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.     

Hydrodynamics of gas fluidized beds with mixture of group D and B particles

The dynamics of a gas‐solid fluidized bed containing Geldart Group D particles mixed with a small proportion of Geldart Group B particles are investigated using pressure fluctuations data. Time series analysis, using a variety of nonlinear dynamics tools, shows that the slugging present with Group D particles can be suppressed by the addition of a small proportion of Group B particles. The power spectra and the auto‐correlation function are used for a preliminary evaluation of dominant slug frequencies. It is shown that the bed fluidized with the mixture of Group D and B particles behaves in a less periodic manner and is dominated by more random bubble motion. On the other hand, the correlation integral is used to analyze the chaotic behaviour of the flow, through evaluation of the fractal structure of the reconstructed attractors. It is shown that the bed fluidized with Group D particles is characterized by a combination of steady slug motion and irregular particles motion. The behaviour of the fluidized bed of Group D/B particles mixture is characterized by a single correlation dimension for a wide range of fluidization velocities.     

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.     

Classification of Static and Dynamic Behavior in Chemostat for Plasmid-Bearing,Plasmid-Free Mixed Recombinant Cultures

The singularity theory is combined with continuation techniques to classify the static and dynamic behavior in a chemostat involving the competition between plasmid-bearing and plasmid-free cell populations. The analysis of the static bifurcation allows the derivation of analytical conditions for the coexistence of the competing populations and for the safe operation of the’bioreactor. The analysis of dynamic bifurcation, on the other hand, shows the ability of the model to predict the coexistence of the two populations in a state of stable limit cycle. Analytical conditions with respect to any growth kinetics are derived for the occurrence of Hopf points in the model. The combination of results of both static and dynamic bifurcation helps to construct a useful picture, in the multidimensional parameter space, of the different behavior predicted by the model.