MC Model Research Articles

PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE JIGSAW UNTUK MENINGKATKAN HASIL BELAJAR KOGNITIF

Abstract The used of the jigsaw-type cooperative learning model can improve the cognitive learning outcomes of students, especially on ionic equilibrium material in salt solution. This study aims to improve students' cognitive chemistry learning outcomes on ion equilibrium in salt solution by applying the jigsaw-type cooperative learning model. This research is a classroom action research with the Kemmis and MC models. Taggart, which included the stages of planning, implementing, observing / collecting data and reflecting, which were carried out in 2 (two) cycles. This research was conducted in class XI IPA at public high school Christian YABT Manokwari with a sample of 17 students. The data collection techniques used were tests and observation sheets. The results showed that the increase in the average value of learning outcomes chemistry from cycle I was 56.18 with classical completeness of 29% to 85.29 with classical completeness of 82% in cycle II. The results of observations on learning using the jigsaw type cooperative model have good responses from students. This proves that the type of jigsaw cooperative learning has a positive impact on students in the teaching and learning process. Keywords : Jigsaw Model, learning outcomes cognitive, Ion Balance, Salt Solution

A TISM approach for the analysis of enablers in implementing mass customization in Indian manufacturing units

Mass Customization is a generic business strategy for organizational excellence and organizations striving for competitive advantage are incorporating MC into their business model. Though research has been done to identify enablers of mass customization, there has been an associated degree of study in conceptual papers while theory-building articles are less in number. Also, less study has been done in the Indian market scenario. The literature show that organizations are apprehensive of their technological potentials and are not taking initiative to transform themselves from conventional mass manufacturing companies to mass customization for emerging market needs. As a response to this research gap, a systematic literature review is conducted on enablers for MC and Total Interpretive Structural Modelling (TISM) and MICMAC analysis is used to develop a framework that illustrates how enablers are placed at different levels and how a particular enabler influences the other or getting influenced. Supporting this insight, a theory for accomplishing mass customization in manufacturing units is established. The theory is verified from experts by structured questionnaire from three Indian manufacturing companies where Mass customization is being practiced. This paper renders a summary of the methodology and specifies its validation in three case study companies. Practices are suggested to reinforce the attainment of mass customization in organizations aspiring to implement mass customization.

A power optimization model for the long‐term planning scenarios: Case study of Mexico's power system decarbonization

AbstractMexico is committed to reducing its CO2 emissions according to the Paris Agreement. A relevant effort must be made for the analysis of Mexico's electric energy system towards a progressive decarbonization with a larger participation of intermittent renewable energies. The analysis of power planning scenarios, with different assumptions on costs, emissions, and intermittent performance of the power generating technologies, is needed to make sustainable decisions in the transition toward a cleaner power sector. Tools for energy modelling are required to develop and analyze scenarios with minimum costs subject to environmental constraints. The purpose of the article is to explain the modelling approach of a novel and flexible power planning tool, which is based on a well‐known linear programming optimization method combined with a computing strategy to optimize time consumed for reading, processing calculations, and writing the huge number of economic and technical parameters required for the hourly power dispatch in complex interconnected electric systems. The time consumed has been optimized by means of a binary matrix that activates the input and use of only the data needed for the solution of the problem. The paper describes the MC model and demonstrates some of its analytical capabilities through a Mexican case study with a least cost scenario and two decarbonization scenarios of the power interconnected system for the period from 2020‐2050.

