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u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">During the last five years the use of acupuncture in female infertility as an adjuvant to conventional treatment in assisted reproductive technology (ART) has increased in popularity.The present paper briefly discusses clinical and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19790751" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">During the last five years the use of acupuncture in female infertility as an adjuvant to conventional treatment in assisted reproductive technology (ART) has increased in popularity.The present paper briefly discusses clinical and experimental data on the effect of acupuncture on uterine and ovarian blood flow, as an analgesic method during ART, and on endocrine and metabolic disturbances such as</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item 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the last five years the use of acupuncture in female infertility as an adjuvant to conventional treatment in assisted reproductive technology (ART) has increased in popularity.The present paper briefly discusses clinical and experimental data on the effect of acupuncture on uterine and ovarian blood flow, as an analgesic method during ART, and on endocrine and metabolic disturbances such as","downloadable_attachments":[{"id":42016829,"asset_id":19790751,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":40520767,"first_name":"Elisabet","last_name":"Stener-Victorin","domain_name":"ki","page_name":"ElisabetStenerVictorin","display_name":"Elisabet Stener-Victorin","profile_url":"https://ki.academia.edu/ElisabetStenerVictorin?f_ri=1120502","photo":"https://0.academia-photos.com/40520767/14598841/15455293/s65_elisabet.stener-victorin.jpg"}],"research_interests":[{"id":2066,"name":"Research 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$('*[data-has-card-for-ri-list=1165002]'), work: {"id":1165002,"title":"Effluents with soluble metabolites generated from acidogenic and methanogenic processes as substrate for additional hydrogen production through photo-biological …","created_at":"2011-12-18T14:41:07.420-08:00","url":"https://www.academia.edu/1165002/Effluents_with_soluble_metabolites_generated_from_acidogenic_and_methanogenic_processes_as_substrate_for_additional_hydrogen_production_through_photo_biological_?f_ri=1120502","dom_id":"work_1165002","summary":null,"downloadable_attachments":[{"id":51079835,"asset_id":1165002,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1056566,"first_name":"S","last_name":"Venkata Mohan","domain_name":"iictindia","page_name":"SVenkataMohan","display_name":"S Venkata 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Egermann","profile_url":"https://independent.academia.edu/PEgermann?f_ri=1120502","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":55,"name":"Environmental Engineering","url":"https://www.academia.edu/Documents/in/Environmental_Engineering?f_ri=1120502","nofollow":false},{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering?f_ri=1120502","nofollow":false},{"id":2024,"name":"Mass Transfer","url":"https://www.academia.edu/Documents/in/Mass_Transfer?f_ri=1120502","nofollow":false},{"id":2215,"name":"Water","url":"https://www.academia.edu/Documents/in/Water?f_ri=1120502","nofollow":false},{"id":4526,"name":"Water resources","url":"https://www.academia.edu/Documents/in/Water_resources?f_ri=1120502"},{"id":90637,"name":"Porous Media","url":"https://www.academia.edu/Documents/in/Porous_Media?f_ri=1120502"},{"id":153168,"name":"Data Collection","url":"https://www.academia.edu/Documents/in/Data_Collection?f_ri=1120502"},{"id":165199,"name":"Interfacial Tension","url":"https://www.academia.edu/Documents/in/Interfacial_Tension?f_ri=1120502"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1120502"},{"id":1256879,"name":"Thermodynamic Properties","url":"https://www.academia.edu/Documents/in/Thermodynamic_Properties?f_ri=1120502"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_21151422" data-work_id="21151422" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/21151422/Closure_relations_for_two_fluid_models_for_two_phase_stratified_smooth_and_stratified_wavy_flows">Closure relations for two-fluid models for two-phase stratified smooth and stratified wavy flows</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The theory-based closure relations for the wall and interfacial shear stresses obtained previously for laminar stratified flow, are extended to be applicable also to turbulent flows in either or both of the phases. The closure relations... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_21151422" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The theory-based closure relations for the wall and interfacial shear stresses obtained previously for laminar stratified flow, are extended to be applicable also to turbulent flows in either or both of the phases. The closure relations are formulated in terms of the single-phase-based expressions, which are augmented by two-phase interaction factors, due to the flow of the two phases in the same channel. These closure relations, which are valid for smooth stratified flow in horizontal or inclined pipes, were used as a platform for introducing necessary empirical corrections required in the stratified wavy flow regime. Based on experimental data available from the literature, new empirical correlations for the wave effect on the interface curvature, on the interfacial shear and on the liquid wall shear were obtained. The predictions of the two-fluid model for the pressure gradient and holdup are tested against extensive data bank and some analytical solutions for stratified flows. 