Effective capacity maximization of two-way full-duplex and half-duplex relays with finite block length packets transmission

Wireless Networks - In order to satisfy the delay requirements of telecommunication systems, in this paper, we present a cooperative network with the short packet transmission in the Rayleigh...     

Intravitreal tolerance of a new perfluorocarbon vitreous replacement, Multifluor APF-144

OBJECTIVE: To evaluate the intravitreal tolerance of a new perfluorocarbon vitreous replacement, Multifluor APF-144 (perfluorotetramethylcyclohexane). DESIGN: Ten New Zealand albino rabbits (one eye from each) underwent vitrectomy. The vitreous was replaced in five eyes with Multifluor APF-144 and in five eyes with saline (control group). OUTCOME MEASURES: Appearance on indirect ophthalmoscopy, electroretinography recordings before and 2, 4 and 8 weeks after vitrectomy, findings on electron and light microscopy at 8 weeks. RESULTS: Endophthalmitis did not develop in any of the eyes. There was no significant change in electroretinography values for the experimental eyes after vitrectomy. No evidence of retinal toxicity was found on light or electron microscopic examination. CONCLUSIONS: Multifluor APF-144 shows promise as a short-term postoperative retinal tamponading agent.     

Synthesis and characterization of novel diimide–dinaphthols and resulting poly(urethane–imide)s

Novel diols containing imide groups were prepared via condensation of aromatic dianhydrides with 5‐amino‐1‐naphthol. The diimide–dinaphthols prepared were characterized by conventional methods and used to synthesize new poly(urethane–imide)s (PUIs). All the polymers were characterized and their physical properties, such as solubility, solution viscosity, thermal stability, and thermal behaviour were studied. The polymers obtained showed more thermal stability than typical polyurethanes because of the presence of the imide groups. Copyright © 2003 Society of Chemical Industry     

基于不同爆轰产物的格尼速度估算新方法（英）

A new method for prediction of Gurney velocity of explosives is introduced in which energy output is correlated with the heat of detonation, the number of moles of gaseous products of detonation per gram of explosive and the average molecular weight of gaseous products. It is assumed that the CHNO explosive reacts to form products composed of N2 , CO, H2O, CO2, H2,O2 and C（s） as determined by the oxygen balance of the unreacted compound. Good agreement is obtained between measured and calculated values of Gurney velocity as compared to previous correlations which assumed the reaction products to consist of N2 , H2O, CO2 and either C（s） or O2.     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Transport of Gellan Gum Microbeads in Soil Columns of Various Grain Size Distributions

The transport of gellan gum microbeads as potential cell carriers was investigated in horizontal columns packed with different grain size classes of gravel (2–16 mm) and sand (0.25–2 mm). A suspension of microbeads was pulsed into each column for 6 h, followed by injection of water for 42 h. In general, the total amount of microbeads travelling across a given section of the column increased with injection time but decreased towards the column outlet, varying as a direct function of grain size. The results of this study demonstrate the feasibility of the transport of gellan gum microbeads through medium sand to medium gravel across distances up to 110 cm.     

Quadratic approximate dynamic programming for input‐affine systems

We consider the use of quadratic approximate value functions for stochastic control problems with input‐affine dynamics and convex stage cost and constraints. Evaluating the approximate dynamic programming policy in such cases requires the solution of an explicit convex optimization problem, such as a quadratic program, which can be carried out efficiently. We describe a simple and general method for approximate value iteration that also relies on our ability to solve convex optimization problems, in this case, typically a semidefinite program. Although we have no theoretical guarantee on the performance attained using our method, we observe that very good performance can be obtained in practice.Copyright © 2012 John Wiley & Sons, Ltd.     

(Approximate) Uncertain Skylines

Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discrete set of locations, we improve the best known exact solution. We also suggest why we believe our solution might be optimal. Next, we describe simple, near-linear time approximation algorithms for computing the probability of each point lying on the skyline. In addition, some of our methods can be adapted to construct data structures that can efficiently determine the probability of a query point lying on the skyline.     

Copper/copper oxide nanoparticles synthesis using Stachys lavandulifolia and its antibacterial activity

Nanoparticles of copper/cuprous oxide (Cu/Cu₂ O) were successfully synthesised by a green chemistry route. The synthesis process was carried out using an extract of Stachys lavandulifolia as both reducing and capping agents with a facile procedure. The nanoparticles were characterised by different techniques including X‐ray diffraction, indicating that the synthesised sample comprised both copper and cuprous oxide entity. The nanoparticles had a mean size of 80 nm and represented an impressive bactericidal effect on Pseudomonas aeruginosa.Inspec keywords: copper, copper compounds, nanoparticles, nanofabrication, nanomedicine, antibacterial activity, X‐ray diffractionOther keywords: nanoparticles synthesis, Stachys lavandulifolia, antibacterial activity, green chemistry route, reducing agents, capping agents, X‐ray diffraction, bactericidal effect, Pseudomonas aeruginosa, Cu‐Cu₂ O     

Dynamic estimation of the modeling error statistics in Diffuse Optical Tomography

Diffuse Optical Tomography (DOT) is a non-invasive imaging technique that suffers from a typical large-scale and ill-posed inverse problem with low spatial resolution. In DOT, the inverse problem is computationally intensive and decreasing the computation complexity and making it well-posed is the one of the most challenging research areas. More precisely, one of the well-known complexity reduction techniques is defined as applying modelling error originated from discretization of forward problem. Applying the discretization error in Bayesian inference has already been discussed; the method in which the likelihood is modified by an off-line prior density estimation. This paper implements a new method to enhance the modelling error approach using an iterative scheme to update statistical parameters of modelling discrepancy in DOT. The algorithm is very similar to Ensemble Kalman Filter. Moreover, the reconstruction process in the applied method is conducted by a small sample size rather than off-line method. Hence, the computation complexity is decreased and the algorithm converges in few iterations. The efficiency of the proposed method is illustrated by simulations.