Routing and Wavelength Assignment (RWA) with Power Considerations In All- Optical Wavelength-Routed Networks

Routing and wavelength assignment (RWA) is an important problem that arises in wavelength division multiplexed (WDM) optical networks. Previous studies have solved many variations of this problem under the assumption of perfect conditions regarding the power of a signal. In this paper, we investigate this problem while allowing for degradation of routed signals by components such as taps, multiplexers, and fiber links. We assume that optical amplifiers are preplaced. We investigate the problem of routing the maximum number of connections while maintaining proper power levels. The problem is formulated as a mixed-integer nonlinear program and two-phase hybrid solution approaches employing two different heuristics are developed

Routing Algorithms for All-Optical Networks with Power Considerations: The Unicast Case

In this paper, we investigate the problem of routing connections in all-optical networks while allowing for degradation of routed signals by different optical components. To overcome the complexity of the problem, we divide it into two parts. First, we solve the pure RWA problem using fixed routes for every connection. Second, power assignment is accomplished by either using the smallest-gain first (SGF) heuristic or using a genetic algorithm. Numerical examples on a wide variety of networks show that (a) the number of connections established without considering the signal attenuation was most of the time greater than that achievable considering attenuation and (b) the genetic solution quality was much better than that of SGF, especially when the conflict graph of the connections generated by the linear solver is denser.

Availability and Use of Audiovisual and Electronic Resources by Distance Learning Students in Nigerian Universities: a Case Study of National Open University of Nigeria (NOUN), Ibadan Study Centre

This study investigated the availability and use of audiovisual and electronic resources by distance learning students at the National Open University of Nigeria (NOUN). A questionnaire was administered tothe distance learning students selected across the various departments of the NOUN. The findings revealed that even though NOUN made provision for audiovisual and electronic resources for students' use, a majority of the audiovisual and electronic resources are available through personal provision by the students.The study also revealed regular use of audiovisual and electronic resources by the distance learning students. Constraints on use include poor power supply, poor infrastructure, lack of adequate skill, and high cost of access.

Predicting Accurate and Actionable Static Analysis Warnings: An Experimental Approach

Static analysis tools report software defects that may or may not be detected by other verification methods. Two challenges complicating the adoption of these tools are spurious false positive warnings and legitimate warnings that are not acted on. This paper reports automated support to help address these challenges using logistic regression models that predict the foregoing types of warnings from signals in the warnings and implicated code. Because examining many potential signaling factors in large software development settings can be expensive, we use a screening methodology to quickly discard factors with low predictive power and cost-effectively build predictive models. Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative models built without screening.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.