Study of surface evolution via Modified Ion Beam Sputtering Semi-Empirical Model (MIBSEM)

An Ion Beam Sputtering Monte Carlo Model (IBSM) was modified to study the effect of different surface diffusion mechanics on the surface height and surface roughness of materials when bombarded with energetic ions. The extension was done by incorporating Seahet al.’s semi-empirical ion sputtering equations into the sputter erosion algorithm of the MC model and by enforcing curvature dependence of sputter yield. Also, TRansport of Ions in Matter (TRIM) software, was used to calculate the sputter yield and the values obtained were compared with those obtained from Seahet al.’s semi-empirical model and the sputter yield obtained from sputtering experiments. Our studies show that the surface diffusion mechanics that occurs during a sputtering process controls the substrate surface height and its roughness while curvature dependence of sputter yield has little or no contribution to the target surface height and its roughness. Our comparison of the calculated sputter yield values of TRIM and Seahet al.’s model with experimental sputter yield values show that Seahet al.’s model gives sputter yield values closer to experimental values than TRIM at a normal angle of incidence while TRIM gives sputter yield values closer to experimental values at 60°angle of incidence.

Stochastic Petri Net Based Modeling of Emergency Medical Rescue Processes during Earthquakes.

The post-disaster emergency medical rescue (EMR) is critical for people’s lives. This paper presents a stochastic Petri net (SPN) model based on the process of the rescue structure and a Markov chain model (MC), which is applied to the optimization of the EMR process, with the aim of identifying the key activities of EMR. An isomorphic MC model is developed for measuring and evaluating the time performance of the EMR process during earthquakes with the data of the 2008 Wenchuan earthquake. This paper provides a mathematical approach to simulate the process and to evaluate the efficiency of EMR. Simultaneously, the expressions of the steady state probabilities of this system under various states are obtained based on the MC, and the variations of the probabilities are analyzed by changing the firing rates for every transition. Based on the concrete data of the event, the authors find the most time consuming and critical activities for EMR decisions. The model results show that the key activities can improve the efficiency of medical rescue, providing decision-makers with rescue strategies during the large scale earthquake.

Multiscale exploration of hydrocarbon adsorption and hopping through ZSM-5 channels - from Monte Carlo modelling to experiment.

In this article the results of statistical MC modelling corroborated by the FT-IR spectroscopy and gravimetric adsorption studies of low aliphatic hydrocarbons in ZSM-5 (Si/Al = 28 or silicalite) are presented. The extension of the existing Dubbeldam's forcefield towards inclusion of the finite aluminium-containing zeolites is proposed and its applicability is verified experimentally for the sorption of linear and branched hydrocarbons. The FT-IR spectroscopy applicability to follow the kinetics of small hydrocarbon adsorption has been successfully verified by the application of the Crank solution for diffusion to spectroscopy derived results.

Evaluating NODDI‐based biomarkers of Alzheimer’s disease

AbstractBackgroundMulti‐compartment (MC) diffusion MRI (dMRI) models are increasingly used to evaluate brain microstructure beyond the classic diffusion tensor model (DTI). The ability to separate the neural tissue into distinct compartments, i.e., intracellular volume fraction (ICVF), extracellular volume fraction (ECVF) and free‐water (FW), offers novel biomarkers for early detection of cognitive decline and Alzheimer’s disease. The most commonly used MC model used is Neurite Orientation Dispersion and Density Imaging (NODDI). Recent evidence shows that the voxel‐wise volume fractions compartment can be wrongly estimated if the same T2 relaxation time is assumed for the gray matter (GM), white matter (WM) and corticospinal fluid (CSF). To more accurately estimate the tissue compartments, here we use a novel method recently proposed by the DMIPY package (https://github.com/AthenaEPI/dmipy), that initially estimates a response function for each brain region (GM, WM and CSF) and uses these to better estimate the ICVF and FW compartments.MethodWe analyzed ADNI‐3 multi‐shell diffusion MRI (dMRI) data to estimate NODDI measures and to assess their ability to predict mild cognitive impairment (MCI). For each measure, we used regularized logistic regression to find cohesive clusters of brain tissue that contribute to correct classification. Data included 34 participants with MCI (mean age: 75.6±7.8yrs, 22M/9F) and 74 cognitively normal individuals (CN; mean age= 74.1±7.1yrs; 25M/39F). Siemens Prisma 3T multi‐shell dMRI data included 13 b0 images, 6 b=500, 48 b=1000, and 60 b=2000 s/mm2 diffusion weighted 32 images (voxel size=2mm, TE=71ms, TR=3300ms, δ=13.6ms, Δ=35ms). We fit three types of NODDI: the classic NODDI model, the NODDI model with a GM tissue response function, and with a WM response function.ResultThe NODDI model with the GM response function increased the accuracy of the logistic regression classification (see Table 1). The highest recall was achieved by the neurite dispersion index (ODI) in all three models.ConclusionAdding specific response functions for the GM and WM in NODDI improved classification accuracies and recall of MCI subjects. The distribution of the classifying regions was different across the three types of NODDI (Figures 1, 2 and 3).

