable effort policy, which presents strong disadvantages. Here, we apply a logistic growth model and a more general profit structure to present alternative policies based on variable effort, named stepwise policies, where the harvesting effort is determined, under the optimal variable effort policy, at the beginning of each year (or of each biennium) but is kept constant during that year (biennium). Replacing the optimal variable effort policy by these stepwise non-optimal policies has the advantage of applicability but, at best, considerably reduces the already small profit advantage the optimal variable effort policy has over the optimal constant effort sustainable policy.

Stem-and-leaf displays have been widely taught since John W. Tukey publicized them energetically in the 1970s. They remain useful for many distributions of small or modest size, especially for showing fine structure such as digit preference. Stata's implementation stem produces typed text displays and has some inevitable limitations, especially for comparison of two or more displays. One can re-create stem-and-leaf displays with a few basic Stata commands as scatterplots of stem variable versus position on line with leaves shown as marker labels. Comparison of displays then becomes easy and natural using scatter, by(). Back-to-back presentation of paired displays is also possible. I discuss variants on standard stem-and-leaf displays in which each distinct value is a stem, each distinct value is its own leaf, or axes are swapped. The problem shows how one can, with a few lines of Stata, often produce standard graph forms from first principles, allowing in turn new variants. I also present a new program, stemplot, as a convenience tool.

Aneurysms are an enlargement of a blood vessel due to a weakened wall and can pose significant health risks. Abdominal aortic aneurysms alone are the 13th leading cause of death in the United States, with 15,000 deaths annually. While there are recommended guidelines for doctors to follow in the treatment of specific aneurysms, they cannot guarantee a satisfactory outcome. Computer simulations of an aneurysm may be able to help doctors in their treatment; however, the results are inaccurate if the vessel wall thickness is poorly measured. In order to provide more accurate, patient-specific simulations, not only does geometry for the fluid domain need to be created from medical images for analysis, but the creation of more accurate models for the wall needs to be accomplished as well. This paper proposes a solution to the latter by deforming the mesh from a healthy vessel into one with an aneurysm through parameterization and the use of a spring model. The thickness of the resulting wall model is empirically valid and fluid-structure interaction simulations show significant improvements when using a variable versus a uniform wall thickness.

Constraint problems are usually solved by specific satisfaction methods. Another way for solving constraints, called solution maintenance, is to try to recover the consistency from an existing solution, disturbed by the addition of new constraints and/or the update of some variables. We present in this paper a solution maintenance algorithm based on value propagation in the constraint network. This algorithm is specially appropriate to interactive applications like spreadsheets, several different kinds of programs for incremental manipulation of geometrical figures, and also in case-based reasoning. Although the method is efficient and incremental, it may not be able to find a solution even when the constraint problem has one.

A space-time mapping that describes a systolic array consists of a scheduling vector that specifies the temporal distribution and an allocation matrix that specifies the spatial distribution. The allocation matrix determines whether a variable is moving or stationary. The space-time mapping provides a complete description of the data flow for moving variables. But it fails to provide any help in handling stationary variables. This paper presents a method for the loading, recovery and access of stationary data in a systolic array. The basic idea is to add new variables, which constitute a specification for the loading, recovery and access of stationary data, to the initial program. This specification leaves open the choice of dependence vectors of the new variables and the scheduling vector. Thus, the search of schemes for handling stationary data reduces to a search of dependence vectors for the new variables and of scheduling vectors.