Garlic peroxidase immobilized into chitosan matrix suitable for biosensors applications

Peroxidase X₁ (POX₁) isoenzyme was purified from garlic bulb (Allium sativum L.). Native-PAGE profile showed two isoforms partially purified (designated POX_1A and POX_1B). A POX_1B-based electrode showed great potential for monitoring hydrogen peroxide in biological samples. Chitosan was used as a matrix for enzyme immobilization. The enzyme activities were studied by photometry. Immobilization was accomplished by either inclusion in a thin film or adsorption to cross-linked microspheres. Two linkage agents were used: glutaraldehyde and glyoxal. The best immobilization and activity yields (82%, 95%) were obtained when POX_1B was incorporated within a chitosan/glyoxal film. The effect of temperature on the immobilized enzyme was tested. Results showed that full activity was retained after 40 min incubation at 40 °C. Anchored POX_1B inside chitosan was used for biosensor design. Cyclic voltammetry and impedance spectroscopy were employed to analyze electrochemical properties of the modified gold electrode and to monitor hydrogen peroxide. The biosensor was very sensitive and attained a detection limit of 100 nM.     

CONSENSUS FUNCTIONS FOR CLUSTER ENSEMBLES

The major task of clustering is to group an heterogeneous population into unknown groups based on a similarity measure. In order to enhance the robustness and the stability of unsupervised classification solutions, clustering ensembles are used; they are considered to be a powerful tool to deal with this issue. Individual clusterers consolidate the process of decision making through the concept of weighting. The aim is to determine an effective combination method that makes use of the benefits of each clusterer while avoiding its weaknesses. In this paper, we study the problem of combining multiple partitioning without accessing the original features. A genetic algorithm is proposed using three different fitness scores. Following three scenarios: Object Distributed Clustering, Feature Distributed Clustering, and Robust Centralized Clustering, the proposed consensus functions algorithm outperforms three existing ones: Cluster-based Similarity Partitioning Algorithm, HyperGraph Partitioning Algorithm and Meta-Clustering Algorithm.     

Postbuckling of Elastic Beams Considering Higher Order Strain Terms

Nonlinear relations between the beam displacement and generalized strain measures, which have basic effects on postbuckling behavior of elastic beams, are presented. The complex coupling phenomena associated with the higher order strain terms is reviewed for the special case of planar and rectilinear pinned-pinned beams. Special consideration was made for the physical assumptions used in the various column-beam models. A natural hierarchy results yielding that all the higher order terms can, for a specific beam formulation, be steadily obtained by dissimilar polynomial approximations of the generalized strains. The asymptotic expansions method and the minimum energy criterion are used to perform analytical calculation of the postbifurcation equilibrium path at the neighborhood of a bifurcation point when only a unique buckling mode is assumed to occur. As a result, postbuckling branches are easily obtained even when accounting for both beam centerline extensional deformation and shear strain. They show that the critical load is scarcely affected by the higher order strain terms unlike the postbuckling paths which are found to be very sensitive to them.     

Inelastic wrinkling and collapse of tubes under combined bending and internal pressure

The problem of inelastic bending and collapse of tubes in the presence of internal pressure is investigated using experiments and analyses. The experiments involve 1.5-inch diameter, D/t=52 stainless steel tubes bent to failure at fixed values of pressure. The moment-curvature response is governed by the inelastic characteristics of the material. Bending induces some ovalization to the tube cross section while, simultaneously, the internal pressure causes the circumference to grow. Following some inelastic deformation, small amplitude axial wrinkles appear on the compressed side of the tube, and their amplitude grows stably as bending progresses. Eventually, wrinkling localizes, causing catastrophic failure usually in the form of an outward bulge. Internal pressure stabilizes the structure, it increases the wavelength of the wrinkles and can increase significantly the curvature at collapse. The onset of wrinkling is established by a custom bifurcation buckling formulation. The evolution of wrinkling and its eventual localization are simulated successfully using a FE shell model. The material is represented as an anisotropic elastic-plastic solid using the flow theory, while the models are assigned initial geometric imperfections with the wavelength of the wrinkling bifurcation mode. It is demonstrated that successful prediction of collapse requires very accurate representation of the material inelastic properties including yield anisotropies, and that as expected, the collapse curvature is sensitive to the imperfection amplitude and wavelength imposed.     

