Abstract-The traditional academic advising process in many tertiary-level institutions today possess significant inefficiencies, which often account for high levels of student dissatisfaction. Common issues include high student-advisor loads, long waiting periods at advisory offices and the need for advisors to handle a significant number of redundant cases, among others.Utilizing semantic web expert system technologies, a solution was proposed that would complement the traditional advising process, alleviating its issues and inefficiencies where possible. The solution coined 'AdviseMe', an intelligent web-based application, provides a reliable, user-friendly interface for the handling of general advisory cases in special degree programmes offered by the Faculty of Science and Technology (FST) at the University of the West Indies (UWI), St. Augustine campus. In addition to providing information on handling basic student issues, the system's core features include course advising, as well as information of graduation status and oral exam qualifications. This paper produces an overview of the solution, with special attention being paid to the its inference system exposed via its RESTful Java Web Server (JWS).The system was able to provide sufficient accurate advice for the sample set presented and showed high levels of acceptability by both students and advisors. Furthermore, its successful implementation demonstrated its ability to enhance the advisory process of any tertiary-level institution with programmes similar to that of FST.

The output of a mu ltip le criteria decision method often has to be analyzed using some sensitivity analysis technique. The SAW MCDM method is commonly used in management sciences and there is a critical need for a robust approach to sensitivity analysis in the context that uncertain data is often present in decision models. Most of the sensitivity analysis techniques for the SAW method involve Monte Carlo simu lation methods on the initial data. These methods are computationally intensive and often require comp lex software. In this paper, the SAW method is extended to include an objective function which makes it easy to analyze the influence of specific changes in certain criteria values thus making easy to perform sensitivity analysis.

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Abstract-The current disjoint path Ad hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol does not have any energy-awareness guarantees. When AOMDV is used in wireless sensor networks (WSNs) energy is an important consideration. To enhance the AOMDV protocol an extra energy metric is added along with the hop count metric. This Energy aware or EA-AOMDV improves path selection using a trade-off between energy and hop count, thus giving more longevity to WSNs. EA-AOMDV is compared to the current AOMDV routing protocol to prove its worth in the context of WSNs. It is found that EA-AOMDV leads to better WSN energy-awareness in resource constrained WSNs.

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