A second look at Faster,Better, Cheaper

“Faster, Better, Cheaper” (FBC) was a systems development methodology used by NASA in the 1990s. While usually a deprecated practice, we find that, with certain caveats, it is a viable approach. To determine this we utilized a stochastic AI tool to determine the behavior of FBC for several case studies. In these case studies we compare results of using FBC with that of other optimization policies. In our tests, FBC is as advantageous a policy to use with projects as other policies, while avoiding their apparent downfalls.     

Efficient Data Processing in Software-Defined UAV-Assisted Vehicular Networks: A Sequential Game Approach

Wireless Personal Communications - In large scale networks like Vehicular Ad-hoc Networks (VANETs), the full coverage of fixed infrastructure is hard to ensure, making network management difficult....     

Efficient data dissemination in cooperative vehicular networks

Vehicular networks are drawing the attention of both research community and automotive industry because they provide intelligent transportation systems as well as drivers and passengers’ assistant services. However, the industrialization of such networks faces a number of challenges, in particular, the high cost of the infrastructure to deploy. To overcome this problem, an effective solution is to rely on cooperative vehicle‐to‐vehicle communication to minimize the deployed infrastructure. Because a large number of cooperative vehicle‐to‐vehicle applications are broadcasting by nature, we proposed an efficient dissemination protocol: Road‐Oriented Dissemination (ROD). ROD consists in two modules: (i) Optimized Distance Defer Transfer Module and (ii) Store‐and‐Forward Module. We compare our protocol with other dissemination protocols and analyze its performance by simulations, on‐road tests and analytically. Performance study shows interesting results of ROD compared with the other existing solutions. ROD is able to provide a low end‐to‐end delay, high delivery ratios, and a minimum bandwidth usage because only a limited number of vehicles are involved in the broadcast scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

Use of neural networks for condition rating of jointed concrete pavements

This paper discusses the use of backpropagation neural networks as a management tool for the maintenance of jointed concrete pavement. The backpropagation algorithm is applied to model the condition rating scheme adopted by Oregon State Department of Transportation. The backpropagation technique was successful in accurately capturing the nonlinear characteristics of the condition rating model. A large training set of actual pavement condition cases was used to train the network. The training was terminated when the average training error reached 0.022. A set of 6802 cases was used to test the generalization ability of the system. The trained network was able to accurately determine the correct condition ratings with an average testing error of 0.024. Finally, a statistical hypothesis test was conducted to demonstrate the system's fault-tolerance and generalization properties.     

TILLING-by-Sequencing+ to Decipher Oil Biosynthesis Pathway in Soybeans: A New and Effective Platform for High-Throughput Gene Functional Analysis

Reverse genetic approaches have been widely applied to study gene function in crop species; however, these techniques, including gel-based TILLING, present low efficiency to characterize genes in soybeans due to genome complexity, gene duplication, and the presence of multiple gene family members that share high homology in their DNA sequence. Chemical mutagenesis emerges as a genetically modified-free strategy to produce large-scale soybean mutants for economically important traits improvement. The current study uses an optimized high-throughput TILLING by target capture sequencing technology, or TILLING-by-Sequencing⁺ (TbyS⁺), coupled with universal bioinformatic tools to identify population-wide mutations in soybeans. Four ethyl methanesulfonate mutagenized populations (4032 mutant families) have been screened for the presence of induced mutations in targeted genes. The mutation types and effects have been characterized for a total of 138 soybean genes involved in soybean seed composition, disease resistance, and many other quality traits. To test the efficiency of TbyS⁺ in complex genomes, we used soybeans as a model with a focus on three desaturase gene families, GmSACPD, GmFAD2, and GmFAD3, that are involved in the soybean fatty acid biosynthesis pathway. We successfully isolated mutants from all the six gene family members. Unsurprisingly, most of the characterized mutants showed significant changes either in their stearic, oleic, or linolenic acids. By using TbyS⁺, we discovered novel sources of soybean oil traits, including high saturated and monosaturated fatty acids in addition to low polyunsaturated fatty acid contents. This technology provides an unprecedented platform for highly effective screening of polyploid mutant populations and functional gene analysis. The obtained soybean mutants from this study can be used in subsequent soybean breeding programs for improved oil composition traits.     

Cost optimization of composite beams using genetic algorithms

This paper presents a genetic algorithm model for the cost optimization of composite beams based on the load and resistance factor design (LRFD) specifications of the AISC. The model formulation includes the cost of concrete, steel beam, and shear studs. Two design examples taken from the literature were analyzed in order to validate the proposed model, to illustrate its use, and to demonstrate its capabilities in optimizing composite beam designs. The results obtained show that the model is capable of achieving substantial cost savings. Hence, it can be of practical value to structural designers. A parametric study was also conducted to investigate the effects of beam spans and loadings on the cost optimization of composite beams.     

Extreme-value-theoretic estimation of local intrinsic dimensionality

This paper is concerned with the estimation of a local measure of intrinsic dimensionality (ID) recently proposed by Houle. The local model can be regarded as an extension of Karger and Ruhl’s expansion dimension to a statistical setting in which the distribution of distances to a query point is modeled in terms of a continuous random variable. This form of intrinsic dimensionality can be particularly useful in search, classification, outlier detection, and other contexts in machine learning, databases, and data mining, as it has been shown to be equivalent to a measure of the discriminative power of similarity functions. Several estimators of local ID are proposed and analyzed based on extreme value theory, using maximum likelihood estimation, the method of moments, probability weighted moments, and regularly varying functions. An experimental evaluation is also provided, using both real and artificial data.     

Static wireless sensor networks deployment using an improved binary PSO

A major issue in designing wireless sensor networks is the deployment problem. Indeed, many performances of the sensor network, such as coverage, are determined by the number and locations of deployed sensors. This paper reviews existing deterministic deployment strategies and devises a modified binary particle swarm optimization, which adopts a new position updating procedure for a faster convergence and exploits the abandonment concept to avoid some drawbacks such as premature convergence. The devised approach combines, in a meaningful way, the characteristics of the binary particle swarm optimization with the wireless sensor networks deployment requirements in order to devise a lightweight and efficient sensor placement algorithm. The effectiveness and efficiency of the proposed approach are evaluated through extensive simulations. The obtained results show that the proposed algorithm outperforms the state‐of‐the‐art approaches, especially in the case of preferential coverage. Copyright © 2015 John Wiley & Sons, Ltd.