Effect of ZnO layer thickness upon optoelectrical properties of NiO/ ZnO heterojunction prepared at room temperature

In this work, p-NiO/n-ZnO heterostructures were successfully prepared at room temperature using RF sputtering technique. The influence of ZnO layer thickness on the performance of the heterojunction was investigated. The deposited ZnO layers have a hexagonal Wurtzite structure with preferable growth orientations along (002) and (103) for thinner films. Increasing the thickness results in more crystallographic orientation randomness. The current–voltage measurements of the realized heterojunctions showed a clear rectifying behavior. The measured ideality factor varies from 2.5 to 1.6 according to the thickness of ZnO layer. The series resistance of the device is enlarged with increasing ZnO thickness. The deduced parameters from the I–V characteristics suggest that 200 nm is the optimal thickness of the ZnO layer according to our experimental conditions. We attribute the relatively better performance of this thickness to achieving reasonable compensation between serial resistance and ideality factor. The best heterojunction was tested and successfully used as a UV detector.     

Cross-Layer Optimization of Dynamic Packet Assignment for Video Transmission Over IEEE 802.11e Networks

Video transmission over IEEE 802.11e wireless networks still shows poor performance for large bandwidth demand and frequently changed environments. Thus, several enhancements of IEEE 802.11e were proposed. On the other hand, big frames and simultaneous sending of adjacent frames always cause packet dropping for buffer overflow. In the past, we proposed an IEEE 802.11e enhancement named DFAA and a content aware mechanism to solve the above problems. The motivation of this paper is to find a proper way to integrate these two mechanisms. A DFAA enhancement (DFAA-E) is proposed to make up the insufficiency of content aware mechanism. Experiments results show that the combination of DFAA-E and content aware mechanism improves the video decoded quality greatly. And its performance can be further enhanced by selecting the suitable settings of certain parameters.     

Evolution of collaboration and optimization of impact: self-organization in multinational research

Scientometrics - The expanding presence of multinational research teams highlights the importance of characterizing the outcomes of international collaboration. Herein, we characterize the...     

Reoptimization framework and policy analysis for maritime inventory routing under uncertainty

We study a maritime inventory routing problem, in which shipments between production and consumption nodes are carried out by a fleet of vessels. The vessels have specific capacities and can be chartered under different agreements. The inventory levels of all consumption nodes and some production nodes should be maintained within specified bounds; for the remaining production nodes, orders should be picked up within pre-defined time windows. We propose a discrete-time mixed-integer programming model. In the face of new information and uncertainty, this optimization model has to be re-solved, as the horizon is rolled forward. We discuss how to account for different sources of uncertainty. We present a rolling-horizon reoptimization framework that allows us to study different policies that impact the quality of the implemented solution, so we can identify the optimal set of policies.     

Quantum Sensing in a Physiological‐Like Cell Niche Using Fluorescent Nanodiamonds Embedded in Electrospun Polymer Nanofibers

Fluorescent nanodiamonds (fNDs) containing nitrogen vacancy (NV) centers are promising candidates for quantum sensing in biological environments. This work describes the fabrication and implementation of electrospun poly lactic‐co‐glycolic acid (PLGA) nanofibers embedded with fNDs for optical quantum sensing in an environment, which recapitulates the nanoscale architecture and topography of the cell niche. A protocol that produces uniformly dispersed fNDs within electrospun nanofibers is demonstrated and the resulting fibers are characterized using fluorescent microscopy and scanning electron microscopy (SEM). Optically detected magnetic resonance (ODMR) and longitudinal spin relaxometry results for fNDs and embedded fNDs are compared. A new approach for fast detection of time varying magnetic fields external to the fND embedded nanofibers is demonstrated. ODMR spectra are successfully acquired from a culture of live differentiated neural stem cells functioning as a connected neural network grown on fND embedded nanofibers. This work advances the current state of the art in quantum sensing by providing a versatile sensing platform that can be tailored to produce physiological‐like cell niches to replicate biologically relevant growth environments and fast measurement protocols for the detection of co‐ordinated endogenous signals from clinically relevant populations of electrically active neuronal circuits.     

Control of reactive distillation production of high-purity isopropanol

The process characteristics and control strategy of a high-purity IPA reactive distillation column were investigated. A robust nominal operation was found by maintaining an excess of propylene feed to the column and recycling the unreacted propylene to the feed instead of the top stage. Stage temperature and propylene composition with one-to-one relationship with reboiler duty and propylene feed are selected as controlled variables for maintaining bottom purity and feed ratio in the presence of possible measurement bias respectively. High nonlinearity between selected input–output pair was reduced by using variable transformation. Dynamic simulations demonstrated that such a control scheme with nonlinear transformed variable was capable of providing much superior control performance than the one using natural variable.     

Synthesis and characterization of biocompatible hydroxyapatite coated ferrite

Ferrite particles coated with biocompatible phases can be used for hyperthermia treatment of cancer. We have synthesized substituted calcium hexaferrite, which is not stable on its own but is stabilized with small substitution of La. Hexaferrite of chemical composition (CaO)_0.75(La₂0₃)_0.20(Fe₂O₃)₆ was prepared using citrate gel method. Hydroxyapatite was prepared by precipitating it from aqueous solution of Ca(NO₃)₂ and (NH₄)₂HPO₄ maintaining pH above 11. Four different methods were used for coating of hydroxyapatite on ferrite particles. SEM with EDX and X-ray diffraction analysis shows clear evidence of coating of hydroxyapatite on ferrite particles. These coated ferrite particles exhibited coercive field up to 2 kOe, which could be made useful for hysteresis heating in hyperthermia. Studies by culturing BHK-21 cells and WBC over the samples show evidence of biocompatibility. SEM micrographs and cell counts give clear indication of cell growth on the surface of the sample. Finally coated ferrite particle was implanted in Kasaulli mouse to test its biocompatibility. The magnetic properties and biocompatibility studies show that these hydroxyapatite coated ferrites could be useful for hyperthermia.     

Electrochemical synthesis and characterization of chloride doped polyaniline

Chloride doped polyaniline conducting polymer films have been prepared in a protic acid medium (HCl) by potentiodynamic method in an electrochemical cell and studied by cyclic voltammetry and FTIR techniques. The FTIR spectra confirmed Cl^- ion doping in the polymers. The polymerization rate was found to increase with increasing concentration of aniline monomer. But the films obtained at high monomer concentration were rough having a nonuniform flaky polyaniline distribution. Results showed that the polymerization rate did not increase beyond a critical HCl concentration. Cyclic voltammetry suggested that, the oxidation-reduction current increased with an increase in scan rate and that the undoped polyaniline films were not hygroscopic whereas chloride doped polyaniline films were found to be highly hygroscopic.     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto