A survey and taxonomy on virtual data center embedding

The Journal of Supercomputing - Data center network virtualization is being considered as a promising technology to provide a performance guarantee for cloud computing applications. One important...     

Extra Virgin Olive Oil Components and Oxidative Stability from Olives Grown in Tunisia

The effects of the contents of lipids, pigments, α-tocopherol and phenols were studied in relation to the antioxidant capacity of five virgin olive oils obtained from five olive cultivars planted in Tunisia (Arbequina, Koroneiki, Leccino, Oueslati and Chemchali). The antioxidant capacities were evaluated by two different radical scavenging activities: radical scavenging activity by the DPPH assay (RSA-DPPH) and total antioxidant status by the ABTS test (TAA-ABTS). The highest contents of antioxidant compounds (75.96, 10.34, 6.32, 15.39 and 241.52 mg kg⁻¹ for oleic acid, O/L ratio, carotenes, chlorophylls and total phenols, respectively) were found for the Koroneiki cultivar except for α-tocopherol and o-diphenols, which had the highest contents (369 and 160.7 mg kg⁻¹, respectively) in the Leccino and Chemchali cultivars (cvs). Furthermore, the highest antioxidant capacity in virgin olive oil was observed in the Koroneiki cultivar (0.24 mmol TE kg⁻¹) followed by the Chemchali and Leccino cvs (0.22 and 0.13 mmol TE kg⁻¹) for the TAA-ABTS test. However, the RSA-DPPH activity was higher for the Chemchali cultivar (19.9%) than for the Koroneiki and Leccino cvs (18.4 and 13.5%, respectively). Correlation between these capacities and the oil composition revealed that they were mainly influenced by the carotene content, followed by chlorophyll and phenolic contents where the ABTS test was more pronounced. Then, the antioxidant capacity of the virgin olive oils was correlated with polar components and the lipid profile which are important for its shelf life.     

Temperature stability of a pure metakaolin based K-geopolymer: Part 2. Variations in the mesoporous network and its rehydration stability

The thermal behavior of a model MK-based K-geopolymer was investigated between room temperature and 1400°C in order to evaluate its potentiality for high-temperature applications. The purpose of our study was to monitor the behavior of a geopolymer during a temperature rise in order to better understand its variations with respect to temperature. The works from the present paper focus only changes in the porous network; it follows a first part devoted to variations in the mineral matrix. The results obtained here show that the geopolymer material preserves its porous integrity up to 800°C, while maintaining the reversibility of water exchanges corresponding to about 25 weight percent. Together with the results of part 1, the findings of this study allow us to affirm that geopolymer materials are only very little affected by temperatures up to 800°C, or even 900°C (keeping its mesoporous amorphous structure).     

Inlet conditions effects on vertical wall jets in forced and mixed convection regimes

In this paper a numerical investigation of a laminar isothermal or nonisothermal two dimensional plane wall jet is carried out. Special attention has been paid to the effect of the inlet conditions at the nozzle exit on the hydrodynamic and thermal characteristics of the flow, in both convection regimes: forced and mixed. Two velocities profiles at the nozzle exit are used: uniform profile and parabolic profile. The Prandtl number effect on the jet flow characteristics is also analyzed in the case of forced convection regime.The system of governing equations is solved with an implicit finite difference scheme. For numerical stability we use a staggered non-uniform grid. The obtained results show, for the two convection modes, that the inlet conditions affect the flow in the immediate neighbourhood of the nozzle (core region) in which the flow is governed mainly by the inertia forces. In the established region the results become independent of the flow inlet conditions. Secondly, the effect of the Prandtl number is significant in the plume region in which the jet flow is governed by buoyant forces.     

