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recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm

International Journal of mining Science and Technology 2013年第1期23卷 69-77页

作者： F. Nakhaei m.r. mosavi A. Sam Department of mining Engineering Shahid Bahonar Kerman University Kerman Iran Department of Electrical Engineering Iran University of Science and Technology Tehran Iran

Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and mo grades and recoveries, respectively. The correlation coefficient (r) values for the testing sets for Cu and mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results disc

关键词： Artificial neural network Genetic algorithm Flotation column Grade recovery Prediction

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Com.r.hensive analysis of the role of ubiquitin-specific peptidases in colorectal cancer:A systematic review

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World Journal of Gastrointestinal Oncology 2024年第1期16卷 197-213页

作者： Eman Al-Balushi Amina Al m.r.ouqi Shima Tavoosi Am.r.Hossein Baghsheikhi Arash Sadri Leyla Sharifi Aliabadi mohammad-mahdi Salarabedi Syed Azizur rahman Nabeel Al-Yateem Alireza mosavi Jarrahi Aram Halimi mohammad Ahmadvand Wael m Abdel-rahman College of Health Sciences University of SharjahSharjah 27272United Arab Emirates Department of Biology Faculty of SciencesUniversity of IsfahanIsfahan 81746-73441Iran Department of Biology Science and Research BranchIslamic Azad UniversityTehran 11365/4435Iran Students’Scientific research Center Tehran University of Medical SciencesTehran ***Iran Cell Therapy and Hematopoietic Stem Cell Transplantation research Center Research Institute for OncologyHematologyand Cell TherapyTehran University of Medical SciencesTehran ***Iran Student research Committee School of MedicineShahid Beheshti University of Medical SciencesTehran ***Iran Department of Nursing University of SharjahSharjah 27272United Arab Emirates Cancer research Centre Shahid Beheshti University of Medical SciencesTehran ***Iran Department of medical Laboratory Sciences University of SharjahSharjah 27272United Arab Emirates

BACKGrOUND Colorectal cancer(CrC)is the third most frequent and the second most fatal *** search for m.r. effective drugs to treat this disease is ongoing.A better understanding of the mechanisms of CrC development and progression may reveal new therapeutic ***-specific peptidases(USPs),the largest group of the deubiquitinase protein family,have long been implicated in various *** have been num.r.us studies on the role of USPs in CrC;however,a com.r.hensive view of this role is *** To provide a systematic review of the studies investigating the roles and functions of USPs in *** We systematically queried the mEDLINE(via Pubmed),Scopus,and Web of Science *** Our study highlights the pivotal role of various USPs in several processes implicated in CrC:regulation of the cell cycle,apoptosis,cancer stemness,epithelial–mesenchymal transition,metastasis,DNA repair,and drug *** findings of this study suggest that USPs have great potential as drug targets and noninvasive biom.r.ers in *** dysregulation of USPs in CrC contributes to drug resistance through multiple *** Targeting specific USPs involved in drug resistance pathways could provide a novel therapeutic strategy for overcoming resistance to current treatment regimens in CrC.

关键词： Ubiquitin-specific peptidases Colorectal cancer Deubiquitinase protein family Drug target discovery Biom.r.er discovery

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Dynamic Analysis of Non-Symmetric Functionally Graded (FG) Cylindrical Structure under Shock Loading by radial Shape Function Using meshless Local Petrov-Galerkin (mLPG) method with Nonlinear Grading Patterns

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Computer modeling in Engineering & Sciences 2017年第4期113卷 497-520页

作者： Y.Sadeghi Ferezghi m.r.Sohrabi S.m mosavi Nezhad Civil Engineering Department Faculty of EngineeringUniversity of Sistan and BaluchestanZahedanIran Civil Engineering Department Faculty of EngineeringUniversity of BirjandBirjandIran

In this paper,dynamic behavior of non-symmetric Functionally Graded(FG)cylindrical structure under shock loading is carried *** equations in the polar coordinates are drawn out using meshless Local Petrov-Galerkin(mLPG)*** volume fractions are used for radial direction to simulate the mechanical properties of Functionally Graded material(FGm).To solve dynamic equations of nonsymmetric FG cylindrical structure in the time domain,the mLPG method are combined with the Laplace transform *** computing the inverse Laplace transform in the present paper,the Talbot algorithm for the num.r.cal inversion is *** verify the obtained results by the mLPG method,these results are compared with the analytical solution and the Finite Element method(FEm).The obtained results through the mLPG method show a good agreement in comparison to other results and the mLPG method has high accuracy for dynamic analysis of non-symmetric FG cylindrical *** capability of the present method to dynamic analysis of non-symmetric FG cylindrical structure is demonstrated by dynamic analysis of the cylinder with different volume fraction exponents under harmonic and rectangular shock *** present method shows high accuracy,efficiency and capability to dynamic analysis of non-symmetric FG cylindrical structure with nonlinear grading patterns,which furnishes a ground for a m.r. flexible design.

