EVALUATION OF FOUR COVARIATE TYPES USED FOR ADJUSTMENT OF SPATIAL VARIABILITY

Four types of covariates are used to account for spatial variability in data from a field experiment for evaluating 620 soybean varieties for iron chlorosis. The covariates are calculated as the average of 4 and of 14 neighboring residuals and of 4 and of 14 neighboring observations. The residual mean square from the analysis of covariance was smaller\u27 when residuals were used in calculation of the covariates than when observations were used. Moreover, use of 14 neighbors resulted in smaller residual mean squares than did use of 4 neighbors. Differences among 4 covariate types were small and not practically important. Expected values for the covariate regression coefficients were derived based on an errors in variables model. The expected values depend only on the measurement error of the covariate and are unrelated to the strength of the spatial variability. The coefficients estimated from the analysis of covariance are generally greater than the expected values

Equation of state of forsterite

Shock wave data for pure forsterite with initial bulk densities of 2.6 and 3.1 g/cm^3 are obtained to 0.370 Mb by impacting series of specimens with tungsten alloy plates that are launched at speeds of up to 2.3 km/sec with a high-performance propellant gun. The onset of a shock-induced phase change, probably corresponding to the forsterite-‘post spinel’ phase change is observed at 0.280±0.025 Mb. Because of the low shock temperatures, the transition is believed to be limited by the reaction rate and this pressure value should be taken only as an upper limit. Adiabats derived from the Hugoniot data for the forsterite phase are fit to the two-parameter finite strain Birch-Murnaghan equation and to two simple ionic equations of state. The Birch-Murnaghan form of the equation of state gives a zero-pressure bulk modulus (1.29 Mb) that agrees more closely with the ultrasonic data than the modulus obtained from the ionic equations of state. An unusual relaxation effect, in which the elastic shock precursor velocity varies from 5.8 to 9.5 km/sec, is also observed. The characteristic time of the relaxation process appears to be less than 1 μsec

Automating the packing heuristic design process with genetic programming

The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains

Comparison of smoking behavior change for SI and UC study groups. MRFIT Research Group

BACKGROUND. The results of MRFIT smoking intervention program are presented for the 4,103 special intervention and 4,091 usual care men who reported smoking cigarettes at the first screening visit. RESULTS. Among the special intervention men, the reported cessation rate increased from 43.1% at 12 months to 48.9% at 72 months. The reported cessation rate among the usual care men increased from 13.5% at 12 months to 28.8% at 72 months. Among smokers who reported cessation at 72 months, 51.3% of special intervention men and 22.7% of usual care men had quit smoking within the first year and remained abstinent thereafter. Average thiocyanate and expired-air carbon monoxide served as objective measures of smoking and were significantly lower among the special intervention men than among the usual care men over the entire follow-up period. The reported cessation rates at 72 months varied according to initial levels of smoking. Smokers reporting 1-19 cigarettes per day at entry were more likely to quit than heavier smokers. For each category of smoking at entry (1-19, 20-39, and 40 or more cigarettes per day) significantly more special intervention than usual care smokers reported cessation. CONCLUSION. These results indicate that the MRFIT smoking intervention program was successful in promoting early cigarette smoking cessation and maintaining cessation over the entire trial for a large percentage of cigarette smokers