Search

Your search keyword '"Bateni S"' showing total 43 results
Start Over Author "Bateni S"
43 results on '"Bateni S"'

2. Anzali Wetland Crisis: Unraveling the Decline of Iran's Ecological Gem.

Author

Mahdian, M., Noori, R., Salamattalab, M. M., Heggy, E., Bateni, S. M., Nohegar, A., Hosseinzadeh, M., Siadatmousavi, S. M., Fadaei, M. R., and Abolfathi, S.

Subjects
WETLANDS, MACHINE learning, GENERAL circulation model, COASTAL wetlands, BODIES of water, ECOSYSTEM health
Abstract

The wetland loss rate in Iran is faster than the global average. Comprehending the shrinkage rate in Iranian wetlands and identifying the underlying drivers of these changes is essential for safeguarding their ecosystems' health and services. This study proposes a novel gray‐box modeling framework to quantify the effects of climate change and anthropogenic activities on the wetlands, by combining process‐based and machine learning models. The developed model is utilized to project the Anzali coastal wetland shrinkage by simulating the complex interaction between the meteorological, hydrological, anthropogenic and sea water level characteristics, and the changes in wetland water surface area. Our framework aggregates Soil and Water Assessment Tool model, the 12 General Circulation Models of the Coupled Model Intercomparison Project Phase 6, Landsat imagery, and the Long Short‐Term Memory model to project the shrinkage of the wetland till 2100. A comprehensive range of climate and Land Use/Cover change scenarios are analyzed. The results show that wetland will seasonally desiccate in 2058, mainly due to increasing air temperature, reduction in precipitation and inflow, excessive sediment loading to the wetland, and decline in the Caspian Sea level. For optimistic scenarios, where no changes in the Caspian Sea level is considered, the wetland will gradually diminish and become a seasonal waterbody by 2100. The outcomes of this study highlight that the Anzali wetland desiccation has profound implications for the regional‐scale ecological balance, ecosystem health and function, public health, and local economy. Robust environmental interventions and sustainable development strategies are urgently needed to mitigate the detrimental impacts of climate and anthropogenic drivers on the wetland. Plain Language Summary: Wetlands, with a small footprint on our planet's land surface, are home to ∼40% of the Earth's species. Despite the numerous benefits wetlands deliver to human life and the environment, they are facing significant threats during the Anthropocene, particularly by global warming. Iran's wetlands are experiencing a rapid disappearance, with shrinkage rates surpassing the global average. Iran is ranked as the second‐worst country, after Greece, in terms of wetlands listed in the Montreux Record. The temporal shrinkage rate of the Anzali wetland, the most ecologically important site in Iran, is investigated under climate and land use/cover change scenarios. The water surface area of the wetland is projected to reach zero by spring 2058. If no changes occur in the Caspian Sea level, which is connected to the Anzali wetland, the wetland is projected to transit a seasonal water body by spring 2093. The desiccation of the wetland is attributed to increasing air temperature, decline in the Caspian Sea's water level, increase in sediment loads, and reduction in precipitation and inflow to the wetland. The Anzali wetland desiccation has profound implications for the regional‐scale ecological balance, ecosystem health and function, public health, and local economy. Key Points: A gray‐box modeling framework is developed to project water surface area for wetlandsThe Anzali wetland desiccates between 2058 and 2062 in non‐conservative scenariosIn conservative scenarios, the Anzali wetland will transit a seasonal waterbody by 2100 [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

4. Strong warming rates in the surface and bottom layers of a boreal lake:results from approximately six decades of measurements (1964–2020)

Author

Noori, R. (Roohollah), Bateni, S. M. (Sayed M.), Saari, M. (Markus), Almazroui, M. (Mansour), Torabi Haghighi, A. (Ali), Noori, R. (Roohollah), Bateni, S. M. (Sayed M.), Saari, M. (Markus), Almazroui, M. (Mansour), and Torabi Haghighi, A. (Ali)

Abstract

High-latitude lakes are warming faster than the global average with deep implications for life on Earth. Using an approximately six-decade long in situ data set, we explored the changes in lake surface-water temperature (LST), lake deep-water temperature (LDT), lake depth-weighted mean water temperature (LDMT), and ice-free days in Lake Kallavesi, a boreal lake in central Finland, when the lake was stratified (June–August). Our results suggest that the LST is warming faster than the local air temperature (AT). As for the LST, fast warming was also observed in the LDT and LDMT, but at rates slower than those in the LST. The number of ice-free days also shows an upward trend, with a rate of about 7 days per decade during the study period. The corresponding local AT is the main driver of the LST, followed by the ice-free days and annual mean AT. Air temperature and ice-free days also mainly contribute to the changes in the LDMT. The LDT is affected more by the North Atlantic Oscillation signals in this freshwater lake. The AT in the prior months does not affect the LDT in Lake Kallavesi although the AT during the prior season, that is, spring, is the main driver of summer LDT. This highlights the local AT impact on the LDT at time scales longer than a month. The warming rates in the lake water are at a minimum in June because the lake is not yet strongly stratified in this month when compared to July and August. These findings improve our knowledge of long-term changes in the lake water temperature in a high-latitude lake, a region with severe environmental consequences due to fast changes in the AT and lake ice phenology.

Published
2022

5. Contribution of Satellite-Based Precipitation in Hydrological Rainfall–Runoff Modeling : Case Study of the Hammam Boughrara Region in Algeria

Author

Bemmoussat, A., Korichi, K., Baahmed, D., Maref, N., Djoukbala, O., Kalantari, Zahra, Bateni, S. M., Bemmoussat, A., Korichi, K., Baahmed, D., Maref, N., Djoukbala, O., Kalantari, Zahra, and Bateni, S. M.

Abstract

Hydrological models are viewed as powerful tools that have a major importance for managing water resources and predicting flows. It should be specified that the meteorological parameter rainfall is the main input in these models. In the current study, data from only one rainfall station are available over the analysis domain, which cannot represent the entire Hammam Boughrara watershed of Algeria. The precipitation data remotely detected by the tropical rainfall measuring mission (TRMM) provide good spatial coverage in the watershed and can be used to fill in the missing data. The use of raw TRMM data gives poor results from the simulated flow rates with a Nash–Sutcliffe efficiency NSE equal to 0.34 for the validation period that ranges from year 2000 to 2005; this is mainly due to uncertainties in the TRMM data. For this reason, it was deemed necessary to carry out a performance test of the model. The results obtained give an unsatisfactory percent bias (PBIAS) of − 46.24%, which suggests the need to perform a correction to decrease the PBIAS of satellite precipitation. For this, two methods were used: the linear regression method and the multiplicative method. These two techniques can only be applied if there is at least one rainfall measurement station available in the watershed. The obtained results are very satisfactory since the PBIAS reaches − 0.62% for the linear regression method and − 11.58% for the multiplicative method. In addition, the use of corrected TRMMs gives also very good results with a Nash–Sutcliffe efficiency that ranges from 0.74 to 0.88 for both validation and calibration periods. Overall, the current study is supportive to estimate the satellite-based rainfall, one of the very sensitive to measure the meteorological parameter, in northwestern Algeria., QC 20220504

Published
2021
Full Text
View/download PDF

9. Estimation of Forest Leaf Area Index Using Meteorological Data: Assessment of Heuristic Models.

Author

Karimi, S., Nazemi, A. H., Sadraddini, A. A., Xu, T. R., Bateni, S. M., and Fard, A. F.

Subjects
LEAF area index, HEURISTIC, PARTIAL least squares regression, IMAGE analysis, REMOTE sensing, MULTISPECTRAL imaging, FOREST biomass
Abstract

Leaf Area Index (LAI) is an important structural feature of our ecosystem as it affects energy, carbon, and water exchanges between the land surface and overlying atmosphere. Global scale LAI datasets have been obtained by regression, heuristic data driven, and radiative transfer models using remotely sensed land surface reflectance data. However, the estimation of LAI from remotely sensed data is limited only to clear sky conditions. Also, it is problematic to estimate LAI in forests by using conventional remote sensing image analysis of multi-spectral data. Due to the above-mentioned shortcomings of estimating LAI from remotely sensed data, this study obtained LAI from meteorological data using the Gene Expression Programming (GEP) technique. The new approach was tested in different forest sites with broad-leaf and needle-leaf trees in USA. The results showed that the GEP technique can accurately estimate LAI from meteorological data in different forest sites. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

11. Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites

Author

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Entekhabi, Dara, Xu, Tongren, Bateni, S. M., Liang, S., Mao, Kebiao, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Entekhabi, Dara, Xu, Tongren, Bateni, S. M., Liang, S., and Mao, Kebiao

Abstract

Recently, a number of studies have focused on estimating surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) observations into variational data assimilation (VDA) schemes. Using the full heat diffusion equation as a constraint, the surface energy balance equation can be solved via assimilation of sequences of LST within a VDA framework. However, the VDA methods have been tested only in limited field sites that span only a few climate and land use types. Hence, in this study, combined-source (CS) and dual-source (DS) VDA schemes are tested extensively over six FluxNet sites with different vegetation covers (grassland, cropland, and forest) and climate conditions. The CS model groups the soil and canopy together as a single source and does not consider their different contributions to the total turbulent heat fluxes, while the DS model considers them to be different sources. LST data retrieved from the Geostationary Operational Environmental Satellites are assimilated into these two VDA schemes. Sensible and latent heat flux estimates from the CS and DS models are compared with the corresponding measurements from flux tower stations. The results indicate that the performance of both models at dry, lightly vegetated sites is better than that at wet, densely vegetated sites. Additionally, the DS model outperforms the CS model at all sites, implying that the DS scheme is more reliable and can characterize the underlying physics of the problem better.

Published
2017

12. Interaction of self-trapped beams in high index glass RID B-7564-2011 RID B-2099-2008

Author

D'Asaro E, Heidari Bateni S, Pasquazi A, Gonzalo J, Solis J, Afonso CN, ASSANTO, GAETANO, D'Asaro, E, Heidari Bateni, S, Pasquazi, A, Assanto, Gaetano, Gonzalo, J, Solis, J, and Afonso, Cn

Subjects
Nonlinear Sciences::Pattern Formation and Solitons
Abstract

We observe attraction, repulsion and energy exchange between two self-trapped beams in a heavy-metal-oxide glass exhibiting a Kerr-like response with multiphoton absorption. The coherent interaction between spatial solitons is controlled by their relative phase and modeled by a nonlinear dissipative Schrodinger equation. (C) 2009 Optical Society of America

Published
2009

15. Estimation of soil dispersivity using soft computing approaches.

Author

Emamgholizadeh, Samad, Bahman, Kiana, Bateni, S., Ghorbani, Hadi, Marofpoor, Isa, and Nielson, Jeffrey

Subjects
SOIL dispersion, ARTIFICIAL neural networks, SOFT computing, GENE expression, GRAIN size, REGRESSION analysis
Abstract

The accurate estimation of soil dispersivity ( α) is required for characterizing the transport of contaminants in soil. The in situ measurement of α is costly and time-consuming. Hence, in this study, three soft computing methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and gene expression programming (GEP), are used to estimate α from more readily measurable physical soil variables, including travel distance from source of pollutant ( L), mean grain size ( D ), soil bulk density ( ρ ), and contaminant velocity ( V ). Based on three statistical metrics [i.e., mean absolute error, root-mean-square error (RMSE), and coefficient of determination ( R )], it is found that all approaches (ANN, ANFIS, and GEP) can accurately estimate α. Results also show that the ANN model (with RMSE = 0.00050 m and R = 0.977) performs better than the ANFIS model (with RMSE = 0.00062 m and R = 0.956), and the estimates from GEP are almost as accurate as those from ANFIS. The performance of ANN, ANFIS, and GEP models is also compared with the traditional multiple linear regression (MLR) method. The comparison indicates that all of the soft computing methods outperform the MLR model. Finally, the sensitivity analysis shows that the travel distance from source of pollution ( L) and bulk density ( ρ ) have, respectively, the most and the least effect on the soil dispersivity. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

22. Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters

Author

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology), Bateni, S. M., Entekhabi, Dara, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology), Bateni, S. M., and Entekhabi, Dara

Abstract

[1] The estimation of surface heat fluxes based on the assimilation of land surface temperature (LST) has been achieved within a variational data assimilation (VDA) framework. Variational approaches require the development of an adjoint model, which is difficult to derive and code in the presence of thresholds and discontinuities. Also, it is computationally expensive to obtain the background error covariance for the variational approaches. Moreover, the variational schemes cannot directly provide statistical information on the accuracy of their estimates. To overcome these shortcomings, we develop an alternative data assimilation (DA) procedure based on ensemble Kalman smoother (EnKS) with the state augmentation method. The unknowns of the assimilation scheme are neutral turbulent heat transfer coefficient (that scales the sum of turbulent heat fluxes) and evaporative fraction, EF (that represents partitioning among the turbulent fluxes). The new methodology is illustrated with an application to the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) that includes areal average hydrometeorological forcings and flux observations. The results indicate that the EnKS model not only provides reasonably accurate estimates of EF and turbulent heat fluxes but also enables us to determine the uncertainty of estimations under various land surface hydrological conditions. The results of the EnKS model are also compared with those of an optimal smoother (a dynamic variational model). It is found that the EnKS model estimates are less than optimal. However, the degree of suboptimality is small, and its outcomes are roughly comparable to those of an optimal smoother. Overall, the results from this test indicate that EnKS is an efficient and flexible data assimilation procedure that is able to extract useful information on the partitioning of available surface energy from LST measurements and eventually provides reliable estimates of turbulent h

Published
2013

23. Relative efficiency of land surface energy balance components

Author

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology), Bateni, S. M., Entekhabi, Dara, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology), Bateni, S. M., and Entekhabi, Dara

Abstract

[1] The partitioning of available energy into dissipative fluxes over land surfaces is dependent on the state variable of the surface energy balance (land surface temperature) and the state variable of the surface water balance (soil moisture). The direct measurement of the turbulent fluxes is achieved with in situ instruments at tower sites. These point-scale measurements are sparsely distributed. Broader scale mapping of the turbulent fluxes is mostly dependent on land surface temperature (LST) and optical/infrared vegetation that can be sensed remotely. There are several data assimilation approaches currently in use that intake sequences of daytime LST that attain different diurnal amplitudes depending on available energy and the relative efficiency of surface energy balance to infer the magnitude of surface flux components such as latent and sensible heat flux. In this study we perform stability analysis on the evolution of LST in order to provide insights into the physical bases for why LST variations can be used to diagnose surface energy balance (SEB) components. The derived relative efficiencies of SEB components in dissipating available energy at the land surface are tested using two field experiment measurements. The results show that the theoretically derived relative efficiencies of SEB components agree well with field observations. The study provides insight into how LST sequences implicitly contain the signature of partitioning of available energy among SEB components and can be used to infer their magnitudes.

Published
2013

31. The r33 electro-optic coefficient of Er:LiNbO3

Author

Petris, A, primary, Heidari Bateni, S, additional, Vlad, V I, additional, Alonzo, M, additional, Pettazzi, F, additional, Argiolas, N, additional, Bazzan, M, additional, Sada, C, additional, Wolfersberger, D, additional, and Fazio, E, additional

Published
2009
Full Text
View/download PDF

35. Estimating Turbulent Heat Fluxes With a Weak-Constraint Data Assimilation Scheme: A Case Study (HiWATER-MUSOEXE).

Author

Xu, Tongren, Bateni, S. Mohyddin, and Liang, Shunlin

Abstract

A weak-constraint variational data assimilation (WC-VDA) scheme was developed to estimate turbulent heat fluxes by assimilating sequences of land surface temperature measurements. In contrast to the commonly used strong-constraint VDA system, the WC-VDA approach accounts for the effects of structural and model errors and generates better results. This is achieved by adding a model error term ( $\omega$) to the surface energy balance equation. The WC-VDA model was tested at two sites with very distinct hydrological and vegetated conditions: the Daman site (a wet site located in an oasis area and covered by seeded corn) and the Huazhaizi site (a dry site located in a desert area and covered by sparse grass). The two sites represent typical desert–oasis landscapes in the middle reaches of the Heihe River Basin, northwestern China. The results proved that the WC-VDA method performed well over very dry and wet conditions, and the estimated sensible and latent heat fluxes agree well with eddy covariance measurements. [ABSTRACT FROM PUBLISHER]

Published
2015
Full Text
View/download PDF

37. Interacting Solitons in a High Index Glass

Author

E. D'Asaro, Alessia Pasquazi, Salvatore Stivala, Gaetano Assanto, S. Heidari-Bateni, Pasquazi, A, D'Asaro, E, Heidari-Bateni, S, Stivala, S, and Assanto, G

Subjects
Physics, Index (economics), Optics, Kerr effect, Optical glass, Condensed matter physics, business.industry, Nonlinear optics, business, Refractive index, Waveguide (optics), Nonlinear optics, Kerr effect, Spatial solitons
Abstract

We investigate the interaction of two coherent 2D+1 solitary beams in a high index glass.