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The closure relations are formulated in terms of the single-phase-based expressions, which are augmented by two-phase interaction factors, due to the flow of the two phases in the same channel. These closure relations, which are valid for smooth stratified flow in horizontal or inclined pipes, were used as a platform for introducing necessary empirical corrections required in the stratified wavy flow regime. Based on experimental data available from the literature, new empirical correlations for the wave effect on the interface curvature, on the interfacial shear and on the liquid wall shear were obtained. The predictions of the two-fluid model for the pressure gradient and holdup are tested against extensive data bank and some analytical solutions for stratified flows. 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itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/53531019/Rheology_of_non_Newtonian_glass_forming_melts">Rheology of non-Newtonian glass-forming melts</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/53531019" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="bb5f57955b2dd83b099818e0e6ba0cda" rel="nofollow" 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Schmelzer","profile_url":"https://independent.academia.edu/JSchmelzer?f_ri=1120502","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1120502","nofollow":false},{"id":511,"name":"Materials Science","url":"https://www.academia.edu/Documents/in/Materials_Science?f_ri=1120502","nofollow":false},{"id":4987,"name":"Kinetics","url":"https://www.academia.edu/Documents/in/Kinetics?f_ri=1120502","nofollow":false},{"id":32927,"name":"Molecular modeling","url":"https://www.academia.edu/Documents/in/Molecular_modeling?f_ri=1120502","nofollow":false},{"id":260118,"name":"CHEMICAL SCIENCES","url":"https://www.academia.edu/Documents/in/CHEMICAL_SCIENCES?f_ri=1120502"},{"id":394477,"name":"Time Dependent","url":"https://www.academia.edu/Documents/in/Time_Dependent?f_ri=1120502"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1120502"},{"id":3423483,"name":"Relaxation Time","url":"https://www.academia.edu/Documents/in/Relaxation_Time?f_ri=1120502"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34637318" data-work_id="34637318" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/34637318/Damage_evolution_and_infrared_thermography_in_woven_composite_laminates_under_fatigue_loading">Damage evolution and infrared thermography in woven composite laminates under fatigue loading</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34637318" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c137e1530c1abd7a8e2f0f33959c0a02" rel="nofollow" data-download="{"attachment_id":54498757,"asset_id":34637318,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54498757/download_file?st=MTczMjczODAxNSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="48974733" href="https://independent.academia.edu/LToubal">L. 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Toubal","profile_url":"https://independent.academia.edu/LToubal?f_ri=1120502","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=1120502","nofollow":false},{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering?f_ri=1120502","nofollow":false},{"id":4496,"name":"Composites","url":"https://www.academia.edu/Documents/in/Composites?f_ri=1120502","nofollow":false},{"id":23076,"name":"Fatigue","url":"https://www.academia.edu/Documents/in/Fatigue?f_ri=1120502","nofollow":false},{"id":169323,"name":"Composite Material","url":"https://www.academia.edu/Documents/in/Composite_Material?f_ri=1120502"},{"id":413300,"name":"Analytical Model","url":"https://www.academia.edu/Documents/in/Analytical_Model?f_ri=1120502"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1120502"},{"id":1993786,"name":"Cumulant","url":"https://www.academia.edu/Documents/in/Cumulant?f_ri=1120502"},{"id":2204428,"name":"Infra red","url":"https://www.academia.edu/Documents/in/Infra_red?f_ri=1120502"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1634358 coauthored" data-work_id="1634358" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1634358/Bayesian_Finite_Element_Model_Updating_Using_Static_and_Dynamic_Data">Bayesian Finite Element Model Updating Using Static and Dynamic Data</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Finite element models of current structures often behave differently than the structure itself. Model updating techniques are used to enhance the capabilities of the numerical model such that it behaves like the real structure.