Unexploited Thapsia garganica,Orlaya maritima,and Retama raetam Seeds: Potential Sources of Unsaturated Fatty Acid and Natural Antioxidants

Total lipid contents, fatty acid compositions, phenolic profiles and antioxidants activities of seeds from Thapsia garganica, Orlaya maritima, and Retama raetam were investigated. The oil values were more than 26 %, except seeds of R. raetam (ca. 3 %). Unsaturated fatty acids accounted for the majority of the fatty acids (more than 75 %). Oleic and linoleic acid were the predominant fatty acids. Total phenolic compounds (24–104 mg GAE g^?1 DR), total flavonoids (4–102 mg QE g^?1g DR), total tannins (28–85 mg GAE g^?1 DR) and condensed tannins (0.62–131 mg CE g^?1 DR) were also determined. The antioxidant activities using different assays were evaluated. The predominant detected classes were the phenolic acids (42–85 %) and the flavonoids (11–48 %). The major phenolic acids were caffeic, trans‐4‐hydroxy‐3‐methoxycinnamic, p‐coumaric, and gallic acid. The predominant flavonoids were quercetin, luteolin, naringin, apigenin, and kaempferol. This study brings attention to the medicinal importance of these species as a source of oil and antioxidant molecules.     

MQBV: multicast quality of service swarm bee routing for vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) are witnessing in recent years a rapid development for road transmissions and are considered as one of the most important types of next generation networks, in which drivers can have access anywhere and anytime to information. However, vehicles have to deal with many challenges such as the links failures due to their frequent mobility as well as limited degrees of freedom in their mobility patterns. In this paper, we propose a new quality of service multicast and multipath routing protocol for VANETs, based on the paradigm of bee's communication, called multicast quality of service swarm bee routing for VANETs (MQBV). The MQBV finds and maintains robust routes between the source node and all multicast group members. Therefore, the average end‐to‐end delay and the normalized overhead load should be reduced, while at the same time increasing the average bandwidth and the packet delivery ratio. Extensive simulation results were obtained using ns‐2 simulator in a realistic VANET settings and demonstrated the efficiency of the proposed protocol. Copyright © 2013 John Wiley & Sons, Ltd.     

Blast furnace slags as functional fillers on rheological,thermal, and mechanical behavior of thermoplastics

Blast furnace slags (BFS) is a secondary byproduct of iron industry, which has a combination of acidic and basic oxides and show a complex, multiphase structure. If appropriately tailored, BFS could be an effective functional filler, improving the property profile of thermoplastics such as polypropylene (PP) and polystyrene (PS). As a raw material, the proposed filler may introduce both economic and ecological advantages, as it is considered an inexpensive secondary product rather than a natural resource. The current study aims at investigating the effect of incorporating BFS as a micro‐sized filler on the rheological, thermal, and mechanical behavior of PP and PS. BFS types in this study are air‐cooled, crystalline, and amorphous, grounded types. Both types are ground into 71, 40, and 20 μm batches and introduced in 10, 20, and 30 weight fractions via melt kneading. Mixtures are then formed into 4‐mm and 2‐mm thick plates via compression molding. Slight increase in rheological factors is observed with increasing filler loading. BFS hinders the crystallization of PP, resulting in slight increase of crystallization temperatures (T_c) and lowering of crystallization enthalpy (ΔH_c). No significant effect of filler on transition temperatures (T_g) is reported. Mechanically, BFS increases the tensile modulus of PP, but decreases its strength. For PS formulations, a modest toughening effect is observed by slag filler. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43021.     

New syntheses of pyrazolo[3,4-d]pyrimidine,pyrazolo[3,4-d]pyridazine,isoindolinedione and pyrazole derivatives

Phenacylmalononitriles 2 react with hydrazines, acetic-hydrochloric acid and with diazotised primary aromatic amines to afford phenacylpyrazole ( 5a,b ), aminofurans ( 6a,b ) and aminopyrazole derivatives ( 3a,d ) respectively. The synthesised derivatives ( 3d, 6a ) were the key materials for the synthesis of isoindolinedione, ( 7a – c ) pyrazolopyrimidine ( 9, 10 ), and pyrazolopyridazine derivatives ( 11 ). The structures of the newly synthesised heterocycles were established on the basis of elemental analyses and spectral data besides synthesis via other routes.     

Learning with partly labeled data

Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of a classifier. This paper outlines the two main classes of learning methods to deal with partly labeled data: pre-labeling-based learning and semi-supervised learning. Concretely, we introduce and discuss three methods from each class. The first three ones are two-stage methods consisting of selecting the data to be labeled and then training the classifier using the pre-labeled and the originally labeled data. The last three ones show how labeled and unlabeled data can be combined in a symbiotic way during training. The empirical evaluation of these methods shows: (1) pre-labeling methods tend be better than semi-supervised learning methods, (2) both labeled and unlabeled have positive effect on the classification accuracy of each of the proposed methods, (3) the combination of all the methods improve the accuracy, and (4) the proposed methods compare very well with the state-of-art methods.     

Towards Incremental Fuzzy Classifiers

Fuzzy classification systems (FCS) are traditionally built from observations (data points) in an off-line one shot-experiment. Once the learning phase is exhausted, the classifier is no more capable to learn further knowledge from new observations nor is it able to update itself in the future. This paper investigates the problem of incremental learning in the context of FCS. It shows how, in contrast to off-line or batch learning, incremental learning infers knowledge in the form of fuzzy rules from data that evolves over time. To accommodate incremental learning, appropriate mechanisms are applied in all steps of the FCS construction: (1) Incremental supervised clustering to generate granules in a progressive manner, (2) Systematic and automatic update of fuzzy partitions, (3) Incremental feature selection using an incremental version of Fisher’s interclass separability criterion. The effect of incrementality on various aspects is demonstrated via a numerical evaluation.     

Adaptive quality of service‐based routing approaches: development of neuro‐dynamic state‐dependent reinforcement learning algorithms

In this paper, we propose two adaptive routing algorithms based on reinforcement learning. In the first algorithm, we have used a neural network to approximate the reinforcement signal, allowing the learner to take into account various parameters such as local queue size, for distance estimation. Moreover, each router uses an online learning module to optimize the path in terms of average packet delivery time, by taking into account the waiting queue states of neighbouring routers. In the second algorithm, the exploration of paths is limited to N‐best non‐loop paths in terms of hops number (number of routers in a path), leading to a substantial reduction of convergence time. The performances of the proposed algorithms are evaluated experimentally with OPNET simulator for different levels of traffic's load and compared with standard shortest‐path and Q‐routing algorithms. Our approach proves superior to classical algorithms and is able to route efficiently even when the network load varies in an irregular manner. We also tested our approach on a large network topology to proof its scalability and adaptability. Copyright © 2006 John Wiley & Sons, Ltd.     

On consensus algorithms design for double integrator dynamics

This paper considers the consensus problem of double integrator multi-agent systems where: (i) each agent is subject to input saturations, and (ii) the velocity (second state) of each agent is not available for feedback. We present new consensus algorithms that handle simultaneously the above mentioned situations. Sufficient conditions are derived such that consensus algorithms developed for first- and second-order multi-agent systems in ideal situations can be used to account for input saturations and remove the requirement of velocity measurements. To illustrate the effectiveness of the proposed approach, we propose solutions to two different second-order consensus problems in the case where the input is saturated and the velocity states are not available for feedback and simulation results are provided in each case.