Preparation and characterization of PET/clay nanocomposites by melt compounding

Poly(ethylene terephthalate) (PET) nanocomposites were prepared via melt compounding using a twin‐screw extruder at 265°C. Three different types of organomodified clay were melt compounded with PET: a commercial ammonium‐modified silicate clay (Cloisite 30B) and specially prepared thermally stable phosphonium‐ and imidazolium‐modified montmorillonites. X‐ray diffraction, scanning electron microscopy, transmission electron microscopy (TEM), and thermogravimetric analysis were used to characterize and evaluate the quality of the nanocomposites. To obtain quantitative evaluation of the dispersion level in nanocomposites, statistical analysis of TEM micrographs was performed using a dispersion parameter, D_0.1, based on free‐path spacing measurements. The results showed that the ammonium surfactant yielded the best intercalation results in nanocomposites. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers     

Thermal modeling of gas engine driven air to water heat pump systems in heating mode using genetic algorithm and Artificial Neural Network methods

The gas-engine driven air-to-water heat pump, type air conditioning system, is composed of two major thermodynamic cycles (including the vapor compression refrigeration cycle and the internal combustion gas engine cycle) as well as a refrigerant-water plate heat exchanger. The thermal modeling of gas engine driven air-to-water heat pump system with engine heat recovery heat exchangers was performed here for the heating mode of operation (in which it was required to model engine heat recovery heat exchanger). The modeling was performed using typical thermodynamic characteristics of system components, Artificial Neural Network and the multi-objective genetic algorithm optimization method. The comparison of modeling results with experimental ones showed average differences of 5.08%, 5.93%, 5.21%, 2.88% and 6.2% which shows acceptable agreement for operating pressure, gas engine fuel consumption, outlet water temperature, engine rotational speed, and system primary energy ratio.     

A systematic review on the effectiveness of thermography in diagnosis of diseases

Collecting and analyzing appropriate information and performing comprehensive systematic studies, considering safety, effectiveness, and cost effectiveness of the technologies are prerequisites for making decisions on buying and using different diagnostic and therapeutic equipment. This study aimed to systematically identify and analyze available evidences related to the effectiveness of contact thermography technique in diagnosis of different diseases. This study was a systematic review of published and gray literature. We searched relevant databases, bibliography of related papers, and companies' websites, using appropriate search strategies and key words. The CASP tool was used by two experts to evaluate the quality of retrieved papers and inconsistencies were resolved by discussion. After removal of duplicate citations, 308 titles were identified through database searching, among which 276 were excluded on reviewing of the titles and abstracts. The full texts of the remaining papers (32) were assessed against the inclusion criteria and 14 papers were recognized qualified, which were categorized into three groups of: breast cancer, DVT, and others. The results showed although contact thermography is a safe, rapid and cheap technique to be used in screening and diagnosing different diseases, but results did not show any acceptable diagnostic value in comparison to other diagnostic techniques. It might be beneficial to use it as a complementary technique. More research is recommended in this area. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 188–193, 2013     

Adsorptive removal of petroleum hydrocarbons from aqueous solutions by novel zinc oxide nanoparticles grafted with polymers

Zinc oxide nanoparticles were synthesized and modified by a three-stage method. Elemental analysis, Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning and transmission electron microscopy, and Brunauer–Emmett–Teller method were applied to characterize the nanoparticles. These nanoparticles were evaluated for toluene adsorption from aqueous solutions as a representative of petroleum hydrocarbon removal. The optimum adsorption condition achieved at pH of 6 and contact time of 30 min. The adsorption isotherms were fitted to the Langmuir model. The measured adsorption capacity was 12.8 mg g^?1. This study demonstrated that these nanoparticles could be used as an effective adsorbent for petroleum hydrocarbon removal.     

Effect of Polyamide 66 on the Mechanical and Thermal Properties of Post-Industrial Waste Polyamide 6

Post-industrial waste (PIW) polyamide 6 is successfully used in lieu of commercial virgin polyamide 6, in several automotive applications. The presence of polyamide 66 in the final formulation may affect the mechanical and thermal properties of the PIW polyamide 6 materials. Using unreinforced polyamide 6 from PIW and commercial sources, it was found that the addition of polyamide 66 (below 10 wt.%) lowered the crystallization rate and crystallinity level of all polyamide 6 materials. The thermal and mechanical properties of glass fiber (GF) reinforced PIW polyamide 6 compounds with and without polyamide 66 were also studied. Differential scanning calorimetry (DSC) showed that reinforced materials without polyamide 66 had a higher level of crystallinity. Furthermore, dynamic mechanical analysis (DMA) showed that reinforced compounds without polyamide 66 also had a faster storage modulus buildup immediately after injection molding. Reinforced PIW polyamide 6 compounds without polyamide 66 also exhibited higher tensile and higher vibration weld strengths as well as a thicker heat affected zone (HAZ) than those with polyamide 66, leading to the conclusion that polyamide 66 had a detrimental effect on crystallinity level and consequently on the mechanical properties of GF-reinforced PIW polyamide 6 materials.     

Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions

A novel high-pressure apparatus with various abilities in hydrate investigation fields has been designed, constructed and fully described in the present paper. In order to achieve an appropriate understanding of the gas hydrate behavior in formation and destabilization, series of laboratory experiments with six different gas mixtures were done and more than 130 hydrate equilibrium points in the pressure range of about 450–3000 psia were recorded. Different methods of hydrate formation prediction were discussed and finally the new promising neural networks method was used. Because of the previous works defects in accurate hydrate formation prediction via neural networks, a new use of neural networks was introduced. Testing and validation of the new neural networks method indicates that it is a reliable technique for the accurate prediction of hydrate formation conditions for generalized gas systems and can be used in future automatic inhibitor dosing devices.     

Steady shear flow properties of wild sage (Salvia macrosiphon) seed gum as a function of concentration and temperature

The steady shear flow properties of dispersions of a new potential hydrocolloid, sage seed gum (SSG), were determined as a function of concentration (0.5–2% w/w), and temperature (20–50 °C). SSG dispersions exhibited strong shear-thinning behavior at all conditions tested, which was even more pronounced than commercial hydrocolloids like xanthan, guar gum and locust bean gum. Different time-independent rheological models were used to fit the experimental data, although the Herschel–Bulkley model (H–B) was found the best model to describe steady shear flow behavior of SSG. An increase in gum concentration led to a large increase in yield stress and consistency coefficient values, whereas there was no definite trend with an increase in temperature. On the other hand, the above-mentioned increases in concentration and temperature did not yield a clear evolution of the shear-thinning characteristics of SSG dispersions. An Arrhenius-type model was also used to describe the effect of temperature. The activation energy (E_a) appeared in the range of 3949–16384 J/mol, as concentration increased from 0.5 to 2%, at a shear rate of 100 s⁻¹. The yield stress values estimated by viscoplastic rheological models were much higher than the data determined by stress ramp method. Apparent viscosity of SSG surpassed many commercial hydrocolloids such as guar gum, locust bean gum, Tara gum, fenugreek gum and konjac gum at the same conditions, which suggest it as a very good stabilizer in food formulations.     

从SpectreVerilog到AMS Designer:移植与可用性改良

Virtuoso~AMS Designer(AMSD)包含大量尖端功能,超越了SpectreVerilog等其他解决方案。尽管如此,很多客户仍然使用SpectreVerilog,因为从SpectreVerilog移植到AMSD存在很多障碍和易用性问题。本文将会讨论在PDK转化、设计调整和Verilog文本用法等中间会遇到的障碍以及解决方案。此外,本文还会讨论具体的性能改进以及可用性和可调试性的改进。AMSD流程中加入了新的网表分析器(OSS网表分析器),以及一种新的仿真器流程,使得这些解决方案成为可能。此外AMSD仿真器本身也有很多重大性能改进。本文的最后部分将会详细介绍简单易行的移植过程,并且将会对照SpectreVerilog讨论具体的性能改进。这种新的方法学让客户可以用最低的代价迅速移植到AMSD并享受其丰富的语言支持和功能,从而大大提高设计工程师的生产效率。根据本文中所提到的解决方案,SpectreVerilog移植到AMSD的过程可以从一个月缩短至不到一天。设计工程师的效率也因为可用性、可调试性和性能的提升而得到进一步提升。这些都将在本文中提到,并附带性能的对比。     

Application of the dc-Exponent Method for Abnormal Pressure Detection in Ahwaz Oil Field: A Comparative Study

Abstract

There are different methods for detection of abnormal pressure during drilling; one of these methods is the dc-exponent method. In this research, the applicability of the dc-exponent method is investigated for the detection of abnormal pressure zones in Ahwaz oil field in Iran.

Two procedures exist to calculate the amount of abnormal pressure from dc-exponent data: the Rehm-McClendon and Zamora methods. In some oil fields around the world just one of these methods is applicable and the other one provides incorrect results or is inaccurate. Therefore, in this work both methods are applied to six wells in Ahwaz oil field and a comparison is made between the real pressure (detected on the rig) and the results obtained.     

Task scheduling using NSGA II with fuzzy adaptive operators for computational grids

Scheduling algorithms have an essential role in computational grids for managing jobs, and assigning them to appropriate resources. An efficient task scheduling algorithm can achieve minimum execution time and maximum resource utilization by providing the load balance between resources in the grid. The superiority of genetic algorithm in the scheduling of tasks has been proven in the literature. In this paper, we improve the famous multi-objective genetic algorithm known as NSGA-II using fuzzy operators to improve quality and performance of task scheduling in the market-based grid environment. Load balancing, Makespan and Price are three important objectives for multi-objective optimization in the task scheduling problem in the grid. Grid users do not attend load balancing in making decision, so it is desirable that all solutions have good load balancing. Thus to decrease computation and ease decision making through the users, we should consider and improve the load balancing problem in the task scheduling indirectly using the fuzzy system without implementing the third objective function. We have used fuzzy operators for this purpose and more quality and variety in Pareto-optimal solutions. Three functions are defined to generate inputs for fuzzy systems. Variance of costs, variance of frequency of involved resources in scheduling and variance of genes values are used to determine probabilities of crossover and mutation intelligently. Variance of frequency of involved resources with cooperation of Makespan objective satisfies load balancing objective indirectly. Variance of genes values and variance of costs are used in the mutation fuzzy system to improve diversity and quality of Pareto optimal front. Our method conducts the algorithm towards best and most appropriate solutions with load balancing in less iteration. The obtained results have proved that our innovative algorithm converges to Pareto-optimal solutions faster and with more quality.