The influence of the soil constitutive models on the seismic analysis of pile-supported wharf structures with batter piles in cut-slope rock dike

Abstract In coastal regions, earthquakes caused severe damage to marine structures. Many researchers have conducted numerical investigations in order to understand the dynamic behavior of these structures. The most frequently used model in numerical calculations of soil is the linear-elastic perfectly plastic model with a Mohr-Coulomb failure criterion (MC model). It is recommended to use this model to represent a first-order approximation of soil behavior. Therefore, it is necessary to accommodate soil constitutive models for the specific geotechnical problems. In this paper, three soil constitutive models with different accuracy were applied by using the two-dimensional finite element software PLAXIS to study the behavior of pile-supported wharf embedded in rock dike, under the 1989 Loma Prieta earthquake. These models are: a linear-elastic perfectly plastic model (MC model), an elastoplastic model with isotropic hardening (HS model), and the Hardening Soil model with an extension to the small-strain stiffness (HSS model). A typical pile-supported wharf structure with batter piles from the western United States ports was selected to perform the study. The wharf included cut-slope (sliver) rock dike configuration, which is constituted by a thin layer of rockfill overlaid by a slope of loose sand. The foundation soil and the backfill soil behind the wharf were all dense sand. The soil parameters used in the study were calibrated in numerical soil element tests (Oedometer and Triaxial tests). The wharf displacement and pore pressure results obtained using models with different accuracy were compared to the numerical results of Heidary-Torkamani et al.[28] It was found that the Hardening Soil model with small-strain stiffness (HSS model) gives clearly better results than the MC and HS models. Afterwards, the pile displacements in sloping rockfill were analyzed. The displacement time histories of the rock dike at the top and at the toe were also exposed. It can be noted that during the earthquake there was a significant lateral ground displacement at the upper part of the embankment due to the liquefaction of loose sand. This movement caused displacement at the dike top greater than its displacement at the toe. Consequently, the behavior of the wharf was affected and the pile displacements were important, specially the piles closest to the dike top.

Adaptive Sum of Markov Chains for Modelling 3D Blockage in mmWave V2I Communications

Blockage attenuation is one of the main challenges that face reliable communication at the millimetre-wave (mmWave) band, especially for dynamic vehicle-to-infrastructure (V2I) communication systems. Modelling the dynamics of the blockage is important for evaluating high gain beamforming techniques, which are used to improve the signal strength in the mmWave communication systems. The novel sum of Markov chains (sum of MC) model is designed to do two main tasks successfully; capturing the dynamics of blockers affecting a moving transceiver and computing the arising channel attenuation. The sum of MC model has advantages over existing Markov chains models, which are: that it can adapt to model non-stationary scenarios. The sum of MC model can integrate any attenuation function, including the 3GPP blockage model and any lab measurement attenuation profile. Additionally, it is computationally efficient. The sum of MC model can match well with the performance results from a more complex geometric channel model.