Temperature stability of a pure metakaolin based K-geopolymer: Part 1. Variations in the amorphous mineral network

The thermal behavior of a model MK based K-geopolymer (Si/Al = 1.38 and K/Al = 0.68; obtained by alkaline activation of a very pure metakaolin) was investigated between room temperature and 1400°C in order to evaluate its potentiality for high-temperature applications. The purpose of our study was to monitor the behavior of a geopolymer during a temperature rise in order to better understand its variations with respect to temperature. The works from the present paper focus only variations in the internal structure of the mineral matrix. The results presented here show that the amorphous mineral matrix is preserved up to 900°C. The results also show that there is a densification of the internal structure of tetrahedral network during heating, due to changes in the Q³ environments in fully-connected Q⁴ for both silicates and aluminates. Thus, our work provides a new more precise vision of the 3D geopolymeric mineral matrix for which the silicoaluminous network is not exclusively composed of Q⁴ entities, contrary to what is frequently encountered in the literature before.     

Characterisation and quantification of phenolic compounds of extra-virgin olive oils according to their geographical origin by a rapid and resolutive LC–ESI-TOF MS method

The phenolic compounds present in seven samples of olive fruits were analysed by a rapid and resolutive LC–ESI-TOF MS method. All samples were collected during the normal picking period for olive oil production, in central and south Tunisia, and were obtained from the Oueslati variety cultivated in different olive growing areas.     

Risk based intrusion detection system in software defined networking

Software defined networking (SDN) separates control from data operations. However, this technology adds a new security cost to the network architecture because of the ongoing and developing security vulnerabilities. An intrusion detection system must be continuously improved and integrated into the SDN architecture in order to provide a network defense against attacks. In this study, we propose a continual learning system based on risk assessment to detect intrusion in SDN. We suggest a technique for continually enhancing datasets to produce a more accurate prediction. The proposed system includes various processes, including risk assessment and the selection of the deep learning (DL) approach. We propose assessing the risks related to different intrusion types. Based on the risk value, we can identify which intrusion types are more important and have a dangerous impact. We use the risk values to choose the most appropriate DL approach and for the dataset's continual enrichment. We compare different DL methods using the standard metrics and two proposed metrics. Then, we propose to use a method based on the bit alternation approach to obtain a unique metric for decision-making. Finally, we have studied the efficacy of our system using two case studies.     

Analysis of search stratagem utilisation

Scientometrics - In interactive information retrieval, researchers consider the user behaviour towards systems and search tasks in order to adapt search results and to improve the search experience...     

Scheduling problems under learning effects: classification and cartography

Traditionally, the processing times of jobs are assumed to be fixed and known throughout the entire process. However, recent empirical research in several industries has demonstrated that processing times decline as workers improve their skills and gain experience after doing the same task for a long time. This phenomenon is known as learning effects. Recently, several researchers have devoted a lot of effort on scheduling problems under learning effects. Although there is increase in the number of research in this topic, there are few review papers. The most recent one considers solely studies on scheduling problems with learning effects models prior to early 2007. For that, this paper focuses on reviewing the most recent advances in this field. First, we attempt to present a concise overview of some important learning models. Second, a new classification scheme for the different model of scheduling under learning effects is proposed and discussed. Next, a cartography showing the relation between some well-known models is proposed. Finally, our viewpoints and several areas for future research are provided.     

Optimal network-aware virtual data center embedding

Recently, the virtual data center embedding (VDCE) problem has drawn significant attention because of a growing need for efficient means of data center resource allocation. By ensuring a set of virtual data center (VDC) integration requests coming from his customers, among the main concern of an infrastructure provider is the maximization of the utilization rate of data center resources and benefits. However, existing VDCE solutions mostly focus on consolidating virtual machines in a single physical data center. Therefore, in this work, we improve the consolidated targets techniques, that consider only the virtual machines integration, by the consideration of network devices and fabrics (e.g., switches and paths/links). We consider new unreleased constraints such as multiple virtual nodes of the same request co-location, and intermediate node requirements when a virtual link is mapped. To address the above problem, in this paper, we propose a binary linear programming-based model, called BLP-VDCE, to solve the VDCE problem with network-aware consideration. This model ensures a simultaneous consolidated embedding of virtual nodes and virtual links. Extensive simulations show that solving the proposed BLP-VDCE model can efficiently embed VDC requests with a high physical resource utilization rate.