Third-generation virtualized architecture for the MVNO context

The third-generation architectures have to support multiple mobile virtual network operators (MVNOs). They have also to host different types of these virtual operators. Virtualizing these architectures will allow the MVNOs to rapidly deploy their equipment. It will separate the management domain between them and the mobile host operator. It will also allow sharing resources and reducing the deployment cost. Motivated by these requirements, we propose some MVNO distributed architectures. First of all, we evaluate the physical and virtual deployment time, then we define the utility function of the equipment for the different types of MVNOs. The utility function evaluates the gain in deployment time for each type of MVNO. This function has to be maximized. Our study demonstrates that the data calls type is the best MVNO candidate for virtualization. This latter consistently yields the best overall utility across an important number of network equipment to be virtualized by varying the time required for software installation and the time spent to determine the physical position of the equipment.     

Multi-criteria decision making in workforce choice using AHP,WSM and WPM

This paper focuses on the implementation of Multi-Criteria Decision Making (MCDM) methods to evaluate the workforce competence in apparel industry. In this study, the Analytic Hierarchy Process (AHP), the Weighted Sum Model (WSM) and the Weighted Product Model (WPM) are suggested to solve workforce selection problem. The use of these methods is expected to help choose the best operator among several alternatives. Based on three criteria, each worker is evaluated in each executed operation. The criteria are a Quality Index ‘QI’, an Activity ‘A’ and an Attendance Rate ‘AR’. Thanks to the MCDM methods, scores allowing an objective classification of operators are obtained. Experimental results on AHP, WSM and WPM methods show the same decisions with the same workers’ classification from the best to the worst alternative. The resulting database contributes to the line balancing optimization by choosing for each operation, and as far as possible, the most competent operator.     

Optimal maintenance plan for two-level assembly system and risk study of machine failure

This paper deals with the optimisation of two-levels assembly system planning. This system is composed of a single machine, inventories at levels 1 and 2 for stock keeping components to assembly and final assembled product. It assumed that the machine processes all assembly operations and is subject to random failure. A mathematical model is developed to incorporate a supply planning for two-level assembly systems under stochastic lead times and breakdowns machine. A preventive maintenance plan is carried out to reduce the frequency of the corrective maintenance actions. This work has double goals. The first one is to find the optimal order release dates for the different components at level 2 and the optimal preventive maintenance plan. The second one is to quantify the risk due to machine failures which have an impact on the lead-time of the finished product. To consider the maintenance actions, preventive maintenance actions are perfectly performed to restore the machine to state “as good as new”, minimal repair is considered at failure. The model minimises the total cost, which is the sum of inventory holding cost for components at levels 1 and 2, backlogging and inventory holding cost for the finished products and maintenance costs. To illustrate the effectiveness of the proposed model, different meta-heuristics are applied; the genetic algorithm shows the most suited to our analytical model, the optimal release date founded by this algorithm allows finding the optimal preventive maintenance plan. The obtained optimal maintenance planning is used in the risk assessment in order to find the threshold repair period that avoids lost profit.     

Tensor-based methods for high-power amplifier identification and predistortion linearization

Aligned with the recent revolution of smart cities concept, a lot of work has been done to support the education process in direct or indirect way. In countries like Lebanon, in which the choice of school does not depend on the residence location, parents are becoming more and more selective in the quality of schools they are choosing for their children. This fact may lead parents to choose a modern and good school even if it is far from their residence. On the other hand, and even if the school was close enough, the traffic congestion would make the on-feet journey of the kid to the school very risky. Driving children to school is a solution. However, it consumes a lot of time and effort each day in the morning and the afternoon. Hence, school buses are commonly used by all schools to take the student from and to his home. Nevertheless, waiting the bus each day to arrive is an exhausting and a time-wasting mission. In this paper we present a smart transportation system for school buses that helps in saving parents’ time, by avoiding waiting school buses in the morning and then in the afternoon to return kids back, especially with the increasing traffic jams at these hours. The proposed mobile and web application is designed to help parents, school and the bus to communicate automatically and easily via the application in order to detect kids’ arrival time. The bus application side will notify parents few minutes before its approaching to their homes. Furthermore, the system will allow parents to inform the school and hence the bus application side about the absence of their kid. The system has been efficiently and dynamically designed and implemented so it can be hosted and used by any school administration without the need to any major modifications. It has been tested on a summer school to prove its efficiency and marked an important positive feedback from the school and the parents’ sides.