Implementation of a dose calculation algorithm based on Monte Carlo simulations for treatment planning towards MRI guided ion beam therapy

Magnetic resonance guidance in particle therapy has the potential to improve the current performance of clinical workflows. However, the presence of magnetic fields challenges the current algorithms for treatment planning. To ensure proper dose calculations, compensation methods are required to guarantee that the maximum deposited energy of deflected beams lies in the target volume. In addition, proper modifications of the intrinsic dose calculation engines, accounting for magnetic fields, are needed. In this work, an algorithm for proton treatment planning in magnetic fields was implemented in a research treatment planning system (TPS), matRad. Setup-specific look up tables were generated using a validated MC model for a clinical proton beamline (62.4 – 215.7 MeV) interacting with a dipole magnet (B = 0–1 T). The algorithm was successfully benchmarked against MC simulations in water, showing gamma index (2%/2mm) global pass rates higher than 96% for different plan configurations. Additionally, absorbed depth doses were compared with experimental measurements in water. Differences within 2% and 3.5% in the Bragg peak and entrance regions, respectively, were found. Finally, treatment plans were generated and optimized for magnetic field strengths of 0 and 1 T to assess the performance of the proposed model. Equivalent treatment plans and dose volume histograms were achieved, independently of the magnetic field strength. Differences lower than 1.5% for plan quality indicators (D2%, D50%, D90%, V95% and V105%) in water, a TG119 phantom and an exemplary prostate patient case were obtained. More complex treatment planning studies are foreseen to establish the limits of applicability of the proposed model.

Study of Forward-Backward Multiplicity Fluctuations and Correlations with Pseudorapidity

Multiplicity fluctuations in two separated pseudorapidity intervals are studied in terms of strongly intensive variables. This work presents results obtained in the MC model with quark-gluon strings as objects extended in pseudorapidity space and acting as particle emitting sources. The string interactions are implemented within the discrete model of local string fusion. Strings of fluctuating length in pseudorapidity are considered in order to estimate the non-critical background of fluctuations. The results on the dependence of strongly intensive observables on the width of acceptance windows, on the pseudorapidity separation between them and on the string density qualitatively describe the available data from the NA61/SHINE and ALICE experiments.

Meningkatkan keterampilan berpikir kritis melalui model pembelajaran decision making pada mata pelajaran Pendidikan Pancasila dan Kewarganegaraan

This study aims to improve the critical thinking skills of students of class XI IPA 2 MAN 2 Karawang on PPKn subjects through the Decision Making learning model. The use of this learning model is one of the efforts to improve critical thinking skills of students of class XI IPA 2 MAN 2 Karawang on PPKn subjects. The research method used in this study is the Classroom Action Research (CAR) method, which is research conducted by teachers in their own class with the aim of improving their performance as teachers, so that student learning outcomes become more improved. The PTK model used is the Kemmis & Mc model. Taggart which consists of planning, implementation, observation, and reflection. The instruments used are test results, observation, and interviews. Learning outcomes tests are used to measure students' success in critical thinking skills in the subject of analyzing cases of threats to ideology, politics, economics, socio-culture, defense and security and strategies to overcome them in the frame of Bhineka Tungga Ika). Observation and interview using observation format and interview format. The results showed that the use of the Decision Making learning model can improve students' critical thinking skills in PPKn subjects. From the pre-action class average value 28.76, it becomes 49.41 in the first cycle, 67.53 in the second cycle, and 91.79 in the third cycle..

Monte Carlo kilovoltage X-ray tube simulation: A statistical analysis and compact simulation method

