Hany El Kadi

Faculty response to ethical issues at an American university in the Middle‐East

Purpose – The objective of this study is to get feedback on faculty perception of ethical issues related to teaching, scholarship and service at a relatively new American‐style university in the Middle‐East.Design/methodology/approach – A questionnaire involving 21 scenarios with multiple choice answers was developed and distributed to all faculty at the institution to get their opinion on the issues. The effects of faculty background, gender, rank, and administrative responsibilities on the obtained responses at the institution were considered.Findings – The findings include: about one‐third of the faculty participants were unaware of the universitys code of ethics; several of the faculty surveyed stated that they would ignore violations of an ethical code of conduct committed by colleagues; and there was no definite trend observed between the responses of faculty based on their discipline.Research limitations/implications – The study is based on a questionnaire; this implies that the faculty responses ...

Predicting the Crushing Behavior of Axially Loaded Elliptical Composite Tubes Using Artificial Neural Networks

In this research work, the artificial neural networks (ANN) technique is used in predicting the crushing behavior and energy absorption characteristics of axially-loaded glass fiber/epoxy composite elliptical tubes. Predictions are compared to actual experimental results obtained from the literature and are shown to be in good agreement. Effects of parameters such as network architecture, number of hidden layers and number of neurons per hidden layer are also considered. The study shows that ANN techniques can effectively be used to predict the crushing response and the energy absorption characteristics of elliptical composite tubes with various ellipticity ratios subjected to axial loading.

Predicting the effect of cooling rate on the mechanical properties of glass fiber–polypropylene composites using artificial neural networks

Properties of thermoplastic-based composites are affected by their processing conditions, and understanding their behavior under these different conditions is of most importance. The current study aims to predict the static tensile behavior of unidirectional glass fiber–polypropylene composite materials processed under different cooling rates using artificial neural networks (ANNs). Stress–strain relations for the material processed under various cooling rates were predicted using ANN. For all the cases investigated, the modulus of elasticity was predicted with a minimum accuracy of 97%, while the ultimate strain was predicted, in most cases, with a minimum accuracy of 90%. These predictions indicate that ANN can be successfully used to predict the mechanical properties of unidirectional composites manufactured under different cooling rates. This method allows users to predict the behavior of the material under cooling rate conditions for which no experimental data are available.

Engineering students and faculty perceptions of academic dishonesty

Purpose This paper aims to survey students and faculty from the College of Engineering at an American university in the United Arab Emirates about their perception on different issues related to academic dishonesty. Opinions were sought on plagiarism, inappropriate collaboration, cheating on exams, copyright violations and complicity in academic dishonesty. Reasons for students to commit dishonest acts and ways to reduce academic misconduct were also included. Design/methodology/approach A survey involving 11 questions with multiple choice answers was developed and distributed to engineering students and faculty at the institution to get their perception of the considered issues. Findings Results of the study showed that while faculty and students were generally in agreement in their perception of the frequency of academic dishonesty among students, they greatly differed on the courses of action needed to reduce them. Most faculty members favored applying tougher penalties and using more proctors in exams. On the other hand, students preferred softer approaches such as educating them on academic integrity issues, applying lenient deadlines for assignments and reducing the difficulty of exams. Research limitations/implications The conclusions and recommendations of the study are applicable to colleges of higher education having similar characteristics and culture to the surveyed institution. Practical implications The findings can be used to understand students’ behavior and faculty’s attitude toward academic dishonesty, and to assess the effectiveness of current strategies addressing the issue at similar universities in the region. Originality/value The conducted literature review indicated that this work is believed to be a pioneering case study in the Gulf Cooperation Council countries.

Reconstruction of turbulence statistics in a dump combustor using neural networks

Artificial Neural networks are utilized to predict flow properties of a confined, isothermal, and swirling flowfield in an axisymmetric sudden expansion combustor using a two-component laser Doppler velocimetry capable of measuring the mean velocity components and their statistics. Generalized feedforward, radial basis function, and coactive neuro-fuzzy inference system neural networks are tested and the results are compared in the reconstruction of the axial, tangential velocity profiles, and their root mean squares of their fluctuating velocity components. The results showed that generalized feed forward networks give the best prediction with the highest correlation coefficients for most of the flow profiles.

Predictions of Turbulence Intensity in a Combustor Model Using Neural Network Analysis

Predictions of turbulence intensity and continuous evolution of fluid flow characteristics in a combustor model are useful and essential for better and optimum design of gas turbine combustors. Many experimental techniques such as Laser Doppler Velocimetry (LDV) measurements provide only limited discrete information at given points; especially, for the cases of complex flows such as dump combustor swirling flows. For this type of flow, usual numerical interpolating schemes appear to be unsuitable. Neural Network Analysis (ANN) is proposed and the results are presented in this paper and are compared with the experimental data used for training purposes. This pilot study showed that artificial neural network is an appropriate method for predicting swirl flow characteristics in a model of a dump combustor. These techniques are proposed for better designs and optimization of dump combustors.Copyright

Using Future Search Conference for e-Learning Strategy Formulation in Higher Education

Much is understood about success factors, issues, and lessons learned in implementing e-Learning in the context of higher education. A consensus is emerging that suggests that each institution is unique and hence requires a highly customized e-Learning strategy. This paper presents a strategy formulation process that takes a community building view towards strategy formulation. In specific, the process uses the future search conference and the Cademenos model of growth of e-Learning within higher education. An application and evaluation of the strategy formulation process at an American University is presented.