Tunis University

About: Tunis University is a education organization based out in Tunis, Tunisia. It is known for research contribution in the topics: Population & Thin film. The organization has 11745 authors who have published 15400 publications receiving 154900 citations. The organization is also known as: University of Tunis & UT.

Reduced graphene oxide/TiO2 nanotube composites for formic acid photodegradation

Abstract: The influence on the photocatalytic performance of TiO2 nanotubes (NT) resulting from the addition of graphene oxide (GO) was studied. TiO2 nanotubes (NT) were prepared using alkaline hydrothermal treatment of TiO2 P25 followed by calcination at 400 °C under air. GO-NT composites were then obtained by wet impregnation of the as-prepared TiO2 nanotubes onto graphene oxide before reduction under H2 at 200 °C. In a first step, the influence of the reduction treatment was evaluated on GO alone to determine its role towards the nature of the oxygen-containing functional groups present. GO-NT composites were also characterized considering both the effect of the reduction treatment and of the GO weight loading on textural, structural, electronic, and optical properties of TiO2 nanotubes. The resulting GO-NT composites were finally evaluated for the photocatalytic degradation of formic acid and compared to TiO2 nanotubes alone and to P25. Results emphasize a strong increase of the electron affinity and conductivity of the GO-NT composites if graphene oxide is reduced at 200 °C. These enhanced properties lead to an easier separation of photogenerated charges and to a limitation of the recombination of electron-hole pairs. A dramatic gain in photocatalytic response is observed. Maximum in photocatalytic efficiency is reached at a GO loading of 1.0 wt% while further increase of GO weight loading blocks light penetration and depletes the photocatalytic response.

Searching for knee regions of the Pareto front using mobile reference points

Abstract: Evolutionary Algorithms (EAs) have been recognized to be well suited to approximate the Pareto front of Multi-objective Optimization Problems (MOPs). In reality, the Decision Maker (DM) is not interested in discovering the whole Pareto front rather than finding only the portion(s) of the front that matches at most his/her preferences. Recently, several studies have addressed the decision-making task to assist the DM in choosing the final alternative. Knee regions are potential parts of the Pareto front presenting the maximal trade-offs between objectives. Solutions residing in knee regions are characterized by the fact that a small improvement in either objective will cause a large deterioration in at least another one which makes moving in either direction not attractive. Thus, in the absence of explicit DM’s preferences, we suppose that knee regions represent the DM’s preferences themselves. Recently, few works were proposed to find knee regions. This paper represents a further study in this direction. Hence, we propose a new evolutionary method, denoted TKR-NSGA-II, to discover knee regions of the Pareto front. In this method, the population is guided gradually by means of a set of mobile reference points. Since the reference points are updated based on trade-off information, the population converges towards knee region centers which allows the construction of a neighborhood of solutions in each knee. The performance assessment of the proposed algorithm is done on two- and three-objective knee-based test problems. The obtained results show the ability of the algorithm to: (1) find the Pareto optimal knee regions, (2) control the extent (We mean by extent the breadth/spread of the obtained knee region.) of the obtained regions independently of the geometry of the front and (3) provide competitive and better results when compared to other recently proposed methods. Moreover, we propose an interactive version of TKR-NSGA-II which is useful when the DM has no a priori information about the number of existing knees in the Pareto optimal front.

Monitoring the coefficient of variation using a variable sample size control chart in short production runs

Abstract: Monitoring the coefficient of variation (CV) is an effective approach to monitor a process when both the process mean and the standard deviation are not constant but, nevertheless, proportional. Until now, few contributions have investigated the monitoring of the CV for short production runs. This paper proposes an adaptive Shewhart control chart implementing a variable sample size (VSS) strategy in order to monitor the coefficient of variation in a short production run context. Formulas for the truncated average run length are derived. Moreover, a comparison is performed with a Fixed Sampling Rate Shewhart chart for the CV in order to evaluate the performance of each chart in a short run context. An example illustrates the use of this chart on real data.