Evaluation of Digitally Encoded Layer‐by‐layer Coated Microparticles as Cell Carriers

To obtain more biologically relevant data there is a growing interest in the use of living cells for assaying the biological activity of unknown chemical compounds. Density ‘multiplex’ cell‐based assays, where different cell types are mixed in one well and simultaneously investigated upon exposure to a certain compound are beginning to emerge. To be able to identify the cells they should be attached to microscopic carriers that are encoded. This paper investigates how digitally encoded microparticles can be loaded with cells while keeping the digital code in the microcarriers readable. It turns out that coating the surface of the encoded microcarriers with polyelectrolytes using the layer‐by‐layer (LbL) approach provides the microcarriers with a ‘highly functional’ surface. The polyelectrolyte layer allows the growth of the cells, allows the orientation of the cell loaded microcarriers in a magnetic field, and does not hamper the reading of the code. It has further been shown that the cells growing on the polyelectrolyte layer can become transduced by adenoviral particles hosted by the polyelectrolyte layer. It is concluded that the digitally encoded microparticles are promising materials for use in biomedical and pharmaceutical in‐vitro research where cells are used as tools.     

Modeling of the AC Arc Discharge on Snow-covered Insulators

A mathematical model for predicting the ac flashover voltage of snow-covered insulators is presented. The arc constant parameters in air gaps and inside snow, as well as the arc reignition condition are determined using a cylindrical model. The effects of the arc length on the arc constants parameters are also investigated. The model is then applied to an EPDM insulator artificially covered with natural snow. There is good concordance between the flashover results determined from the mathematical model and those obtained experimentally.     

High-speed techniques for estimating t1, t2, and density images

This work presents methods for estimating relaxation times T1 and T2 and proton density using a real time digital video processor. This device performs each mathematical step of the fitting algorithms in one video frame interval (1/30 s). Two-point fits of T1 and T2 may be generated in approximately 15 video frame intervals or about 0.5 s; generation of a T2 image from four acquired images requires about 30 video frame intervals or about 1 s. The hardware is common to many commercial MR scanners and requires no array processor. Such high-speed techniques can expedite the clinical use of computed images and facilitate the implementation of image synthesis.     

Theoretical and experimental analyses of rheological,compatibility and mechanical properties of PVMQ/XNBR-g GMA/XNBR/GO ternary hybrid nanocomposites

Iranian Polymer Journal - Effects of graphene oxide (GO) on various properties of rubber hybrid nanocomposites based on PVMQ/XNBR-g-GMA/XNBR (phenyl-vinyl-methyl-polysiloxane/carboxylated nitrile...     

Carbon nanotube based miniaturized electron gun and column assembly

We report on the application of silicon micromachining for the fabrication of miniaturized electron gun (MEG) assembly using vertically aligned carbon nanotubes. The proposed MEG consists of two main parts of electron gun and the accelerating column. While the electron gun consists of carbon nanotubes grown on a silicon substrate acting as an electron emission source, the accelerating column is made of micromachined silicon wafers with 5 μm thick membranes operating as objective lenses. These two wafers are placed together and sealed using a three-dimensional packaging technique. The simulation and experimental results show the evolution of a narrow electron beam by applying a proper voltage to the anodes and objective lens. The diverging and focusing of the beam can be controlled by applying the proper voltage on electrostatic lenses. This structure could be suitable for low energy SEM devices and surface physics applications.     

Estimation of the critical properties of compounds using volume-based thermodynamics

The importance, yet scarcity of the critical constants of thermally unstable fluids warrants the development of reliable methods for the estimation of these essential thermodynamic properties. A thorough investigation undertaken in this study on 1,589 compounds belonging to 83 chemical classes, indicated that the ratio of critical temperature to critical pressure of both low and high molecular weight compounds could be well expressed in terms of their volumetric properties. In addition, two new methodologies are presented for estimating V_c, as well as an indirect approach for prediction of T_c from surface tension data, altogether allowing the calculation of Z_c. Moreover, comparative studies are made with five group contribution methods. It is also demonstrated that by employing the Peng-Robinson EOS, and without prior knowledge of the critical properties, it is possible to calculate various thermophysical properties including P^sat., T_b, , ∆H^vap. , C_p, and even the T_c and P_c themselves.     

CQARPL: Congestion and QoS-aware RPL for IoT applications under heavy traffic

The Journal of Supercomputing - The Internet of Things (IoT) provides a common platform to connect the heterogeneous devices over the internet. Hence, the number of devices connected via the...     

High strain electromechanical actuators based on electrodeposited polypyrrole doped with di-(2-ethylhexyl)sulfosuccinate

The low-voltage electromechanical actuation of polypyrrole (PPy) doped with di-(2-ethylhexyl)sulfosuccinate (DEHS) has been investigated. The PPy-DEHS has been prepared both chemically (cast as films from solution) and by more conventional electrochemical polymerization. Very large strains of ∼30% were obtained during slow-scan redox cycling of the electrochemically prepared PPy-DEHS films. In constrast, PPy-DEHS films cast from solutions of the chemically polymerized polymer gave actuation strains of ∼2.5%. The polymerization method was also found to have a significant effect on the structure, conductivity and mechanical properties of the PPy-DEHS materials. The conductivity of the electrochemically polymerized PPy-DEHS was 75 S cm⁻¹, considerably higher than that found for the chemically derived polymer (7 S cm⁻¹). The structure of the PPy-DEHS was further elucidated from UV-vis, Raman and FT-IR spectral studies which indicated that the conjugation length of the PPy could be increased significantly by varying the polymerization method. Films obtained by casting chemically prepared PPy-DEHS showed higher modulus (2.3 GPa) than electropolymerized PPy-DEHS (0.6 GPa), but were more brittle. Both materials were electroactive in acetonitrile/water electrolyte. The higher actuation strain observed in the electrochemically prepared films was attributed to a more open molecular structure (as indicated by the lower modulus) allowing for easier ion diffusion and a higher conductivity allowing easier charge transfer.     

Définition théorique des paramètres en fort niveau d’un multipôle actif non linéaire

In this paper, large signal non linear P parameters are rigorously defined, allowing the characterization of non linear active n-port circuits in the frequency domain with the aim of computer aided design of power microwave devices. No a priori conditions bind the parameters to the variables describing the input signal. However the following rules apply to the parameters: they may be function of any input variables of the n-port circuit; the expression of the large-signal parameters should become identical to the expression of the linear parameters characterizing the n-port circuit under linear operating conditions when the values of the input variables tend to zero. An application of the definition to afet is demonstrated.     

An adaptive learning automata-based ranking function discovery algorithm

Due to the massive amount of heterogeneous information on the web, insufficient and vague user queries, and use of the same query by different users for different aims, the information retrieval process deals with a huge amount of uncertainty and doubt. Under such circumstances, designing an efficient retrieval function and ranking algorithm by which the most relevant results are provided is of the greatest importance. In this paper, a learning automata-based ranking function discovery algorithm in which different sources of information are combined is proposed. In this method, the learning automaton is used to adjust the portion of the final ranking that is assigned to each source of evidence based on the user feedback. All sources of information are first given the same importance. The proportion of a given source increases, if the documents provided by this source are reviewed by the user and decreases otherwise. As the proposed algorithm proceeds, the probability of appearance of each source in the final ranking gets proportional to its relevance to the user queries. Several simulation experiments are conducted on well-known data collections and query types to show the performance of the proposed algorithm. The obtained results demonstrate that the proposed algorithm outperforms several existing methods in terms of precision at position n, mean average precision, and normalized discount cumulative gain.