TEACHER RECOGNITION PROGRAM: Sample Essays Questions

1963 ◽  
Vol 1 (3) ◽  
pp. 143-147
Keyword(s):  
Program Sample ◽  
Teacher Recognition

scholarly journals AN EXACT SOLUTION FOR THE RESOURCE-CONSTRAINED CONSTRUCTION SCHEDULING PROBLEM

2016 ◽  
Vol 8 (2) ◽  
pp. 71-78
Author(s):  
Bartłomiej Sroka ◽  
Elżbieta Radziszewska-Zielina
Keyword(s):  
Execution Time ◽  
Critical Path ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Building Projects ◽  
Construction Scheduling ◽  
Program Sample ◽  
Resource Constrained Scheduling Problem ◽  
The Cost ◽  
Exponential Complexity

Reduced time and, by the same token, the cost of the project is a crucial factor in contemporary construction. This article presents a method for the exact optimisation of a resource-constrained scheduling problem. Based on the Critical Path Method, graph theory and linear programming, an algorithm was developed and the FROPT program was written in Matlab to minimise the execution time of the task. By using the newly-created program, sample networks were calculated and the results were compared with results obtained by using the MS Project scheduling program (using approximation algorithm). The execution time obtained by using FROPT were on average 10% shorter than those obtained using MS Project. In selected cases the improvement in execution time reached 25%. A deterministic approach to the problem may shorten planned project times and bring financial benefits. Due to the exponential complexity of the algorithm, it is most useful in solving small or highly coherent networks. The algorithm and program may result in benefits not offered by commercial software for planners of building projects.

The Teacher Recognition Program

1967 ◽  
Vol 5 (5) ◽  
pp. 240-241
Author(s):  
Melba Phillips
Keyword(s):  
Teacher Recognition

An Artificial Neural Networks Model for Compressive Strength of Self-Compacting Concrete

2016 ◽  
Vol 845 ◽  
pp. 226-230
Author(s):  
Akhmad Suryadi ◽  
Qomariah ◽  
M. Sarosa
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Experimental Tests ◽  
Experimental Program ◽  
Self Compacting Concrete ◽  
Design Program ◽  
Artificial Neural ◽  
Program Sample ◽  
Mathematic Program

An experimental program was undertaken to evaluate the compressive strength of self-compacting concrete using commercial mathematic program. Sample variation was monitored using an experimental cylinder of concrete measuring 150 mm in diameter and 300 mm in height. This research examined various mixture designs in the laboratory tests with the goal of creating mixtures with desirable flow specification that did not require additional vibration yet provided adequate compressive strength. After 28 days, compressive strength of cylinder concrete determination, a model of Artificial Neural Networks (ANNs) was designed for this research and the results were obtained in this model of ANN. Both experimental tests and mix design program data was analyzed with statistical packet software. The result of statistical analysis has been done in 98.54 percent of confidence interval. It has been seen that the ANN can be used as reliable modelling method for similar experiment.

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