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...     

Study of Adsorption/Desorption Effect of 2-Mercaptobenzothiazole as Sweet Corrosion Inhibitor on API-5L X60 Steel

Protection of Metals and Physical Chemistry of Surfaces - In this study, the inhibitive performance of 2-mercaptobenzothiazole (2MBT) and 2-aminobenzothiazole (2ABT) were investigated on API-5L X60...     

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 potential trackable bone filler: Preparation and characterization

This study reported the synthesis of fluorescent hydroxyapatite/alginate/carbon quantum dots (HA/Alg/CQDs) nanocomposites via the co-precipitation technique. The N-doped CQDs as a new class of fluorescent materials were prepared by the citric acid pyrolysis method, with an average size around 4 nm. Physical, chemical, and optical properties of the synthesized nanocomposites were investigated by X-ray diffraction (XRD), Fourier-transformed infrared spectroscopy (FTIR), atomic force microscopy (AFM), field-emission scanning electron microscopy (FESEM), UV–visible spectroscopy, and photoluminescence (PL) spectroscopy, respectively. The PL spectroscopy data verified the favorable in vitro luminescent emission of the HA/Alg/CQDs nanocomposites in comparison with HA/Alg and HA samples. The XRD patterns of the prepared samples confirmed the formation of crystalline HA in all composites, possessing a Ca/P ratio around 1.5 as obtained by EDX elemental analysis. The FESEM analysis exhibited HA nanoplates that homogeneously distributed throughout the alginate matrix. Therefore, the synthesized nanocomposites could be regarded as potential trackable drug carriers for hard tissue engineering applications.     

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.     

A Novel Magnetic Hardening Mechanism for Nd-Fe-B Permanent Magnets Based on Solid-State Phase Transformation

Permanent magnets based on neodymium-iron-boron (Nd-Fe-B) alloys provide the highest performance and energy density, finding usage in many high-tech applications. Their magnetic performance relies on the intrinsic properties of the hard-magnetic Nd₂Fe₁₄B phase combined with control over the microstructure during production. In this study, a novel magnetic hardening mechanism is described in such materials based on a solid-state phase transformation. Using modified Nd-Fe-B alloys of the type Nd₁₆Fe_bal-x-y-zCo_xMo_yCu_zB₇ for the first time it is revealed how the microstructural transformation from the metastable Nd₂Fe₁₇B_x phase to the hard-magnetic Nd₂Fe₁₄B phase can be thermally controlled, leading to an astonishing increase in coercivity from ≈200 kAm⁻¹ to almost 700 kAm⁻¹. Furthermore, after thermally treating a quenched sample of Nd₁₆Fe₅₆Co₂₀Mo₂Cu₂B₇, the presence of Mo leads to the formation of fine FeMo₂B₂ precipitates, in the range from micrometers down to a few nanometers. These precipitates are responsible for the refinement of the Nd₂Fe₁₄B grains and so for the high coercivity. This mechanism can be incorporated into existing manufacturing processes and can prove to be applicable to novel fabrication routes for Nd-Fe-B magnets, such as additive manufacturing.     

Nanocrystalline Nd–Fe–B Anisotropic Magnets by Flash Spark Plasma Sintering

Flash spark plasma sintering (flash SPS) is an attractive method to obtain Nd–Fe–B magnets with anisotropic magnetic properties when starting from melt-spun powders. Compared to the benchmark processing route via hot pressing with subsequent die upsetting, flash SPS promises electroplasticity as an additional deformation mechanism and reduced tool wear, while maximizing magnetic properties by tailoring the microstructure—fully dense and high texture. A detailed parameter study is conducted to understand the influence of Flash SPS parameters on the densification and magnetic properties of commercial MQU-F powder. It is revealed that the presintering conditions and preheating temperature before applying the power pulse play a major role for tailoring grain size and texture in the case of hot deformation via Flash SPS. Detailed microstructure and magnetic domain evaluation disclose the texture enhancement with increasing flash SPS temperature at the expense of coercivity. The best compromise between remanence and coercivity (1.37 T and 1195 kA m⁻¹, respectively) is achieved through a combination of presintering at 500 °C for 120 s and preheating temperature of 600 °C, resulting in a magnet with energy product (BH)_max of 350 kJm⁻³. These findings show the potential of flash SPS to obtain fully dense anisotropic nanocrystalline magnets with high magnetic performance.     

Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.