IntroductionMonte Carlo (MC) simulations are a powerful tool for improving image quality in X-ray based imaging modalities. An accurate X-ray source model is essential to MC modeling for CBCT but can be difficult to implement on a GPU while maintaining efficiency and memory limitations. A statistical analysis of the photon distribution from a MC X-ray tube simulation is conducted in hopes of building a compact source model. Materials & methodsMC simulations of an X-ray tube were carried out using BEAMnrc. The resulting photons were sorted into four categories: primary, scatter, off-focal radiation (OFR), and both (scatter and OFR). A statistical analysis of the photon components (energy, position, direction) was completed. A novel method for a compact (memory efficient) representation of the PHSP data was implemented and tested using different statistical based linear transformations (PCA, ZCA, ICA), as well as a geometrical transformation. ResultsThe statistical analysis showed all photon groupings had strong correlations between position and direction, with the largest correlation in the primary data. The novel method was successful in compactly representing the primary (error < 2%) and scatter (error < 6%) photon groupings by reducing the component correlations. Discussion & conclusionStatistical linear transforms provide a method of reducing the memory required to accurately simulate an X-ray source in a GPU MC system. If all photon types are required, the proposed method reduces the memory requirements by 3.8 times. When only primary and scatter data is needed, the memory requirement is reduced from gigabytes to kilobytes.

Predictive model for the quantitative analysis of human skin using photothermal radiometry and diffuse reflectance spectroscopy.

We have recently introduced a novel methodology for the noninvasive analysis of the structure and composition of human skin in vivo. The approach combines pulsed photothermal radiometry (PPTR), involving time-resolved measurements of mid-infrared emission after irradiation with a millisecond light pulse, and diffuse reflectance spectroscopy (DRS) in the visible part of the spectrum. Simultaneous fitting of both data sets with respective predictions from a numerical model of light transport in human skin enables the assessment of the contents of skin chromophores (melanin, oxy-, and deoxy-hemoglobin), as well as scattering properties and thicknesses of the epidermis and dermis. However, the involved iterative optimization of 14 skin model parameters using a numerical forward model (i.e., inverse Monte Carlo - IMC) is computationally very expensive. In order to overcome this drawback, we have constructed a very fast predictive model (PM) based on machine learning. The PM involves random forests, trained on ∼9,000 examples computed using our forward MC model. We show that the performance of such a PM is very satisfying, both in objective testing using cross-validation and in direct comparisons with the IMC procedure. We also present a hybrid approach (HA), which combines the speed of the PM with versatility of the IMC procedure. Compared with the latter, the HA improves both the accuracy and robustness of the inverse analysis, while significantly reducing the computation times.

Physical performance of a long axial field-of-view PET scanner prototype with sparse rings configuration: A Monte Carlo simulation study.

There is a growing interest in extending the axial fields-of-view (AFOV) of PET scanners. One major limitation for the widespread clinical adoption of such systems is the multifold increase in the associated material costs. In this study, we propose a cost-effective solution to extend the PET AFOV using a sparse detector rings configuration. The corresponding physical performance was validated using Monte Carlo simulations. Monte Carlo model of the Siemens BiographTM mCT PET/CT, with a 21.8cm AFOV and a set of compact rings of LSO crystals was developed as a gold standard. The mCT configuration was then modified by interleaving the LSO crystals in the axial direction within each detector block with 4mm physical gaps (equivalent to the LSO crystal axial dimension) thus extending the AFOV to 43.6cm (Ex-mCT). The physical performances of the two MC models were assessed and then compared using NEMA NU 2-2007 standards. Ex-mCT showed <0.2mm difference in transaxial spatial resolution, and, 0.8mm and 0.3mm deterioration in axial spatial resolution, compared to the mCT, at 1 and 10cm off-center of the transaxial field-of-view respectively. The system sensitivities for the mCT and Ex-mCT models were 9.4±0.2 and 10.75±0.2cps/kBq respectively. The higher sensitivity of Ex-mCT was due to four additional detector rings required to double the mCT AFOV. PET images of the NEMA Image Quality (IQ) phantom showed no artifacts due to detector rings sparsity, and all spheres were visible in both configurations. Ex-mCT achieved percent contrast recoveries within 5.6% of those of the mCT for all spheres and a maximum of 36% higher background variability at the center of the AFOV. The Ex-mCT, however, showed a more uniform noise distribution over an axial range of almost twice the length of the mCT AFOV. Using the proposed sparse detector-ring configuration, the AFOV of current generation PET systems can be doubled while maintaining the original number and volume of detector crystal elements, and without jeopardizing the system's overall physical performance. Despite an increase in the noise level, the Ex-mCT exhibited an improved noise uniformity.