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1634358" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Finite element models of current structures often behave differently than the structure itself. Model updating techniques are used to enhance the capabilities of the numerical model such that it behaves like the real structure. Experimental data is used in model updating techniques to identify the parameters of the numerical model. In civil infrastructure these model updating techniques use either static or dynamic measurements, separately. This paper studies how a Bayesian updating framework behaves when both static and dynamic data are used to updated the model. Displacements at specific structure locations are obtained for static tests using a computer vision method. High density mode shapes and natural frequencies are obtained using a moving accelerometer structure. The static data and the modal characteristics are combined in a Bayesian modal updating technique that accounts for the incompleteness and uncertainty of the data as well as the possible nonuniqueness of the solution. Results show how the posterior probability density function changes when different type of information is included for updating.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1634358" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="67018d104a1849a1be90f005bd860e0c" rel="nofollow" data-download="{"attachment_id":16906705,"asset_id":1634358,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/16906705/download_file?st=MTczMjczODAxNSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1051335" href="https://sc.academia.edu/BorisZ%C3%A1rate">Boris Zárate</a><script data-card-contents-for-user="1051335" type="text/json">{"id":1051335,"first_name":"Boris","last_name":"Zárate","domain_name":"sc","page_name":"BorisZárate","display_name":"Boris Zárate","profile_url":"https://sc.academia.edu/BorisZ%C3%A1rate?f_ri=1120502","photo":"https://0.academia-photos.com/1051335/362374/36043783/s65_boris.z_rate.jpeg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-1634358">+1</span><div class="hidden js-additional-users-1634358"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://universidaddelvallecolombia.academia.edu/JohannioMarulanda">Johannio Marulanda</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-1634358'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-1634358').html(); 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Model updating techniques are used to enhance the capabilities of the numerical model such that it behaves like the real structure. Experimental data is used in model updating techniques to identify the parameters of the numerical model. In civil infrastructure these model updating techniques use either static or dynamic measurements, separately. This paper studies how a Bayesian updating framework behaves when both static and dynamic data are used to updated the model. Displacements at specific structure locations are obtained for static tests using a computer vision method. High density mode shapes and natural frequencies are obtained using a moving accelerometer structure. The static data and the modal characteristics are combined in a Bayesian modal updating technique that accounts for the incompleteness and uncertainty of the data as well as the possible nonuniqueness of the solution. Results show how the posterior probability density function changes when different type of information is included for updating.","downloadable_attachments":[{"id":16906705,"asset_id":1634358,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1051335,"first_name":"Boris","last_name":"Zárate","domain_name":"sc","page_name":"BorisZárate","display_name":"Boris Zárate","profile_url":"https://sc.academia.edu/BorisZ%C3%A1rate?f_ri=1120502","photo":"https://0.academia-photos.com/1051335/362374/36043783/s65_boris.z_rate.jpeg"},{"id":6365464,"first_name":"Johannio","last_name":"Marulanda","domain_name":"universidaddelvallecolombia","page_name":"JohannioMarulanda","display_name":"Johannio Marulanda","profile_url":"https://universidaddelvallecolombia.academia.edu/JohannioMarulanda?f_ri=1120502","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=1120502","nofollow":false},{"id":26245,"name":"Model Updating","url":"https://www.academia.edu/Documents/in/Model_Updating?f_ri=1120502","nofollow":false},{"id":146633,"name":"Natural Frequency","url":"https://www.academia.edu/Documents/in/Natural_Frequency?f_ri=1120502","nofollow":false},{"id":338950,"name":"Dynamic Panel Data","url":"https://www.academia.edu/Documents/in/Dynamic_Panel_Data?f_ri=1120502","nofollow":false},{"id":386557,"name":"Finite Element Model","url":"https://www.academia.edu/Documents/in/Finite_Element_Model?f_ri=1120502"},{"id":497452,"name":"Numerical Model","url":"https://www.academia.edu/Documents/in/Numerical_Model?f_ri=1120502"},{"id":620661,"name":"High Density Concrete","url":"https://www.academia.edu/Documents/in/High_Density_Concrete?f_ri=1120502"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1120502"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4253070" data-work_id="4253070" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4253070/A_spectral_index_to_monitor_the_head_emergence_of_wheat_in_semi_arid_conditions">A spectral index to monitor the head-emergence of wheat in semi-arid