Nonflat surface level pyramid: a high connectivity multidimensional interconnection network

Parallel machines are extensively used to increase computational speed in solving different scientific problems. Various topologies with different properties have been proposed so far and each one is suitable for specific applications. Pyramid interconnection networks have potentially powerful architecture for many applications such as image processing, visualization, and data mining. The major advantage of pyramids which is important for image processing systems is hierarchical abstracting and transferring the data toward the apex node, just like the human being vision system, which reach to an object from an image. There are rapidly growing applications in which the multidimensional datasets should be processed simultaneously. For such a system, we need a symmetric and expandable interconnection network to process data from different directions and forward them toward the apex. In this paper, a new type of pyramid interconnection network called Non-Flat Surface Level (NFSL) pyramid is proposed. NFSL pyramid interconnection networks constructed by L-level A-lateral-base pyramids that are named basic-pyramids. So, the apex node is surrounded by the level-one surfaces of NFSL that are the first nearest level of nodes to apex in the basic pyramids. Two topologies which are called NFSL-T and NFSL-Q originated from Trilateral-base and Quadrilateral-base basic-pyramids are studied to exemplify the proposed structure. To evaluate the proposed architecture, the most important properties of the networks are determined and compared with those of the standard pyramid networks and its variants.     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

Boosting performance of transactional memory through O-GEHL predictors

Time-based Software Transactional Memory (STM) exploits a global clock to validate transactional data and guarantee consistency of transactions. While this method is simple to implement it results in contentions over the clock if transactions commit simultaneously. The alternative method is thread local clock (TLC) which exploits local variables to maintain consistency of transactions. However, TLC may increase false aborts and degrade performance of STMs. In this paper, we analyze global clock and TLC in the context of STM systems, highlighting both the implementation trade-offs and the performance implications of the two techniques. We demonstrate that neither global clock nor TLC is optimum across applications. To counter this challenge, we introduce two optimization techniques: The first optimization technique is Adaptive Clock (AC) which dynamically selects one of the two validation techniques based on probability of conflicts. AC is a speculative approach and relies on software O-GEHL predictors to speculate future conflicts. The second optimization technique is AC+ which reduces timing overhead of O-GEHL predictors by implementing the predictors in hardware. In addition, we exploit information theory to eliminate unnecessary computational resources and reduce storage requirements of the O-GEHL predictors. Our evaluation with TL2 and Stamp benchmark suite reveals that AC is effective and improves execution time of transactional applications up to 65%.     

Study on the performance of Cd2+ sorption using dimethylethylenediamine-modified zinc-based MOF (ZIF-8-mmen): optimization of the process by RSM technique

ABSTRACT

Cadmium as a highly toxic metal is released into the environment through paper production, metal processing, phosphate fertilizers, insecticides, and treatment of wastewater. Cadmium also inhibits the body activities and is very toxic for kidney and other organisms. In the current study, zinc-based metal–organic framework, zeolitic imidazolate framework (ZIF)-8, was synthesized and modified by dimethylethylenediamine (ZIF-8-mmen) for the removal of cadmium. To optimize the experiments, response surface methodology was applied with three variables including pH, adsorbent dosage, and contact time using central composite design. The optimum conditions for pH, dosage, and time were 2, 0.1 g, and 89 min, respectively, with removal efficiency of 85.38%. The Langmuir isotherm (q _m = 1000 mg/g) indicates the monolayer adsorption. The kinetic studies reveal that the Lagergren model was predominant and cadmium was not chemisorbed. Thermodynamic parameters show spontaneous, endothermic, and physisorption processes.     

Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...     