Published
2010

38. NCCN Guidelines® Insights: Melanoma: Cutaneous, Version 2.2024.

Author

Swetter SM, Johnson D, Albertini MR, Barker CA, Bateni S, Baumgartner J, Bhatia S, Bichakjian C, Boland G, Chandra S, Chmielowski B, DiMaio D, Dronca R, Fields RC, Fleming MD, Galan A, Guild S, Hyngstrom J, Karakousis G, Kendra K, Kiuru M, Lange JR, Lanning R, Logan T, Olson D, Olszanski AJ, Ott PA, Ross MI, Rothermel L, Salama AK, Sharma R, Skitzki J, Smith E, Tsai K, Wuthrick E, Xing Y, McMillian N, and Espinosa S

Subjects
Humans, Neoplasm Staging, Medical Oncology standards, Medical Oncology methods, Melanoma therapy, Melanoma diagnosis, Melanoma pathology, Skin Neoplasms therapy, Skin Neoplasms diagnosis, Skin Neoplasms pathology
Abstract

The NCCN Guidelines for Cutaneous Melanoma (termed Melanoma: Cutaneous) provide multidisciplinary recommendations for diagnostic workup, staging, and treatment of patients. These NCCN Guidelines Insights focus on the update to neoadjuvant systemic therapy options and summarize the new clinical data evaluated by the NCCN panel for the recommended therapies in Version 2.2024 of the NCCN Guidelines for Cutaneous Melanoma.

Published
2024
Full Text
View/download PDF

39. 2023 Canadian Surgery Forum: Sept. 20-23, 2023.