conditions</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4253070" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" 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href="https://www.academia.edu/Documents/in/Decision_Making">Decision Making</a>, <script data-card-contents-for-ri="1681" type="text/json">{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=1120502","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34960" href="https://www.academia.edu/Documents/in/Emergence">Emergence</a>, <script data-card-contents-for-ri="34960" type="text/json">{"id":34960,"name":"Emergence","url":"https://www.academia.edu/Documents/in/Emergence?f_ri=1120502","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="45874" href="https://www.academia.edu/Documents/in/Decision_support_system">Decision support system</a><script data-card-contents-for-ri="45874" type="text/json">{"id":45874,"name":"Decision support system","url":"https://www.academia.edu/Documents/in/Decision_support_system?f_ri=1120502","nofollow":false}</script></span></li><script>(function(){ if 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Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing?f_ri=1120502","nofollow":false},{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=1120502","nofollow":false},{"id":34960,"name":"Emergence","url":"https://www.academia.edu/Documents/in/Emergence?f_ri=1120502","nofollow":false},{"id":45874,"name":"Decision support system","url":"https://www.academia.edu/Documents/in/Decision_support_system?f_ri=1120502","nofollow":false},{"id":57433,"name":"Seasonality","url":"https://www.academia.edu/Documents/in/Seasonality?f_ri=1120502"},{"id":87479,"name":"Field Crops","url":"https://www.academia.edu/Documents/in/Field_Crops?f_ri=1120502"},{"id":98134,"name":"United States","url":"https://www.academia.edu/Documents/in/United_States?f_ri=1120502"},{"id":192551,"name":"Nutritional Status","url":"https://www.academia.edu/Documents/in/Nutritional_Status?f_ri=1120502"},{"id":208877,"name":"Satellite Imagery","url":"https://www.academia.edu/Documents/in/Satellite_Imagery?f_ri=1120502"},{"id":217324,"name":"Soil sciences","url":"https://www.academia.edu/Documents/in/Soil_sciences?f_ri=1120502"},{"id":242028,"name":"Water Absorption","url":"https://www.academia.edu/Documents/in/Water_Absorption?f_ri=1120502"},{"id":402722,"name":"Vegetation Index","url":"https://www.academia.edu/Documents/in/Vegetation_Index?f_ri=1120502"},{"id":440529,"name":"Semi Arid Region","url":"https://www.academia.edu/Documents/in/Semi_Arid_Region?f_ri=1120502"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=1120502"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1120502"},{"id":1425602,"name":"Classification Accuracy","url":"https://www.academia.edu/Documents/in/Classification_Accuracy?f_ri=1120502"},{"id":2016586,"name":"Advanced Land Imager","url":"https://www.academia.edu/Documents/in/Advanced_Land_Imager?f_ri=1120502"},{"id":2163839,"name":"Growing Season","url":"https://www.academia.edu/Documents/in/Growing_Season?f_ri=1120502"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29074563" data-work_id="29074563" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29074563/Comparison_of_experimental_and_Monte_Carlo_results_of_stochastic_Coulomb_interaction_in_projection_beam_lithography">Comparison of experimental and Monte Carlo results of stochastic Coulomb interaction in projection beam lithography</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div 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prediction of tunnelling induced ground movements in urban area</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The main goal of this specific study is to reveal the benefits from the use of the ANN in geotechnical engineering by focusing on the elaboration of data coming from real field measurements concerning ground movements due to tunnelling.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_26346947" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The main goal of this specific study is to reveal the benefits from the use of the ANN in geotechnical engineering by focusing on the elaboration of data coming from real field measurements concerning ground movements due to tunnelling. Moreover, the Back Propagation algorithm is used so as to reveal the hierarchy of each of the parameters mainly affecting the total amount of the induced settlements due to tunnelling. The short review of our experience in applying ANN&#39;s in Geotechnical Engineering is succeeded by the main accomplishments of this study. The provided information, originally published by Attewell, concerns tunnel size and depth, maximum settlement, settlement trough width, volume and slope, ground description, geological properties and excavation method.