关键词： mLPG cylindrical structure FGm radial basis functions volume fraction

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The Photon Polarization Parameter of 2H（n, γ）3H reaction with Inclusion of the Electric Quadrupole Contribution

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Chinese Physics Letters 2014年第9期31卷 32-35页

作者： H. Sadeghi m. mosavi-Khansari Department of Physics Faculty of Science Arak University Arak 8349-8-38156 Iran

We use effective field theory （EFT） for the calculation of neutron-deuteron radiative capture at very low energies. We present here the use of EFT to calculate a low-energy photo-nuclear observable in three-body systems, the photon polarization parameter and and fore-aft asymmetry at thermal neutron energies up to next-to-next to leading order （N2 LO）, with inclasion of the electric quadrupole contribution. The photon polarization parameter in total is found to be rc = -0.421±0.003 and is in good agreement with the other modern theoretical calculations based on modern nucleon-nucleon potentials. In comparison with our previous work, a satisfactory agreement with the available experimental data is found by inclusion of the electric quadrupole contribution.

关键词： Polarization

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mechanical and microstructural Properties of Ultra-fine Grained AZ91magnesium Alloy Tubes Processed via multi Pass Tubular Channel Angular Pressing(TCAP)

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Journal of materials Science & Technology 2014年第2期30卷 134-138页

作者： G.Faraji P.Yavari S.Aghdamifar m.mosavi mashhadi Department of mechanical Engineering University College of EngineeringUniversity of Tehran Department of mechanical and Aerospace Engineering Science and Research BranchIslamic Azad University

Ultra-fine grained （UFG） cylindrical tubes were produced via recently developed tubular channel angular pressing （TCAP） process through different passes from as-cast AZ91 magnesium alloy. The microstructure and mechanical properties of processed tube through one to four passes of TCAP process at 200℃ were investigated. microhardness of the processed tube was increased to 98.5 HV after one pass from an initial value of 67 Hr. An increase in the number of passes from one to higher number of passes has no m.r. effect on the microhardness. Yield and ultimate strengths were increased by 4.3 and 1.4 times compared to those in as-cast condition. Notable increase in the strength was achieved after one pass of TCAP while higher number of passes has no m.r. effect. microstructural investigation shows notable decrease in the grain size to around 500 nm from the prim.r. value of - 150 μm. Dissolution and distribution of hard mglTAI12 phase in the grain boundaries of dynamically recrystallized UFG AZ91 with a mean grain size of - 500 nm was an interesting issue of TCAP processing at 200 ℃ compared to other severe plastic deformation processes.

关键词： Severe plastic deformation Tubular channel angular pressing AZ91 Grain refinement mechanical behaviour

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Interevent times estimation of major and continuous earthquakes in Hormozgan region based on radial basis function neural network

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Geodesy and Geodynamics 2016年第1期7卷 64-75页

作者： m.r.mosavi m.Kavei m.Shabani Y.Hatem Khani Department of Electrical Engineering Iran University of Science and Technology Department of Physics University of Hormozgan Department of Electrical Engineering University of Imam Khomeini Marine Sciences

This paper presents a new method to estimate the time of important earthquakes in Hormozgan region with magnitude greater than 5.5 based on the radial Basis Function （rBF） Neural Network （NN） models. Input vector to the network is composed of different seisrnicity rates between main events that are calculated in convenient and reliable way to create optimized training methods. It helps network with a limited number of training data to estimation. It is common for earthquakes modeling by data-driven methods in this case. In addition, the proposed method is combined with rosenberg cluster method to remove aftershocks events from the history of catalog for NN to better process the data. The results show that created rBF model successfully estimates the interevent times between large and sequence earthquakes that can be used as a tool to predict earthquake, so that comparison with other NN structure, for example multi- Layer Perceptron （mLP） NN, reveals the superiority of the proposed method. Because of superiority proposed method has higher accuracy, lower costs and simpler network structure.