Author

Brière R, Émond M, Benhamed A, Blanchard PG, Drolet S, Habashi R, Golbon B, Shellenberger J, Pasternak J, Merchant S, Shellenberger J, La J, Sawhney M, Brogly S, Cadili L, Horkoff M, Ainslie S, Demetrick J, Chai B, Wiseman K, Hwang H, Alhumoud Z, Salem A, Lau R, Aw K, Nessim C, Gawad N, Alibhai K, Towaij C, Doan D, Raîche I, Valji R, Turner S, Balmes PN, Hwang H, Hameed SM, Tan JGK, Wijesuriya R, Tan JGK, Hew NLC, Wijesuriya R, Lund M, Hawel J, Gregor J, Leslie K, Lenet T, McIsaac D, Hallet J, Jerath A, Lalu M, Nicholls S, Presseau J, Tinmouth A, Verret M, Wherrett C, Fergusson D, Martel G, Sharma S, McKechnie T, Talwar G, Patel J, Heimann L, Doumouras A, Hong D, Eskicioglu C, Wang C, Guo M, Huang L, Sun S, Davis N, Wang J, Skulsky S, Sikora L, Raîche I, Son HJ, Gee D, Gomez D, Jung J, Selvam R, Seguin N, Zhang L, Lacaille-Ranger A, Sikora L, McIsaac D, Moloo H, Follett A, Holly, Organ M, Pace D, Balvardi S, Kaneva P, Semsar-Kazerooni K, Mueller C, Vassiliou M, Al Mahroos M, Fiore JF Jr, Schwartzman K, Feldman L, Guo M, Karimuddin A, Liu GP, Crump T, Sutherland J, Hickey K, Bonisteel EM, Umali J, Dogar I, Warden G, Boone D, Mathieson A, Hogan M, Pace D, Seguin N, Moloo H, Li Y, Best G, Leong R, Wiseman S, Alaoui AA, Hajjar R, Wassef E, Metellus DS, Dagbert F, Loungnarath R, Ratelle R, Schwenter F, Debroux É, Wassef R, Gagnon-Konamna M, Pomp A, Richard CS, Sebajang H, Alaoui AA, Hajjar R, Dagbert F, Loungnarath R, Sebajang H, Ratelle R, Schwenter F, Debroux É, Wassef R, Gagnon-Konamna M, Pomp A, Santos MM, Richard CS, Shi G, Leung R, Lim C, Knowles S, Parmar S, Wang C, Debru E, Mohamed F, Anakin M, Lee Y, Samarasinghe Y, Khamar J, Petrisor B, McKechnie T, Eskicioglu C, Yang I, Mughal HN, Bhugio M, Gok MA, Khan UA, Fernandes AR, Spence R, Porter G, Hoogerboord CM, Neumann K, Pillar M, Guo M, Manhas N, Melck A, Kazi T, McKechnie T, Jessani G, Heimann L, Lee Y, Hong D, Eskicioglu C, McKechnie T, Tessier L, Archer V, Park L, Cohen D, Parpia S, Bhandari M, Dionne J, Eskicioglu C, Bolin S, Afford R, Armstrong M, Karimuddin A, Leung R, Shi G, Lim C, Grant A, Van Koughnett JA, Knowles S, Clement E, Lange C, Roshan A, Karimuddin A, Scott T, Nadeau K, Macmillan J, Wilson J, Deschenes M, Nurullah A, Cahill C, Chen VH, Patterson KM, Wiseman SM, Wen B, Bhudial J, Barton A, Lie J, Park CM, Yang L, Gouskova N, Kim DH, Afford R, Bolin S, Morris-Janzen D, McLellan A, Karimuddin A, Archer V, Cloutier Z, Berg A, McKechnie T, Wiercioch W, Eskicioglu C, Labonté J, Bisson P, Bégin A, Cheng-Oviedo SG, Collin Y, Fernandes AR, Hossain I, Ellsmere J, El-Kefraoui C, Do U, Miller A, Kouyoumdjian A, Cui D, Khorasani E, Landry T, Amar-Zifkin A, Lee L, Feldman L, Fiore J, Au TM, Oppenheimer M, Logsetty S, AlShammari R, AlAbri M, Karimuddin A, Brown C, Raval MJ, Phang PT, Bird S, Baig Z, Abu-Omar N, Gill D, Suresh S, Ginther N, Karpinski M, Ghuman A, Malik PRA, Alibhai K, Zabolotniuk T, Raîche I, Gawad N, Mashal S, Boulanger N, Watt L, Razek T, Fata P, Grushka J, Wong EG, Hossain I, Landry M, Mackey S, Fairbridge N, Greene A, Borgoankar M, Kim C, DeCarvalho D, Pace D, Wigen R, Walser E, Davidson J, Dorward M, Muszynski L, Dann C, Seemann N, Lam J, Harding K, Lowik AJ, Guinard C, Wiseman S, Ma O, Mocanu V, Lin A, Karmali S, Bigam D, Harding K, Greaves G, Parker B, Nguyen V, Ahmed A, Yee B, Perren J, Norman M, Grey M, Perini R, Jowhari F, Bak A, Drung J, Allen L, Wiseman D, Moffat B, Lee JKH, McGuire C, Raîche I, Tudorache M, Gawad N, Park LJ, Borges FK, Nenshi R, Jacka M, Heels-Ansdell D, Simunovic M, Bogach J, Serrano PE, Thabane L, Devereaux PJ, Farooq S, Lester E, Kung J, Bradley N, Best G, Ahn S, Zhang L, Prince N, Cheng-Boivin O, Seguin N, Wang H, Quartermain L, Tan S, Shamess J, Simard M, Vigil H, Raîche I, Hanna M, Moloo H, Azam R, Ko G, Zhu M, Raveendran Y, Lam C, Tang J, Bajwa A, Englesakis M, Reel E, Cleland J, Snell L, Lorello G, Cil T, Ahn HS, Dube C, McIsaac D, Smith D, Leclerc A, Shamess J, Rostom A, Calo N, Thavorn K, Moloo H, Laplante S, Liu L, Khan N, Okrainec A, Ma O, Lin A, Mocanu V, Karmali S, Bigam D, Bruyninx G, Georgescu I, Khokhotva V, Talwar G, Sharma S, McKechnie T, Yang S, Khamar J, Hong D, Doumouras A, Eskicioglu C, Spoyalo K, Rebello TA, Chhipi-Shrestha G, Mayson K, Sadiq R, Hewage K, MacNeill A, Muncner S, Li MY, Mihajlovic I, Dykstra M, Snelgrove R, Wang H, Schweitzer C, Wiseman SM, Garcha I, Jogiat U, Baracos V, Turner SR, Eurich D, Filafilo H, Rouhi A, Bédard A, Bédard ELR, Patel YS, Alaichi JA, Agzarian J, Hanna WC, Patel YS, Alaichi JA, Provost E, Shayegan B, Adili A, Hanna WC, Mistry N, Gatti AA, Patel YS, Farrokhyar F, Xie F, Hanna WC, Sullivan KA, Farrokhyar F, Patel YS, Liberman M, Turner SR, Gonzalez AV, Nayak R, Yasufuku K, Hanna WC, Mistry N, Gatti AA, Patel YS, Cross S, Farrokhyar F, Xie F, Hanna WC, Haché PL, Galvaing G, Simard S, Grégoire J, Bussières J, Lacasse Y, Sassi S, Champagne C, Laliberté AS, Jeong JY, Jogiat U, Wilson H, Bédard A, Blakely P, Dang J, Sun W, Karmali S, Bédard ELR, Wong C, Hakim SY, Azizi S, El-Menyar A, Rizoli S, Al-Thani H, Fernandes AR, French D, Li C, Ellsmere J, Gossen S, French D, Bailey J, Tibbo P, Crocker C, Bondzi-Simpson A, Ribeiro T, Kidane B, Ko M, Coburn N, Kulkarni G, Hallet J, Ramzee AF, Afifi I, Alani M, El-Menyar A, Rizoli S, Al-Thani H, Chughtai T, Huo B, Manos D, Xu Z, Kontouli KM, Chun S, Fris J, Wallace AMR, French DG, Giffin C, Liberman M, Dayan G, Laliberté AS, Yasufuku K, Farivar A, Kidane