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/26346947" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="939af5e04a40d70313d373b08d77ac8c" rel="nofollow" data-download="{"attachment_id":46655110,"asset_id":26346947,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46655110/download_file?st=MTczMjczODAxNSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="50516421" href="https://independent.academia.edu/AikKapsampeli">Aik Kapsampeli</a><script data-card-contents-for-user="50516421" type="text/json">{"id":50516421,"first_name":"Aik","last_name":"Kapsampeli","domain_name":"independent","page_name":"AikKapsampeli","display_name":"Aik Kapsampeli","profile_url":"https://independent.academia.edu/AikKapsampeli?f_ri=1120502","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-26346947">+1</span><div class="hidden js-additional-users-26346947"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://ntua.academia.edu/MSakellariou">Michael Sakellariou</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-26346947'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-26346947').html(); 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The provided information, originally published by Attewell, concerns tunnel size and depth, maximum settlement, settlement trough width, volume and slope, ground description, geological properties and excavation method.","downloadable_attachments":[{"id":46655110,"asset_id":26346947,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":50516421,"first_name":"Aik","last_name":"Kapsampeli","domain_name":"independent","page_name":"AikKapsampeli","display_name":"Aik Kapsampeli","profile_url":"https://independent.academia.edu/AikKapsampeli?f_ri=1120502","photo":"/images/s65_no_pic.png"},{"id":5586242,"first_name":"Michael","last_name":"Sakellariou","domain_name":"ntua","page_name":"MSakellariou","display_name":"Michael Sakellariou","profile_url":"https://ntua.academia.edu/MSakellariou?f_ri=1120502","photo":"https://0.academia-photos.com/5586242/5984469/16909177/s65_michael.sakellariou.jpg"}],"research_interests":[{"id":32994,"name":"Analytic Network 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In the particular case of bubbly... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12351937" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In two-phase flow studies, a volumetric interfacial area balance equation is often used in addition to the multidimensional two-fluid model to describe the geometrical structure of the two-phase flow. 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Preference of Three Cycloalkanols when Included in β-Cyclodextrin</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/79540871" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="55c91366d9f1c2b7f5277a96e1b020c6" rel="nofollow" data-download="{"attachment_id":86221207,"asset_id":79540871,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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β-Cyclodextrin","created_at":"2022-05-20T13:27:40.379-07:00","url":"https://www.academia.edu/79540871/Experimental_NMR_and_Theoretical_MD_Simulations_Studies_on_the_Conformational_Preference_of_Three_Cycloalkanols_when_Included_in_%CE%B2_Cyclodextrin?f_ri=1120502","dom_id":"work_79540871","summary":null,"downloadable_attachments":[{"id":86221207,"asset_id":79540871,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":57102530,"first_name":"Ivan","last_name":"Beà","domain_name":"independent","page_name":"IvanBeà","display_name":"Ivan Beà","profile_url":"https://independent.academia.edu/IvanBe%C3%A0?f_ri=1120502","photo":"https://0.academia-photos.com/57102530/14996374/42221265/s65_ivan.be_.jpg"}],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry?f_ri=1120502","nofollow":false},{"id":6156,"name":"Inclusion","url":"https://www.academia.edu/Documents/in/Inclusion?f_ri=1120502","nofollow":false},{"id":260118,"name":"CHEMICAL SCIENCES","url":"https://www.academia.edu/Documents/in/CHEMICAL_SCIENCES?f_ri=1120502","nofollow":false},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1120502","nofollow":false},{"id":1129256,"name":"MD Simulation","url":"https://www.academia.edu/Documents/in/MD_Simulation?f_ri=1120502"},{"id":1242504,"name":"Molecular Dynamic Simulation","url":"https://www.academia.edu/Documents/in/Molecular_Dynamic_Simulation?f_ri=1120502"},{"id":1636662,"name":"Coupling 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href="https://www.academia.edu/Documents/in/Methanol">Methanol</a><script data-card-contents-for-ri="112334" type="text/json">{"id":112334,"name":"Methanol","url":"https://www.academia.edu/Documents/in/Methanol?f_ri=1120502","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=77391098]'), work: {"id":77391098,"title":"Thermodynamic properties of the mixture acetone + methanol + n-octane at 25°C","created_at":"2022-04-23T13:38:15.730-07:00","url":"https://www.academia.edu/77391098/Thermodynamic_properties_of_the_mixture_acetone_methanol_n_octane_at_25_C?f_ri=1120502","dom_id":"work_77391098","summary":null,"downloadable_attachments":[{"id":84770156,"asset_id":77391098,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":221674003,"first_name":"miguel","last_name":"iglesias","domain_name":"independent","page_name":"migueliglesias42","display_name":"miguel iglesias","profile_url":"https://independent.academia.edu/migueliglesias42?f_ri=1120502","photo":"https://0.academia-photos.com/221674003/79419552/67984757/s65_miguel.iglesias.jpeg"}],"research_interests":[{"id":9355,"name":"Equations of