Social collaborative filtering using local dynamic overlapping community detection

The Journal of Supercomputing - Recommender systems play an important role in dealing with the problems caused by the great and growing amount of information, and the collaborative filtering method...     

Photocatalytic degradation of methyl tert‐butyl ether (MTBE) in contaminated water by ZnO nanoparticles

BACKGROUND: Over the past several decades methyl tert‐butyl ether (MTBE) as additive to gasoline, intended to either boost ratings of fuel or to reduce air pollution, has been accepted worldwide. Since MTBE has high water solubility, the occurrence of fuel spills or leaks from underground storage tanks or transferring pipeline has led to the contamination of natural waters. In this study the degradation of aqueous MTBE at relatively high concentrations was investigated by a UV‐visible/ZnO/H₂O₂ photocatalytic process. The effects of important operational parameters such as pH, amount of H₂O₂, catalyst loading and irradiation time were also investigated. Concentration of MTBE and intermediates such as tert‐butyl formate and tert‐butyl alcohol were measured. RESULTS: Time required for complete degradation increased from 20 to 150 min when the initial concentration was increased from 10 to 500 mg L^?1. The first‐order rate constants for degradation of MTBE were estimated to be 0.183–0.022 min^?1 as the concentration increased from 10 to 500 mg L^?1. Study of the overall mineralization monitored by total organic carbon analysis showed that at an initial concentration of 100 mg L^?1 MTBE complete mineralization was obtained after 100 min under UV‐visible/ZnO/H₂O₂ photocatalysis. CONCLUSION: The data presented in this paper clearly indicated that UV‐visible/ZnO/O₂ as an advanced oxidation process provides an efficient treatment alternative for the remediation of MTBE‐contaminated waters. Copyright © 2008 Society of Chemical Industry     

Multi-criteria Decision-making Approach for Environmental Impact Assessment to Reduce the Adverse Effects Of Dams

Water Resources Management - Environmental Impact Assessments (EIAs) of development projects are necessary to minimize negative impacts and maximize benefits. The objective of this paper is to...     

The Shape Boltzmann Machine: A Strong Model of Object Shape

A good model of object shape is essential in applications such as segmentation, detection, inpainting and graphics. For example, when performing segmentation, local constraints on the shapes can help where object boundaries are noisy or unclear, and global constraints can resolve ambiguities where background clutter looks similar to parts of the objects. In general, the stronger the model of shape, the more performance is improved. In this paper, we use a type of deep Boltzmann machine (Salakhutdinov and Hinton, International Conference on Artificial Intelligence and Statistics, 2009) that we call a Shape Boltzmann Machine (SBM) for the task of modeling foreground/background (binary) and parts-based (categorical) shape images. We show that the SBM characterizes a strong model of shape, in that samples from the model look realistic and it can generalize to generate samples that differ from training examples. We find that the SBM learns distributions that are qualitatively and quantitatively better than existing models for this task.     

Modification of glassy carbon electrode with multi-walled carbon nanotubes and iron(III)-porphyrin film: Application to chlorate, bromate and iodate detection

In this study, multi-wall carbon nanotubes (MWCTs) is evaluated as a transducer, stabilizer and immobilization matrix for the construction of amperometric sensor based on iron-porphyrin. 5,10,15,20-Tetraphenyl-21H,23H-porphine iron(III) chloride (Fe(III)P) adsorbed on MWCNTs immobilized on the surface of glassy carbon electrode. Cyclic voltammograms of the Fe(III)P-incorporated-MWCNTs indicate a pair of well-defined and nearly reversible redox couple with surface confined characteristics at wide pH range (2-12). The surface coverage (Γ) and charge transfer rate constant (k_s) of Fe(III)P immobilized on MWCNTs were 7.68 × 10⁻⁹ mol cm⁻² and 1.8 s⁻¹, respectively, indicating high loading ability of MWCNTs for Fe(III)P and great facilitation of the electron transfer between Fe(III)P and carbon nanotubes immobilized on the electrode surface. Modified electrodes exhibit excellent electrocatalytic activity toward reduction of ClO₃⁻, IO₃⁻ and BrO₃⁻ in acidic solutions. The catalytic rate constants for catalytic reduction of bromate, chlorate and iodate were 6.8 × 10³, 7.4 × 10³ and 4.8 × 10² M⁻¹ s⁻¹, respectively. The hydrodynamic amperometry of rotating-modified electrode at constant potential versus reference electrode was used for detection of bromate, chlorate and iodate. The detection limit, linear calibration range and sensitivity for chlorate, bromate and iodate detections were 0.5 μM, 2 μM to 1 mM, 8.4 nA/μM, 0.6 μM, 2 μM to 0.15 mM, 11 nA/μM, and 2.5 μM, 10 μM to 4 mM and 1.5 nA/μM, respectively. Excellent electrochemical reversibility of the redox couple, good reproducibility, high stability, low detection limit, long life time, fast amperometric response time, wide linear concentration range, technical simplicity and possibility of rapid preparation are great advantages of this sensor. The obtained results show promising practical application of the Fe(III)P-MWCNTs-modified electrode as an amperometric sensor for chlorate, iodate and bromate detections.     

TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.

     

A new homotopy perturbation method for solving systems of partial differential equations

In this paper, a new homotopy perturbation method (NHPM) is introduced for obtaining solutions of systems of non-linear partial differential equations. Theoretical considerations are discussed. To illustrate the capability and reliability of the method three examples are provided. Comparison of the results of applying NHPM with those of applying HPM reveal the effectiveness and convenience of the new technique.     

A computational method of forecasting based on high-order fuzzy time series

This paper presents a computational method of forecasting based on high-order fuzzy time series. The developed computational method provides a better approach to overcome the drawback of existing high-order fuzzy time series models. Its simplicity lies with the use of differences in consecutive values of various orders as forecasting parameter and a w-step fuzzy predictor in place of complicated computations of fuzzy logical relations. The objective of the present study is to examine the suitability of various high-order fuzzy time series models in forecasting. The general suitability of the developed method has been tested by implementing it in the forecasting of student enrollments of the University of Alabama and in the forecasting of crop (Lahi) production, a case of high uncertainty in time series data. The results obtained have been compared in terms of average error of forecast to show superiority of the proposed model.     

Effect of galvanic contact on the flotability of galena in the presence and absence of a collector

In this paper, the effect of various electrochemical environments in the galena flotation is investigated. The electrochemical environments consisted of a ball mill for grinding of galena ore and a Denver flotation cell for flotation of galena in the laboratory scale. In order to achieve the maximum recovery with sodium hyposulfite, the concentrations of 0.06 and 0.12 M of air and nitrogen gases have been used to control the pulp potential in the Denver flotation cell. The galena sample was from the “Era mine” which is located in the Kiyasar area, north of Iran. This mine contains: Galena (PbS) at least 22%, Fluorite (CaF₂) 73.37%, Quartz (SiO₂) 2.54% and other minerals such as Cerussite (PbCO₃) and Kaolinite (Al₂Si₂O₅(OH)₄). Flotation of Galena was conducted in a 0.12 M of sodium hyposulfite solution. It was found that the amount of recovery by this process was 85% and 63% in the presence and absence of potassium amyl xanthate (KAX) and at an estimated potential of 280 to 350 mV respectively while the recovery was 70% and 52% at an approximate potential of 175 to 210 mV in water in the presence and absence of collector (KAX).