B, Weessies C, Robinson M, Bednarek L, Buduhan G, Liu R, Tan L, Srinathan SK, Kidane B, Nasralla A, Safieddine N, Gazala S, Simone C, Ahmadi N, Hilzenrat R, Blitz M, Deen S, Humer M, Jugnauth A, Buduhan G, Kerr L, Sun S, Browne I, Patel Y, Hanna W, Loshusan B, Shamsil A, Naish MD, Qiabi M, Nayak R, Patel R, Malthaner R, Pooja P, Roberto R, Greg H, Daniel F, Huynh C, Sharma S, Vieira A, Jain F, Lee Y, Mousa-Doust D, Costa J, Mezei M, Chapman K, Briemberg H, Jack K, Grant K, Choi J, Yee J, McGuire AL, Abdul SA, Khazoom F, Aw K, Lau R, Gilbert S, Sundaresan S, Jones D, Seely AJE, Villeneuve PJ, Maziak DE, Pigeon CA, Frigault J, Drolet S, Roy ÈM, Bujold-Pitre K, Courval V, Tessier L, McKechnie T, Lee Y, Park L, Gangam N, Eskicioglu C, Cloutier Z, McKechnie T (McMaster University), Archer V, Park L, Lee J, Patel A, Hong D, Eskicioglu C, Ichhpuniani S, McKechnie T, Elder G, Chen A, Logie K, Doumouras A, Hong D, Benko R, Eskicioglu C, Castelo M, Paszat L, Hansen B, Scheer A, Faught N, Nguyen L, Baxter N, Sharma S, McKechnie T, Khamar J, Wu K, Eskicioglu C, McKechnie T, Khamar J, Lee Y, Tessier L, Passos E, Doumouras A, Hong D, Eskicioglu C, McKechnie T, Khamar J, Sachdeva A, Lee Y, Hong D, Eskicioglu C, Fei LYN, Caycedo A, Patel S, Popa T, Boudreau L, Grin A, Wang T, Lie J, Karimuddin A, Brown C, Phang T, Raval M, Ghuman A, Candy S, Nanda K, Li C, Snelgrove R, Dykstra M, Kroeker K, Wang H, Roy H, Helewa RM, Johnson G, Singh H, Hyun E, Moffatt D, Vergis A, Balmes P, Phang T, Guo M, Liu J, Roy H, Webber S, Shariff F, Helewa RM, Hochman D, Park J, Johnson G, Hyun E, Robitaille S, Wang A, Maalouf M, Alali N, Elhaj H, Liberman S, Charlebois P, Stein B, Feldman L, Fiore JF Jr, Lee L, Hu R, Lacaille-Ranger A, Ahn S, Tudorache M, Moloo H, Williams L, Raîche I, Musselman R, Lemke M, Allen L, Samarasinghe N, Vogt K, Brackstone M, Zwiep T, Clement E, Lange C, Alam A, Ghuman A, Karimuddin A, Phang T, Raval M, Brown C, Clement E, Liu J, Ghuman A, Karimuddin A, Phang T, Raval M, Brown C, Mughal HN, Gok MA, Khan UA, Mughal HN, Gok MA, Khan UA, Mughal HN, Gok MA, Khan UA, Mughal HN, Gok MA, Khan UA, James N, Zwiep T, Van Koughnett JA, Laczko D, McKechnie T, Yang S, Wu K, Sharma S, Lee Y, Park L, Doumouras A, Hong D, Parpia S, Bhandari M, Eskicioglu C, McKechnie T, Tessier L, Lee S, Kazi T, Sritharan P, Lee Y, Doumouras A, Hong D, Eskicioglu C, McKechnie T, Lee Y, Hong D, Dionne J, Doumouras A, Parpia S, Bhandari M, Eskicioglu C, Hershorn O, Ghuman A, Karimuddin A, Brown C, Raval M, Phang PT, Chen A, Boutros M, Caminsky N, Dumitra T, Faris-Sabboobeh S, Demian M, Rigas G, Monton O, Smith A, Moon J, Demian M, Garfinkle R, Vasilevsky CA, Rajabiyazdi F, Boutros M, Courage E, LeBlanc D, Benesch M, Hickey K, Hartwig K, Armstrong C, Engelbrecht R, Fagan M, Borgaonkar M, Pace D, Shanahan J, Moon J, Salama E, Wang A, Arsenault M, Leon N, Loiselle C, Rajabiyazdi F, Boutros M, Brennan K, Rai M, Farooq A, McClintock C, Kong W, Patel S, Boukhili N, Caminsky N, Faris-Sabboobeh S, Demian M, Boutros M, Paradis T, Robitaille S, Dumitra T, Liberman AS, Charlebois P, Stein B, Fiore JF Jr, Feldman LS, Lee L, Zwiep T, Abner D, Alam T, Beyer E, Evans M, Hill M, Johnston D, Lohnes K, Menard S, Pitcher N, Sair K, Smith B, Yarjau B, LeBlanc K, Samarasinghe N, Karimuddin AA, Brown CJ, Phang PT, Raval MJ, MacDonell K, Ghuman A, Harvey A, Phang PT, Karimuddin A, Brown CJ, Raval MJ, Ghuman A, Hershorn O, Ghuman A, Karimuddin A, Raval M, Phang PT, Brown C, Logie K, Mckechnie T, Lee Y, Hong D, Eskicioglu C, Matta M, Baker L, Hopkins J, Rochon R, Buie D, MacLean A, Ghuman A, Park J, Karimuddin AA, Phang PT, Raval MJ, Brown CJ, Farooq A, Ghuman A, Patel S, Macdonald H, Karimuddin A, Raval M, Phang PT, Brown C, Wiseman V, Brennan K, Patel S, Farooq A, Merchant S, Kong W, McClintock C, Booth C, Hann T, Ricci A, Patel S, Brennan K, Wiseman V, McClintock C, Kong W, Farooq A, Kakkar R, Hershorn O, Raval M, Phang PT, Karimuddin A, Ghuman A, Brown C, Wiseman V, Farooq A, Patel S, Hajjar R, Gonzalez E, Fragoso G, Oliero M, Alaoui AA, Rendos HV, Djediai S, Cuisiniere T, Laplante P, Gerkins C, Ajayi AS, Diop K, Taleb N, Thérien S, Schampaert F, Alratrout H, Dagbert F, Loungnarath R, Sebajang H, Schwenter F, Wassef R, Ratelle R, Debroux É, Cailhier JF, Routy B, Annabi B, Brereton NJB, Richard C, Santos MM, Gimon T, MacRae H, de Buck van Overstraeten A, Brar M, Chadi S, Kennedy E, Baker L, Hopkins J, Rochon R, Buie D, MacLean A, Park LJ, Archer V, McKechnie T, Lee Y, McIsaac D, Rashanov P, Eskicioglu C, Moloo H, Devereaux PJ, Alsayari R, McKechnie T, Ichhpuniani S, Lee Y, Eskicioglu C, Hajjar R, Oliero M, Fragoso G, Ajayi AS, Alaoui AA, Rendos HV, Calvé A, Cuisinière T, Gerkins C, Thérien S, Taleb N, Dagbert F, Sebajang H, Loungnarath R, Schwenter F, Ratelle R, Wassef R, Debroux E, Richard C, Santos MM, Kennedy E, Simunovic M, Schmocker S, Brown C, MacLean A, Liberman S, Drolet S, Neumann K, Stotland P, Jhaveri K, Kirsch R, Alnajem H, Alibrahim H, Giundi C, Chen A, Rigas G, Munir H, Safar A, Sabboobeh S, Holland J, Boutros M, Kennedy E, Richard C, Simunovic M, Schmocker S, Brown C, MacLean A, Liberman S, Drolet S, Neumann K, Stotland P, Jhaveri K, Kirsch R, Bruyninx G, Gill D, Alsayari R, McKechnie T, Lee Y, Hong D, Eskicioglu C, Zhang L, Abtahi S, Chhor A, Best G, Raîche I, Musselman R, Williams L, Moloo H, Caminsky NG, Moon JJ, Marinescu D, Pang A, Vasilevsky CA, Boutros M, Al-Abri M, Gee E, Karimuddin A, Phang PT, Brown C, Raval