State","url":"https://www.academia.edu/Documents/in/Equations_of_State?f_ri=1120502","nofollow":false},{"id":17726,"name":"Density","url":"https://www.academia.edu/Documents/in/Density?f_ri=1120502","nofollow":false},{"id":35828,"name":"Solution Chemistry","url":"https://www.academia.edu/Documents/in/Solution_Chemistry?f_ri=1120502","nofollow":false},{"id":112334,"name":"Methanol","url":"https://www.academia.edu/Documents/in/Methanol?f_ri=1120502","nofollow":false},{"id":151141,"name":"Refractive Index","url":"https://www.academia.edu/Documents/in/Refractive_Index?f_ri=1120502"},{"id":163342,"name":"Molar 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itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/70167920/Investigation_of_graphite_oxidation_kinetics_in_MgO_C_composite_via_artificial_neural_network_approach">Investigation of graphite oxidation kinetics in MgO–C composite via artificial neural network approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this study an artificial neural network (ANN) model was developed to predict the oxidation behavior of magnesia graphite composites. After mechanism evaluation in different conditions, the kinetic parameters such as effective diffusion... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_70167920" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this study an artificial neural network (ANN) model was developed to predict the oxidation behavior of magnesia graphite composites. After mechanism evaluation in different conditions, the kinetic parameters such as effective diffusion coefficient and diffusion activation energy of oxidation were calculated from ANN predicted results at different graphite content. The obtained mechanism and kinetic parameters were compared with experimental data.First of all, the reliability of the model was checked with different available data. It was found that the model results were in good agreement with experimental data prediction.The results showed that the main mechanism of oxidation was pore diffusion and effective diffusion coefficient as well as diffusion activation energy were comparable with previous works.Effective diffusion coefficient and diffusion activation energy which were calculated versus graphite content are in good agreement with experimental values.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/70167920" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="50683556" href="https://independent.academia.edu/ZiaratNemati">Ziarat Nemati</a><script data-card-contents-for-user="50683556" type="text/json">{"id":50683556,"first_name":"Ziarat","last_name":"Nemati","domain_name":"independent","page_name":"ZiaratNemati","display_name":"Ziarat Nemati","profile_url":"https://independent.academia.edu/ZiaratNemati?f_ri=1120502","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_70167920 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="70167920"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 70167920, container: ".js-paper-rank-work_70167920", }); 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After mechanism evaluation in different conditions, the kinetic parameters such as effective diffusion coefficient and diffusion activation energy of oxidation were calculated from ANN predicted results at different graphite content. The obtained mechanism and kinetic parameters were compared with experimental data.First of all, the reliability of the model was checked with different available data. It was found that the model results were in good agreement with experimental data prediction.The results showed that the main mechanism of oxidation was pore diffusion and effective diffusion coefficient as well as diffusion activation energy were comparable with previous works.Effective diffusion coefficient and diffusion activation energy which were calculated versus graphite content are in good agreement with experimental values.","downloadable_attachments":[],"ordered_authors":[{"id":50683556,"first_name":"Ziarat","last_name":"Nemati","domain_name":"independent","page_name":"ZiaratNemati","display_name":"Ziarat Nemati","profile_url":"https://independent.academia.edu/ZiaratNemati?f_ri=1120502","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":56,"name":"Materials Engineering","url":"https://www.academia.edu/Documents/in/Materials_Engineering?f_ri=1120502","nofollow":false},{"id":505,"name":"Condensed Matter Physics","url":"https://www.academia.edu/Documents/in/Condensed_Matter_Physics?f_ri=1120502","nofollow":false},{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry?f_ri=1120502","nofollow":false},{"id":2430,"name":"Computational Materials Science","url":"https://www.academia.edu/Documents/in/Computational_Materials_Science?f_ri=1120502","nofollow":false},{"id":4987,"name":"Kinetics","url":"https://www.academia.edu/Documents/in/Kinetics?f_ri=1120502"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=1120502"},{"id":12502,"name":"Composite Materials","url":"https://www.academia.edu/Documents/in/Composite_Materials?f_ri=1120502"},{"id":83315,"name":"Diffusion","url":"https://www.academia.edu/Documents/in/Diffusion?f_ri=1120502"},{"id":147640,"name":"Activation Energy","url":"https://www.academia.edu/Documents/in/Activation_Energy?f_ri=1120502"},{"id":309493,"name":"Diffusion