Novel PVC-based copper(II) membrane sensor based on 2-(1'-(4'-(1'-hydroxy-2'-naphthyl)methyleneamino)butyl iminomethyl)-1-naphthol.

A copper(II) ion-selective PVC membrane sensor based on 2-(1'-(4'-(1'-hydroxy-2'-naphthyl)methyleneamino)butyl iminomethyl)-1-naphthol (BHNB) as a novel Schiff base containing a sensing material has been successfully developed. The sensor exhibits a good linear response of 29 mV per decade within the concentration range of 10(-1)-10(-6) M of Cu2+. The sensor shows good selectivity for copper(II) ion in comparison with alkali, alkaline earth, transition and heavy metal ions. The BHNB-based sensor is suitable for use with aqueous solutions of pH 3.5-7.0 and displays minimal interference by Sr(II), Cd(II), Hg(II), Zn(II) and Pb(II), which are known to interfere with other previously suggested electrodes. The proposed membrane electrode was used as a sensor for determining the Cu(II) content in black tea samples. It was also applied as an indicator electrode in the potentiometric titration of Cu2+ ions with EDTA.     

INTREPID: an integrated network tool for routing, evaluation ofperformance, and interactive design

The network design problem discussed is that of selecting nodes and links in a network. This includes deciding where to place network processors and links and what capacity nodes and links to use at each location. At a higher level, these decisions also determine the network architecture. The focus is on a two-level network, backbone and local access. Although the selection of specific equipment or the detailed configuration of devices is not discussed, INTREPID is sufficiently fast and flexible to be of use in making such decisions. The principles used in the development of INTREPID and its components are described. A top-level view of INTREPID is followed by a discussion of the design flow, utility modules, and design algorithms. Experience with the tool is briefly described     

Application of modified magnetic nanoparticles with amine groups as an efficient solid sorbent for simultaneous removal of 2,4-Dichlorophenoxyacetic acid and 2-methyl-4-chlorophenoxyacetic acid from aqueous solution: optimization and modeling

In the present work, functionalized magnetic nano-adsorbent with amine groups (Fe₃O₄@SiO₂@NH₂) was prepared for the simultaneous removal of 2,4-Dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) from aqueous solution. Characterization such as Fourier transform infrared spectroscopy, vibrating sample magnetometry, and scanning electron microscope confirmed that the magnetic nanoparticles structure of Fe₃O₄@SiO₂ nano-adsorbent was successfully functionalized by amine groups. The impact of some influencing parameters such as contact time, pH, adsorbent dosage, 2,4-D and MCPA initials concentration and solution temperature were studied. The equilibrium data were analyzed by Langmuir and Freundlich adsorption isotherms and also two models kinetically of pseudo-first-order and pseudo-second-order. Findings of the present study showed that the synthesized amino-functionalized MNPs will be helpful in use as an effective recyclable adsorbent for the removal of phenoxy acid herbicides from aqueous solution due to its advantages such as facile and rapid separation of target molecules from solution.     

Optimizing the choice of influential nodes for diffusion on a social network

An optimization approach is taken to locating the optimal set of initial contacts in a social network to maximize the number of total network members reached by a message. It is assumed that initial contacts are costly and that the number of initial contacts must be minimized simultaneously with maximizing the total number of network members contacted. A bi‐objective probabilistic integer programming model is developed that assumes that actors are heterogeneous in the probability that they will pass messages along their ego networks. Considering the complexity of solving the proposed model, it reformulated as a pure integer programming model. The algorithm is illustrated by the analysis of message passing in a short‐message system (texting) among university students. Copyright © 2015 John Wiley & Sons, Ltd.     

Continuous contact force models for impact analysis in multibody systems

One method for predicting the impact response of a multibody system is based on the assumption that the impacting bodies undergo local deformations and the contact forces are continuous. In a continuous analysis, the integration of the system equations of motion is carried out during the period of contact; therefore, a model for evaluating the contact forces is required. In this paper, two such contact force models are presented, both Hertzian in nature and based upon the direct-central impact of two solid particles.At low impact velocities, the energy dissipation during impact can be represented by material damping. A model is constructed based on the general trend of the Hertz contact law in conjuction with a hysteresis damping function. The unknown parameters are determined in terms of a given coefficient of restitution and the impact velocity. When local plasticity effects are the dominant factor accounting for the dissipation of energy at high impact velocities, a Hertzian contact force model with permanent indentation is constructed. Utilizing energy and momentum considerations, the unknown parameters in the model are again evaluated. The two particle models are generalized to an impact analysis between two bodies of a multibody system.     

Emotion classification during music listening from forehead biosignals

Emotion recognition systems are helpful in human–machine interactions and clinical applications. This paper investigates the feasibility of using 3-channel forehead biosignals (left temporalis, frontalis, and right temporalis channel) as informative channels for emotion recognition during music listening. Classification of four emotional states (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) in arousal–valence space was performed by employing two parallel cascade-forward neural networks as arousal and valence classifiers. The inputs of the classifiers were obtained by applying a fuzzy rough model feature evaluation criterion and sequential forward floating selection algorithm. An averaged classification accuracy of 87.05 % was achieved, corresponding to average valence classification accuracy of 93.66 % and average arousal classification accuracy of 93.29 %.