Restricted immunological and cellular pathways are shared by murine models of chronic alcohol consumption

Murine models of chronic alcohol consumption are frequently used to investigate alcoholic liver injury and define new therapeutic targets. Lieber-DeCarli diet (LD) and Meadows-Cook diet (MC) are the most accepted models of chronic alcohol consumption. It is unclear how similar these models are at the cellular, immunologic, and transcriptome levels. We investigated the common and specific pathways of LD and MC models. Livers from LD and MC mice were subjected to histologic changes, hepatic leukocyte population, hepatic transcripts level related to leukocyte recruitment, and hepatic RNA-seq analysis. Cross-species comparison was performed using the alcoholic liver disease (ALD) transcriptomic public dataset. Despite LD mice have increased liver injury and steatosis by alcohol exposure, the number of CD45+ cells were reduced. Opposite, MC mice have an increased number of monocytes/liver by alcohol. The pattern of chemokine gradient, adhesion molecules, and cytokine transcripts is highly specific for each model, not shared with advanced human alcoholic liver disease. Moreover, hepatic RNA-seq revealed a limited and restricted number of shared genes differentially changed by alcohol exposure in these 2 models. Thus, mechanisms involved in alcohol tissue injury are model-dependent at multiple levels and raise the consideration of significant pathophysiological diversity of human alcoholic liver injury.

Application of the phase-space distribution approach of Monte Carlo for radiation contamination dose estimation from the (n,γ), (γ,n) nuclear reactions and linac leakage photons in the megavoltage radiotherapy facility.

The aim of this study was to characterize the radiation contamination inside and outside the megavoltage radiotherapy room. Radiation contamination components in the 18MV linac room are the secondary neutron, prompt gamma ray, electron and linac leakage radiation. An 18MV linac modeled in a typical bunker employing the MCNPX code of Monte Carlo. For fast calculation, phase-space distribution (PSD) file modeling was applied and the calculations were conducted for the radiation contamination components dose and spectra at 6 locations inside and outside the bunker. The results showed that the difference of measured and calculated percent depth-dose (PDD) and photo beam-profile (PBP) datasets were lower than acceptable values. At isocenter, the obtained photon dose and neutron fluence were 2.4×10-14 Gy/initial e° and 2.22×10-8 n°/cm2, respectively. Then, neutron apparent source strength (QN) value was found as 1.34×1012 n°/Gy X at isocenter and the model verified to photon and neutron calculations. A surface at 2cm below the flattening filter was modeled as phase-space (PS) file for PDD and PBP calculations. Then by use of a spherical cell in the center of the linac target as a PS surface, contaminant radiations dose, fluence and spectra were estimated at 6 locations in a considerably short time, using the registered history of all particles and photons in the 13GB PSD file as primary source in the second step. Designing the PSD file in MC modeling helps user to solve the problems with complex geometry and physics precisely in a shorter run-time.

PENINGKATAN HASIL BELAJAR FISIKA PESERTA DIDIK MELALUI MELALUI PEMBELAJARAN TEAMS GAMES TOURNAMENT

This study aims to determine the application of cooperative learning models of the Teams Games Tournament (TGT) type on learning to improve student learning outcomes. The subject of this research was the tenth grade students of SMAN 1 Batang Onang. This type of research is Classroom Action Research. The research design used was the Kemmis and Mc model. Taggart which includes the stages of planning, action and observation, and reflection. The data collection instruments used were observation sheets, student activity scale, and LKS. The data analysis technique used is descriptive qualitative and quantitative data analysis. Based on the results of data analysis in the first cycle of meeting I showed an increase in student mathematics learning outcomes. Research Results, namely an increase in student learning outcomes The average grade value when the pre test is only 38.67. The class average value increased by 63 in cycle I. The average grade value in the first cycle still did not meet the KKM set by the school. In the second cycle the average value of the class experienced an increase of 85.33. Classical learning completeness in the first cycle was only 53%. In cycle II classical learning completeness becomes 96%. This shows an increase in students' classical learning completeness by 43%. Keywords: Teams Games Tournament (TGT) Learning Model, Learning Outcomes