Coefficient","url":"https://www.academia.edu/Documents/in/Diffusion_Coefficient?f_ri=1120502"},{"id":563382,"name":"Oxidation","url":"https://www.academia.edu/Documents/in/Oxidation?f_ri=1120502"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1120502"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network?f_ri=1120502"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_64455467" data-work_id="64455467" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/64455467/Many_scale_molecular_simulation_for_ABS_MMT_nanocomposites_Upgrading_of_industrial_scraps">Many-scale molecular simulation for ABS-MMT nanocomposites: Upgrading of industrial scraps</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this work, we simulated modified organoclay exfoliation and intercalation at an atomistic level, thus providing information about the rational choice of compatibilizers and polymer insertions behavior. Phase morphology and segregation... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_64455467" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this work, we simulated modified organoclay exfoliation and intercalation at an atomistic level, thus providing information about the rational choice of compatibilizers and polymer insertions behavior. Phase morphology and segregation domains have then been obtained ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/64455467" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4b9ea9e587a3dc1d814f48675798e950" rel="nofollow" data-download="{"attachment_id":76485425,"asset_id":64455467,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/76485425/download_file?st=MTczMjczODAxNSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="437780" href="https://units.academia.edu/MaurizioFermeglia">Maurizio Fermeglia</a><script data-card-contents-for-user="437780" type="text/json">{"id":437780,"first_name":"Maurizio","last_name":"Fermeglia","domain_name":"units","page_name":"MaurizioFermeglia","display_name":"Maurizio Fermeglia","profile_url":"https://units.academia.edu/MaurizioFermeglia?f_ri=1120502","photo":"https://0.academia-photos.com/437780/139882/162246/s65_maurizio.fermeglia.jpg"}</script></span></span></li><li class="js-paper-rank-work_64455467 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="64455467"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 64455467, container: ".js-paper-rank-work_64455467", }); 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Phase morphology and segregation domains have then been obtained ...","downloadable_attachments":[{"id":76485425,"asset_id":64455467,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":437780,"first_name":"Maurizio","last_name":"Fermeglia","domain_name":"units","page_name":"MaurizioFermeglia","display_name":"Maurizio Fermeglia","profile_url":"https://units.academia.edu/MaurizioFermeglia?f_ri=1120502","photo":"https://0.academia-photos.com/437780/139882/162246/s65_maurizio.fermeglia.jpg"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1120502","nofollow":false},{"id":511,"name":"Materials Science","url":"https://www.academia.edu/Documents/in/Materials_Science?f_ri=1120502","nofollow":false},{"id":5645,"name":"Recycling","url":"https://www.academia.edu/Documents/in/Recycling?f_ri=1120502","nofollow":false},{"id":8208,"name":"Rational 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Blend","url":"https://www.academia.edu/Documents/in/Polymer_Blend?f_ri=1120502"},{"id":865728,"name":"ACRYLONITRILE BUTADIENE STYRENE","url":"https://www.academia.edu/Documents/in/ACRYLONITRILE_BUTADIENE_STYRENE?f_ri=1120502"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data?f_ri=1120502"},{"id":2724508,"name":"Mechanical Property","url":"https://www.academia.edu/Documents/in/Mechanical_Property?f_ri=1120502"},{"id":2846082,"name":"Simulation Technique","url":"https://www.academia.edu/Documents/in/Simulation_Technique?f_ri=1120502"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_63736562" data-work_id="63736562" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/63736562/Predictive_pharmacokinetic_pharmacodynamic_modeling_of_tumor_growth_kinetics_in_xenograft_models_after_administration_of_anticancer_agents">Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/63736562" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b9e10e7047c6516bb281bffca91c52a7" rel="nofollow" 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type="text/json">{"id":6021,"name":"Cancer","url":"https://www.academia.edu/Documents/in/Cancer?f_ri=1120502","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="6177" href="https://www.academia.edu/Documents/in/Modeling">Modeling</a>, <script data-card-contents-for-ri="6177" type="text/json">{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=1120502","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="38650" href="https://www.academia.edu/Documents/in/Cell_Division">Cell Division</a><script data-card-contents-for-ri="38650" type="text/json">{"id":38650,"name":"Cell Division","url":"https://www.academia.edu/Documents/in/Cell_Division?f_ri=1120502","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=63736562]'), work: {"id":63736562,"title":"Predictive pharmacokinetic-pharmacodynamic modeling of tumor 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