关键词： Interevent timesradial basis functionNeural networksEarthquakes

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Im.r.ving the target position detection in the crossed array trackers seeker using the pulses number distribution in the FOV

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红外与毫米波学报 2014年第5期33卷 451-459页

作者： A.r.Yrfanean m.r.mosavi A.mohammadi S.Y.Alchekh Yasin Department of Electrical Engineering Malek Ashtar University of Technology Department of Electrical Engineering Iran University of Science and Technology

Seeking in a field of view(FOV) is influenced by the existence of jamm.r.,noise,shine background or flying *** these factors may push the target out of the FOV and cause missing the *** all the seekers the FOV is not fully exploited which means the target can be missed before becoming out of the FOV,this results of the nonlinearity of the reticle *** this paper,a novel method of the target position detection a crossed four slits or crossed array trackers(CAT) seeker will be designed,simulated and *** idea of this method depends on dividing the FOV into main regions up to a certain parameter,which is the pulses number;then,each main region will be divided into sub-regions up to a second parameter which will be the pulses distribution a spin *** errors sources will be discussed and *** new idea will be applied which is exploiting some area of the FOV where a part of the position data is missed in the information signal by pushing the target to the region where the information signal carries the total position data.

关键词：红外技术毫米波学光学技术仪器

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Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

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Frontiers of Structural and Civil Engineering 2020年第5期14卷 1097-1109页

作者： Zohreh SHEIKH KHOZANI Khabat KHOSrAVI mohammadamin TOrABI Am.r.mosavi Bahram.r.ZAEI Timon rABCZUK Institute of Structural mechanics Bauhaus Universität-WeimarWeimar D-99423Germany Department of Watershed management Engineering Faculty of Natural ResourcesSari Agricultural Science and Natural Resources UniversitySari 48181Iran Department of Civil and Environmental Engineering Idaho State UniversityPocatelloID 83209USA School of the Built Environment Oxford Brookes UniversityOxford OX30BPUK Kando Kalman Faculty of Electrical Engineering Obuda UniversityBudapest 1034Hungary Department of Civil Engineering Bu-Ali Sina UniversityHamedan 65178Iran

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable *** this study,at first,a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound *** shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions.A set of,data mining and machine learning algorithms including random Forest(rF),m5P,random Committee,KStar and Additive regression implemented on attained data to predict the shear stress distribution in the compound *** indicated among these five models;rF method indicated the most precise results with the highest r2 value of ***,the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method(SKm)and Shannon method to acquire the proposed model functioning in predicting the shear stress *** results showed that the rF model has the best prediction performance compared to SKm and Shannon models.

关键词： compound channel machine learning SKm model shear stress distribution data mining models

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modeling of GPS SPS Timing Error using multilayered Neural Network

Modeling of GPS SPS Timing Error using Multilayered Neural N...

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第八届国际信号处理国际会议

作者： m.r.mosavi Department of Electrical Engineering Behshahr University of Science and TechnologyBehshahr 48518-78413Iran

＜正＞GPS is not only an accurate navigation system;it also delivers time with unprecedented accuracy. In this paper, a multilayered Neural Network （mNN） based approach for forecast and im.r.vement of GPS Standard Positioning Service （SPS） timing error is presented. The proposed mNN is trained using Back-Propagation （BP） and Extended Kalman Filter （EKF） training algorithms. The performance of these proposed mNNs is demonstrated by showing its effectiveness in GPS timing error prediction of a low cost GPS receiver. The tests results on the collected real data show that GPS timing error rmS can reduce from 300nsec and 200nsec to less than 120nsec and 43nsec by using mNN prediction, before and after SA, respectively. The experimental results emphasize that performance of mNN based on the EKF training algorithm is better than BP.

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Performance Im.r.vement of GPS GDOP Approximation Using recurrent Wavelet Neural Network

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Journal of Geographic Information System 2011年第4期3卷 318-322页

作者： S. Tafazoli m. r. mosavi 不详

One of the most important factors affecting the precision of the performance of a GPS receiver is the relative positioning of satellites to each other. Therefore, it is essential to choose appropriate accessible satellites utilized in the calculation of GPS positions. Optimal subsets of satellites are determined using the least value of their Geometric Dilution of Precision (GDOP). The most correct method of calculating GPS GDOP uses inverse matrix for all combinations and selecting the lowest ones. However, the inverse matrix method, especially when there are so many satellites, imposes a huge calculation load on the processor of the GPS navigator. In this paper, the rapid and precise calculation of GPS GDOP based on recurrent Wavelet Neural Network (rWNN) has been introduced for selecting an optimal subset of satellites. The method of NNs provides a realistic calculation approach to determine GPS GDOP without any need to calculate inverse matrix.

关键词： rapid and Precise Calculation GPS GDOP rWNN

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