Expert Systems for Construction Project Monitoring

Potential applications of knowledge based expert systems in the area of construction project monitoring and control are described. Originally developed from research in artificial intelligence, these systems are computer programs that can undertake intelligent tasks currently performed by highly skilled people. While some project monitoring can be accomplished by algorithmic procedures, the capability of knowledge based expert systems to deal with illstructured problems and to be extensively modified over time make them desirable for application in this area. Sample applications and heuristic rules in scheduling and inventory control are provided.     

是天使？是魔鬼？——IE7正式版体验

10月19日,历经5年的等待,姗姗来迟的InternetExplorer7正式版终于出现在世人面前。有别于以往渐近式的改进,本次的IE7不管是外观,还是内涵,都发生了翻天覆地的变化。不过,资源占用情况依然不容乐观,本文将带你了解这一切。     

The infrared infiltration and joining of advanced material

JOM - Infrared processing is a newly developed technique for materials processing. Key features include rapid processing, simplicity, and low cost. Because of the rapid processing, very little...     

Estimation of failure intensity for the Weibull process

A Weibull process/non-homogeneous Poisson process is commonly used to analyze the failure behavior of repairable systems. The object of the present study is to obtain exact estimates of the failure intensity of this model at the time of n failures. The resulting MLE estimate is biased and the corrected version for biasedness along with some approximate estimates is given. An analytical and numerical comparison of the relative efficiencies of the MLE of the exact biased, approximated biased, exact unbiased and approximated unbiased of the intensity function is presented. It will be shown that for small n (n < 30) there is quite a large relative difference between the mean squared errors of the exact and approximate estimates of the intensity function. Real failure data are used to illustrate the difference between the exact and approximate estimates of the intensity function.     

Attributions and relapse in opiate addicts.

Investigated whether attributions of opiate addicts would predict both their ability to abstain from future use and their reactions to abstinence violations. Measures of generalized beliefs about responsibility for positive and negative outcomes and specific attributions about relapse episodes were elicited from 80 addicts at the time of admission for inpatient detoxification and treatment. Addicts who at admission attributed to themselves greater responsibility for negative outcomes and who attributed relapse episodes to more personally controllable factors were subsequently (at 6-mo follow-up) more likely either to be completely abstinent or to contain the effects of temporary lapses into opiate use. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region

The objective of this research is to develop a non-destructive method for predicting cooked beef tenderness using optical scattering of light on fresh beef muscle tissue. A hyperspectral imaging system (λ = 496–1,036 nm) that consists of a CCD camera and an imaging spectrograph, was used to acquire beef steak images. The hyperspectral image consisted of 120 bands with spectral intervals of 4.54 nm. Sixty-one fresh beef steaks, including 44 strip loin and 17 tenderloin cuts, were collected. After imaging, the steaks were cooked and Warner-Bratzler shear (WBS) force values were collected as tenderness references. The optical scattering profiles were derived from the hyperspectral images and fitted to the modified Lorentzian function. Parameters, such as the peak height, full scattering width at half maximum (FWHM), and the slope around the FWHM were determined at each wavelength. Stepwise regression was used to identify 7 key wavelengths and parameters. The parameters were then used to predict the WBS scores. The model was able to predict WBS scores with an R = 0.67. Optical scattering implemented with hyperspectral imaging shows limited success for predicting current status of tenderness in beef steak.