Sensitivity analysis of Monte Carlo model of a gantry‐mounted passively scattered proton system

PurposeThis study aimed to present guidance on the correlation between treatment nozzle and proton source parameters, and dose distribution of a passive double scattering compact proton therapy unit, known as Mevion S250.MethodsAll 24 beam options were modeled using the MCNPX MC code. The calculated physical dose for pristine peak, profiles, and spread out Bragg peak (SOBP) were benchmarked with the measured data. Track‐averaged LET (LETt) and dose‐averaged LET (LETd) distributions were also calculated. For the sensitivity investigations, proton beam line parameters including Average Energy (AE), Energy Spread (ES), Spot Size (SS), Beam Angle (BA), Beam Offset (OA), and Second scatter Offset (SO) from central Axis, and also First Scatter (FS) thickness were simulated in different stages to obtain the uncertainty of the derived results on the physical dose and LET distribution in a water phantom.ResultsFor the physical dose distribution, the MCNPX MC model matched measurements data for all the options to within 2 mm and 2% criterion. The Mevion S250 was found to have a LETt between 0.46 and 8.76 keV.μm–1 and a corresponding LETd between 0.84 and 15.91 keV.μm–1. For all the options, the AE and ES had the greatest effect on the resulting depth of pristine peak and peak‐to‐plateau ratio respectively. BA, OA, and SO significantly decreased the flatness and symmetry of the profiles. The LETs were found to be sensitive to the AE, ES, and SS, especially in the peak region.ConclusionsThis study revealed the importance of considering detailed beam parameters, and identifying those that resulted in large effects on the physical dose distribution and LETs for a compact proton therapy machine.

Modelling of Urban Growth and Planning: A Critical Review

For thousands of years, cities have been the center of civilization. According to that, detecting, monitoring and controlling urban growth became the most urgent need in urban planning and urban development process to get the expected results that can build a concrete base for decision makers to drive the polices toward best track. The issue of this paper is about urban growth and planning models and techniques such as geographic information system (GIS), cellular automata (CA), genetic algorithm (GA), regression model (R model) and etc. The main objective of this paper is to summarize the 70 scientific papers concern about urban growth to make a review and find out the most important objective, factors, techniques and results for best approach to studying urban growth. The criteria of choosing the papers are that each paper should focus mainly on urban growth modeling and techniques, also, using wide variety of data and factors. This paper aims to fill the gap of absence of the best methods for studying urban growth, as there is a diversity in the methods used, and there is also an absence of exemplary methods or optimal methods for using analytical tools to study urban growth. So, this paper tries to make it easy for researcher to mix the suitable techniques to get acceptable result for their hypothesis. The results assert combining two or more than two techniques and model to assure that the simulation or prediction models can give real and right approaches. However, most researches focused on combining specific techniques with models such as Cellular Automata CA-Markov Chain MC Model-Logistic regression or Cellular Automata CA-Markov Chain MC model or GIS-MCDM or GIS Based AHP etc. Although, in many references some of these techniques were combined together to extract best result. However, the rule that defines the best combination relies on project criteria, the infinite factors, analysis tools, the nature and quality of these models. On the other hand, whether the project needs a simulation or prediction models, all these models can achieve better result when integrated with quantitative models such as analytic hierarchy process (AHP), the Markov chain analysis or multi-criteria decision making (MCDM) techniques. Also, using remote sensing, satellite images and land use and land cover maps as basic data for analysis were the most common factors according to this review.