A Multi-Criteria Group Decision Making Methodology Using Interval Type-2 Fuzzy Sets: Application to Water Resources Management

In this paper, a new fuzzy group decision-making methodology which determines and incorporates negotiation powers of decision makers is developed. The proposed method is based on a combination of interval type-2 fuzzy sets and a multi-criteria decision making (MCDM) model, namely TOPSIS. To examine the applicability of the proposed methodology, it is used for finding the best scenario of allocating water and reclaimed wastewater to domestic, agricultural, and industrial water sectors and restoring groundwater quantity and quality in the Varamin region located in Tehran metropolitan area in Iran. The results show that the selected scenario leads to an acceptable groundwater conservation level during a long-term planning horizon. Although the capital cost of this scenario is high, which leads to groundwater restoration during the 34-year planning horizon, it is determined as the best allocation scenario. This scenario also entails the second least pumping cost, due to less water allocation from the groundwater. To evaluate the results of the proposed methodology, they are compared with those obtained using some well-known interval type-2 decision-making approaches including arithmetic-based, TOPSIS-based, and likelihood-based comparison methods. The Spearman correlation coefficient shows that the obtained results generally concur with those of the other methods. It is also concluded that the proposed methodology gives more reasonable results by calculating and considering the negotiation powers of decision makers in an extended TOPSIS-based group decision-making model.

     

Thermo economic evaluation of oxy fuel combustion cycle in Kazeroon power plant considering enhanced oil recovery revenues简

Oxy fuel combustion and conventional cycle （currently working cycle） in Kazeroon plant are modeled using commercial thermodynamic modeling software. Economic evaluation of the two models regarding the resources of transport and injection of carbon dioxide into oil fields at Gachsaran for enhanced oil recovery in the various oil price indices is conducted and indices net present value （NPV） and internal rate of return on investment （IRR） are calculated. The results of the two models reveal that gross efficiency of the oxy fuel cycle is more than reference cycle （62% compared to 49.03%）, but the net efficiency is less （41.85% compared to 47.92%） because of the high-energy consumption of the components, particularly air separation unit （ASU） in the oxy fuel cycle. In this model, pure carbon dioxide with pressure of 20~ 105 Pa and purity of 96.84% was captured. NOx emissions also decrease by 4289.7 tons per year due to separation of nitrogen in ASU. In this model, none of the components of oxy fuel cycle is a major engineering challenge. With increasing oil price, economic justification of oxy fuel combustion model increases. With the price of oil at $ 80 per barrel in mind and $ 31 per ton fines for emissions of carbon dioxide in the atmosphere, IRR is the same for both models.     

Solving singular control problems using uniform trigonometrization method

This study investigates the application of a recently developed construct, the uniform trigonometrization method (UTM), to the singular control problems in chemical engineering. The UTM involves minimal modifications to the original problem, thereby generating near-singular control solutions that can be used for conceptual design and serve as an alternate to direct techniques like nested and simultaneous approaches. Eight classical singular control problems with known analytical solutions and three complex singular control problems from chemical engineering domain are solved in this study. The results obtained using the UTM for these problems are found to match well with the literature and are of higher resolution as compared to the results obtained using a direct pseudospectral-based solver. The ability of the UTM to handle complex chemical engineering problems with both singular controls and state path constraints has also been demonstrated in this study.     

Preparation and characterization of poly(vinyl alcohol)/gum tragacanth/cellulose nanocomposite film

The effects of gum tragacanth obtained from two species of Astragalus Gossypinus (GT-G) and A. Parrowianus (GT-P) at two levels of 10% and 30% combined with cellulose nanofibers (CNF; 5%) on the physico-mechanical and structural properties of polyvinyl alcohol (PVA) nanocomposite film were investigated in this study. The water solubility and water vapor permeability of the films decreased with increasing the content of both gums, especially in the film containing 30% GT-P. The highest values of the tensile strength (39.3 MPa) and elongation at break (445%) belonged to the treatment containing 10% GT-P (90/10P/0). The FTIR and DSC analyses confirmed good interactions between GT and PVA in the 90/10P/0 treatment. SEM images indicated the dense structure of this film as the optimum treatment. Although the presence of CNF in the films containing GT-G improved some properties, especially the Young modulus, it impaired all the functional properties of nanocomposite GT-P film.