M, Ghuman A, Morena N, Ben-Zvi L, Hayman V, Hou M (University of Calgary), Nguyen D, Rentschler CA, Meguerditchian AN, Mir Z, Fei L, McKeown S, Dinchong R, Cofie N, Dalgarno N, Cheifetz R, Merchant S, Jaffer A, Cullinane C, Feeney G, Jalali A, Merrigan A, Baban C, Buckley J, Tormey S, Benesch M, Wu R, Takabe K, Benesch M, O'Brien S, Kazazian K, Abdalaty AH, Brezden C, Burkes R, Chen E, Govindarajan A, Jang R, Kennedy E, Lukovic J, Mesci A, Quereshy F, Swallow C, Chadi S, Habashi R, Pasternak J, Marini W, Zheng W, Murakami K, Ohashi P, Reedijk M, Hu R, Ivankovic V, Han L, Gresham L, Mallick R, Auer R, Ribeiro T, Bondzi-Simpson A, Coburn N, Hallet J, Cil T, Fontebasso A, Lee A, Bernard-Bedard E, Wong B, Li H, Grose E, Brandts-Longtin O, Aw K, Lau R, Abed A, Stevenson J, Sheikh R, Chen R, Johnson-Obaseki S, Nessim C, Hennessey RL, Meneghetti AT, Bildersheim M, Bouchard-Fortier A, Nelson G, Mack L, Ghasemi F, Naeini MM, Parsyan A, Kaur Y, Covelli A, Quereshy F, Elimova E, Panov E, Lukovic J, Brierley J, Burnett B, Swallow C, Eom A, Kirkwood D, Hodgson N, Doumouras A, Bogach J, Whelan T, Levine M, Parvez E, Ng D, Kazazian K, Lee K, Lu YQ, Kim DK, Magalhaes M, Grigor E, Arnaout A, Zhang J, Yee EK, Hallet J, Look Hong NJ, Nguyen L, Coburn N, Wright FC, Gandhi S, Jerzak KJ, Eisen A, Roberts A, Ben Lustig D, Quan ML, Phan T, Bouchard-Fortier A, Cao J, Bayley C, Watanabe A, Yao S, Prisman E, Groot G, Mitmaker E, Walker R, Wu J, Pasternak J, Lai CK, Eskander A, Wasserman J, Mercier F, Roth K, Gill S, Villamil C, Goldstein D, Munro V, Pathak A (University of Manitoba), Lee D, Nguyen A, Wiseman S, Rajendran L, Claasen M, Ivanics T, Selzner N, McGilvray I, Cattral M, Ghanekar A, Moulton CA, Reichman T, Shwaartz C, Metser U, Burkes R, Winter E, Gallinger S, Sapisochin G, Glinka J, Waugh E, Leslie K, Skaro A, Tang E, Glinka J, Charbonneau J, Brind'Amour A, Turgeon AF, O'Connor S, Couture T, Wang Y, Yoshino O, Driedger M, Beckman M, Vrochides D, Martinie J, Alabduljabbar A, Aali M, Lightfoot C, Gala-Lopez B, Labelle M, D'Aragon F, Collin Y, Hirpara D, Irish J, Rashid M, Martin T, Zhu A, McKnight L, Hunter A, Jayaraman S, Wei A, Coburn N, Wright F, Mallette K, Elnahas A, Alkhamesi N, Schlachta C, Hawel J, Tang E, Punnen S, Zhong J, Yang Y, Streith L, Yu J, Chung S, Kim P, Chartier-Plante S, Segedi M, Bleszynski M, White M, Tsang ME, Jayaraman S, Lam-Tin-Cheung K, Jayaraman S, Tsang M, Greene B, Pouramin P, Allen S, Evan Nelson D, Walsh M, Côté J, Rebolledo R, Borie M, Menaouar A, Landry C, Plasse M, Létourneau R, Dagenais M, Rong Z, Roy A, Beaudry-Simoneau E, Vandenbroucke-Menu F, Lapointe R, Ferraro P, Sarkissian S, Noiseux N, Turcotte S, Haddad Y, Bernard A, Lafortune C, Brassard N, Roy A, Perreault C, Mayer G, Marcinkiewicz M, Mbikay M, Chrétien M, Turcotte S, Waugh E, Sinclair L, Glinka J, Shin E, Engelage C, Tang E, Skaro A, Muaddi H, Flemming J, Hansen B, Dawson L, O'Kane G, Feld J, Sapisochin G, Zhu A, Jayaraman S, Cleary S, Hamel A, Pigeon CA, Marcoux C, Ngo TP, Deshaies I, Mansouri S, Amhis N, Léveillé M, Lawson C, Achard C, Ilkow C, Collin Y, Tai LH, Park L, Griffiths C, D'Souza D, Rodriguez F, McKechnie T, Serrano PE, Hennessey RL, Yang Y, Meneghetti AT, Panton ONM, Chiu CJ, Henao O, Netto FS, Mainprize M, Hennessey RL, Chiu CJ, Hennessey RL, Chiu CJ, Jatana S, Verhoeff K, Mocanu V, Jogiat U, Birch D, Karmali S, Switzer N, Hetherington A, Verhoeff K, Mocanu V, Birch D, Karmali S, Switzer N, Safar A, Al-Ghaithi N, Vourtzoumis P, Demyttenaere S, Court O, Andalib A, Wilson H, Verhoeff K, Dang J, Kung J, Switzer N, Birch D, Madsen K, Karmali S, Mocanu V, Wu T, He W, Vergis A, Hardy K, Zmudzinski M, Daenick F, Linton J, Zmudzinski M, Fowler-Woods M, He W, Fowler-Woods A, Shingoose G, Vergis A, Hardy K, Lee Y, Doumouras A, Molnar A, Nguyen F, Hong D, Schneider R, Fecso AB, Sharma P, Maeda A, Jackson T, Okrainec A, McLean C, Mocanu V, Birch D, Karmali S, Switzer N, MacVicar S, Dang J, Mocanu V, Verhoeff K, Jogiat U, Karmali S, Birch D, Switzer N, McLennan S, Verhoeff K, Purich K, Dang J, Kung J, Mocanu V, McLennan S, Verhoeff K, Mocanu V, Jogiat U, Birch DW, Karmali S, Switzer NJ, Jeffery L, Hwang H, Ryley A, Schellenberg M, Owattanapanich N, Emigh B, Nichols C, Dilday J, Ugarte C, Onogawa A, Matsushima K, Martin MJ, Inaba K, Schellenberg M, Emigh B, Nichols C, Dilday J, Ugarte C, Onogawa A, Shapiro D, Im D, Inaba K, Schellenberg M, Owattanapanich N, Ugarte C, Lam L, Martin MJ, Inaba K, Rezende-Neto J, Patel S, Zhang L, Mir Z, Lemke M, Leeper W, Allen L, Walser E, Vogt K, Ribeiro T, Bateni S, Bondzi-Simpson A, Coburn N, Hallet J, Barabash V, Barr A, Chan W, Hakim SY, El-Menyar A, Rizoli S, Al-Thani H, Mughal HN, Bhugio M, Gok MA, Khan UA, Warraich A, Gillman L, Ziesmann M, Momic J, Yassin N, Kim M, Makish A, Walser E, Smith S, Ball I, Moffat B, Parry N, Vogt K, Lee A, Kroeker J, Evans D, Fansia N, Notik C, Wong EG, Coyle G, Seben D, Smith J, Tanenbaum B, Freedman C, Nathens A, Fowler R, Patel P, Elrick T, Ewing M, Di Marco S, Razek T, Grushka J, Wong EG, Park LJ, Borges FK, Nenshi R, Serrano PE, Engels P, Vogt K, Di Sante E, Vincent J, Tsiplova K, Devereaux PJ, Talwar G, Dionne J, McKechnie T, Lee Y, Kazi T, El-Sayes A, Bogach J, Hong D, Eskicioglu C, Connell M, Klooster A, Beck J, Verhoeff K, Strickland M, Anantha R, Groszman L, Caminsky NG, Watt L, Boulanger N, Razek T, Grushka J, Di Marco S, Wong EG, Livergant R, McDonald B, Binda C, Luthra S, Ebert N, Falk R, and Joos E

