Environmental damage costs in Iran by the energy sector

On the basis of the energy supply and demand, this paper assesses the environmental damage from air pollution in Iran using the Extern-E study that has extended over 10 years and is still in progress in the European Union (EU) commission. Damage costs were transferred from Western European practice to the conditions of Iran by scaling according to GDP per capital measured in PPP terms.     

Converting Daily Rainfall Data to Sub-daily—Introducing the MIMD Method

One of the most important analysis in many hydrological and agricultural studies is to convert the daily rainfall data into sub-daily (hourly) because in many rainfall stations, only the daily rainfall data are available and for a comprehensive rainfall analysis, these data should be converted to sub-daily. Many experimental and analytical methods are available for this conversion but one of the simplest yet accurate ones has been proposed by the Indian Meteorological Department (IMD). Since the IMD method has shown low accuracy in some regions, in this study, the IMD method is modified to a single parameter equation, called Modified Indian Meteorological Department (MIMD) in order to improve the accuracy of the conversion. For this reason, the parameter is calibrated so that the maximum correlation between observed and estimated values is achieved. Five stations in different regions with different climatic conditions were selected so that the daily and sub-daily rainfall data were available in each of them. Then, the parameter of the MIMD method was derived for each station. The results were compared with both observed data and IMD method and it was shown that the mean correlation coefficient of MIMD and IMD methods were 0.9 and 0.73 respectively for 12-h rainfall depth which indicated that the accuracy of the MIMD method in estimation of sub-daily rainfall depths was significantly increased. Moreover, the results showed that the accuracy of the MIMD method decreases as rainfall duration decreases.

     

Ignite-GPU: a GPU-enabled in-memory computing architecture on clusters

The Journal of Supercomputing - During recent years, big data explosion and the increase in main memory capacity, on the one hand, and the need for faster data processing, on the other hand, have...     

Efficient designs of reversible sequential circuits

The Journal of Supercomputing - Recently, the synthesis of reversible sequential circuits has attracted researchers’ attention for implementing low-power logic designs. So far, the direct and...     

Extremal optimization vs. learning automata: Strategies for spin selection in portfolio selection problems

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.     

On problem transformability in VLSI

The two basic performance parameters that capture the complexity of any VLSI chip are the area of the chip,A, and the computation time,T. A systematic approach for establishing lower bounds onA is presented. This approach relatesA to the bisection flow, . A theory of problem transformation based on , which captures bothAT ² andA complexity, is developed. A fundamental problem, namely, element uniqueness, is chosen as a computational prototype. It is shown under general input/output protocol assumptions that any chip that decides ifn elements (each with (1+)lognbits) are unique must have =(nlogn), and thus, AT²=(n ²log² n), andA= (nlogn). A theory of VLSI transformability reveals the inherentAT ² andA complexity of a large class of related problems.This work was supported in part by the Semiconductor Research Corporation under contract RSCH 84-06-049-6.     

Improving the Pressurized Flushing Efficiency in Reservoirs: an Experimental Study

Sediment flushing in many reservoirs of the world is accomplished with low efficiency. In this study, a new configuration was proposed for reservoir bottom outlet to increase the pressurized flushing efficiency. In the new configuration, a projecting semi-circular structure was connected to the upstream edge of bottom outlet. It was observed that by employing the projecting bottom outlet, the sediment removal efficiency increased significantly compared to the flushing via typical bottom outlet. In the case of new-configuration bottom outlet with L _sc /D _outlet  = 5.26 and D _sc /D _outlet  = 1.32, the dimensionless length, width and depth of flushing cone increased 280%, 45% and 14%, respectively, compared to the reference test. The proposed structure can ensure the sustainable use of reservoirs.     

Effect of Addition of Ni metal catalyst onto the Co and Fe supported catalysts for the formation of carbon nanotubes

Production of novel porous material is a major target in current material science research due to its wide applications. As carbon nanotube (CNTs) is a one dimensional hollow structure it is also one of the promising materials in applications ranging from electronics to hydrogen storage medium. Catalytic chemical vapor deposition (CCVD) is a method whereby CNTs can be produced in large amount. Thus, in this work, we have synthesized CNTs via pyrolysis of acetylene using various supported transition-metal catalysts in a fixed-bed reactor. Scanning electron microscope (SEM) and transmission electron microscope (TEM) were used to investigate the CNTs structure. The structures of nanotubes formed by acetylene pyrolysis were dependent on the catalysts used. It was found that alumina supported Ni/Fe catalyst inhibited the formation of CNTs growth while alumina supported Ni/Co catalyst gave high density of CNTs. However, nanotubes grown over alumina supported Ni/Fe catalyst were less dense due to the deactivation of the catalyst at the early stage of the pyrolysis process.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

Preparation of new poly(amide–imide)s with chiral architectures via direct polyamidation reaction

N‐trimellitylimido‐L ‐methionine ( 3 ) was prepared by reaction of trimellitic anhydride ( 1 ) with ‐L ‐methionine ( 2 ) in acetic acid solution at refluxing temperature. This diacid was reacted with thionyl chloride, and N‐trimellitylimido‐L ‐methionine diacid chloride ( 4 ) was obtained in quantitative yield. The resulting diacid chloride was reacted with p‐aminobenzoic acid in dry acetone and bis(p‐aminobenzoic acid)‐N‐trimellitylimido‐L ‐methionine ( 5 ) was obtained as a novel optically active amide–imide diacid monomer in high yield. The direct polycondensation of amide–imide diacid monomer 5 with several aromatic diamines was carried out with tosyl chloride (TsCl)/pyridine (Py)/dimetheylformamide (DMF) system. The resulting thermally stable poly(amide–imide)s (PAIs) were obtained in good yields and inherent viscosities ranging between 0.24 and 0.46 dL g^?1 and were characterized with FTIR, ¹H NMR, CHN, and TGA techniques. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 1248–1254, 2007