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| 1 | A numerical simulation study of CO2 injection for enhancing hydrocarbon recovery and sequestration in liquid-rich shales显示文摘Less than 10% of oil is usually recovered from liquid-rich shales and this leaves much room for improvement, while water injection into shale formation is virtually impossible because of the extremely low permeability of the formation matrix. Injecting carbon dioxide(CO_2) into oil shale formations can potentially improve oil recovery. Furthermore, the large surface area in organicrich shale could permanently store CO_2 without jeopardizing the formation integrity. This work is a mechanism study of evaluating the effectiveness of CO_2-enhanced oil shale recovery and shale formation CO_2 sequestration capacity using numerical simulation. Petrophysical and fluid properties similar to the Bakken Formation are used to set up the base model for simulation. Result shows that the CO_2 injection could increase the oil recovery factor from7.4% to 53%. In addition, petrophysical characteristics such as in situ stress changes and presence of a natural fracture network in the shale formation are proven to have impacts on subsurface CO_2 flow. A response surface modeling approach was applied to investigate the interaction between parameters and generate a proxy model for optimizing oil recovery and CO_2 injectivity. | Sumeer Kalra Wei Tian Xingru Wu | 2018 | Petroleum Science2018，15，1： | 4 |
| 2 | Transnasal Esophagoscopy: Revisited (over 700 Consecutive Cases)显示文摘 | Gregory N.Postma Jacob T.Cohen Peter C.Belafsky Stacey L.Halum Sumeer K.Gupta Kevin K.Bach Jamie A.Koufman | 2009 | The Laryngoscope2009，，2： | 1 |
| 3 | Neuro-glial hamartoma of the suprasellar cistern显示文摘 | Borlis NM Sumeer S Schwartz R | 1994 | Surg Neurol1994，41，： | 1 |
| 4 | The After Tax Rate of Return Affects Private Savings显示文摘 | | 1984 | NBER Working Paper1984，，5： | 1 |
| 5 | Neuro-glial hamar-toma of the suprasellar cistern显示文摘 | Borlis NM Sumeer S Schwartz R | 1994 | Surg Neurol1994，41，： | 1 |
| 6 | Sorption of heavy metals from synthetic metal solutions and industrial wastewater using plant materials显示文摘 | SUMEER A A ZDRA VKO D | 1999 | Water Quality Research Journal of Canada1999，34，3： | 1 |
| 7 | High-speed-Motion Estimation Architecture for Real-time Video Trans-mission显示文摘 | Goel Sumeer Ismail Yasser Bayoumi Magdy | 2012 | The Computer Journal2012，55，1： | 1 |
| 8 | 新一年的数字印刷将走向何方?显示文摘虽然传统印刷市场面临持续挑战,但新的市场机遇正在不断涌现,使得印刷在当今信息大融合中仍占有一席之地。 | Sumeer Chandra | 2013 | 数码印刷2013，，2： | 0 |
| 9 | LIBRA:an adaptative integrative tool for paired single-cell multi-omics data显示文摘Background:Single-cell multi-omics technologies allow a profound system-level biology understanding of cells and tissues.However,an integrative and possibly systems-based analysis capturing the different modalities is challenging.In response,bioinformatics and machine learning methodologies are being developed for multi-omics single-cell analysis.It is unclear whether current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter fine-tuning.Methods:We designed LIBRA,a neural network based framework,to learn translation between paired multi-omics profiles so that a shared latent space is constructed.Additionally,we implemented a variation,aLIBRA,that allows automatic fine-tuning by identifying parameter combinations that optimize both the integrative and predictive tasks.All model parameters and evaluation metrics are made available to users with minimal user iteration.Furthermore,aLIBRA allows experienced users to implement custom configurations.The LIBRA toolbox is freely available as R and Python libraries at GitHub(TranslationalBioinformaticsUnit/LIBRA).Results:LIBRA was evaluated in eight multi-omic single-cell data-sets,including three combinations of omics.We observed that LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type(clustering)resolution in the integrated latent space.Furthermore,when assessing the predictive power across data modalities,such as predictive chromatin accessibility from gene expression,LIBRA outperforms existing tools.As expected,adaptive parameter optimization(aLIBRA)significantly boosted the performance of learning predictive models from paired data-sets.Conclusion:LIBRA is a versatile tool that performs competitively in both“integration”and“prediction”tasks based on single-cell multi-omics data.LIBRA is a data-driven robust platform that includes an adaptive learning scheme. | Xabier Martinez-de-Morentin Sumeer AKhan Robert Lehmann Sisi Qu Alberto Maillo Narsis AKiani Felipe Prosper Jesper Tegner David Gomez-Cabrero | 2023 | Quantitative Biology2023，11，3： | 0 |
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