Published
2023
Full Text
View/download PDF

40. A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.

Author

Emrani Z, Bateni S, and Rabbani H

Abstract

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms., Competing Interests: There are no conflicts of interest.

Published
2017

41. Evolutionary Dynamics of Tumor-Stroma Interactions in Multiple Myeloma.

Author

Sartakhti JS, Manshaei MH, Bateni S, and Archetti M

Subjects
Cell Communication, Evolution, Molecular, Models, Biological, Multiple Myeloma pathology, Stromal Cells pathology
Abstract

Cancer cells and stromal cells cooperate by exchanging diffusible factors that sustain tumor growth, a form of frequency-dependent selection that can be studied in the framework of evolutionary game theory. In the case of multiple myeloma, three types of cells (malignant plasma cells, osteoblasts and osteoclasts) exchange growth factors with different effects, and tumor-stroma interactions have been analysed using a model of cooperation with pairwise interactions. Here we show that a model in which growth factors have autocrine and paracrine effects on multiple cells, a more realistic assumption for tumor-stroma interactions, leads to different results, with implications for disease progression and treatment. In particular, the model reveals that reducing the number of malignant plasma cells below a critical threshold can lead to their extinction and thus to restore a healthy balance between osteoclast and osteoblast, a result in line with current therapies against multiple myeloma., Competing Interests: The authors have declared that no competing interests exist.

Published
2016
Full Text
View/download PDF

42. Recurrent suicide attempt and female hormones.

Author

Mousavi SG, Bateni S, Maracy MR, Mardanian F, and Mousavi SH

Abstract

Background: Because of more frequency of suicidal attempts in females, we need to study about its relationship with the female hormones. The aim of this study was to evaluate the serum estrogen and progesterone concentration and their relationship with suicidal attempt ranking in the attempted females., Materials and Methods: The studied cases chose from patients who had referred to clinical toxicology emergency of Noor Hospital (Isfahan, Iran), during 2012, because of suicidal attempt. The estrogen and progesterone serum level of the 111 females were measured during 24 hours after suicidal attempt. The rank of their suicide, the demographic properties, and the menstrual cycle phase of them were also registered, as the patient's statement. The results were analyzed by ANCOVA and Kruscal-Wallis under SPSS16., Results: Mean serum concentration of the estrogen was 76.8 pg/mL, and the mean serum concentration of progesterone was 2.99 ng/mL. Of them, 62.2% were in the luteal phase, and 37.8% were in the follicular phase, as they said. The serum progesterone concentration of the patients with more than two times suicidal attempts was significantly higher than the others., Conclusion: The suicidal attempt ranks significantly related to the serum progesterone concentration and the luteal phase.

Published
2014
Full Text
View/download PDF

43. Interaction of self-trapped beams in high index glass.

Author

D'Asaro E, Heidari-Bateni S, Pasquazi A, Assanto G, Gonzalo J, Solis J, and Afonso CN

Abstract

We observe attraction, repulsion and energy exchange between two self-trapped beams in a heavy-metal-oxide glass exhibiting a Kerr-like response with multiphoton absorption. The coherent interaction between spatial solitons is controlled by their relative phase and modelled by a nonlinear dissipative Schrödinger equation.

Published
2009
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results