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| 1 | Biocatalytic solvent-free polymerization to producehigh molecular weight polyesters 显示文摘 | Apurva K Chaudhary Jesus Lopez Eric J Beckman Alan JRussell | 1997 | BiotechnologyProgress1997，13，3： | 1 |
| 2 | 2010 ACCF/AHA/AATS/ACR/ASA/SCA/SCAI/SIR/STS/SVM Guidelines for the Diagnosis and Management of Patients With Thoracic Aortic Disease显示文摘 | Loren F. Hiratzka George L. Bakris Joshua A. Beckman Robert M. Bersin Vincent F. Carr Donald E. Casey Kim A. Eagle Luke K. Hermann Eric M. Isselbacher Ella A. Kazerooni Nicholas T. Kouchoukos Bruce W. Lytle Dianna M. Milewicz David L. Reich Souvik Sen Jul | 2010 | Journal of the American College of Cardiology2010，，14： | 1 |
| 3 | One-stepbiocatalytic synthesis of linear polyesters with hydroxylgroups 显示文摘 | Billie J Kline Eric J Beckman Alan J Russell | 1998 | American Chemical Society1998，120，37： | 1 |
| 4 | One-pot green synthesis of propylene oxide using in situ generated hydrogen peroxide in carbon dioxide显示文摘 | Chen Qunlai Eric J Beckman | 2008 | Green Chemistry2008，10，： | 1 |
| 5 | Making Polymers from Carbon Dioxide显示文摘 | Eric J Beckman | 1999 | Science1999，283，： | 1 |
| 6 | Generation of Microcellular Polyurethane Foams via Polymerization in Carbon Dioxide II Foam Formation and Characterization 显示文摘 | Parks L Kristen Beckman J Eric | 1996 | Polymer Engineering and Science1996，36，19： | 1 |
| 7 | A New Peptide-based Urethane Polymer: Synthesis, Biodegradation and Potential to Support Cell Growth in Vitro 显示文摘 | Zhang Jian Ying Eric J Beckman Nicholas P Piesco | 2000 | Biomaterials2000，，21： | 1 |
| 8 | 2010 ACCF/AHA/AATS/ACR/ASA/SCA/SCAI/SIR/STS/SVM Guidelines for the Diagnosis and Management of Patients With Thoracic Aortic Disease显示文摘 | Loren F. Hiratzka George L. Bakris Joshua A. Beckman Robert M. Bersin Vincent F. Carr Donald E. Casey Kim A. Eagle Luke K. Hermann Eric M. Isselbacher Ella A. Kazerooni Nicholas T. Kouchoukos Bruce W. Lytle Dianna M. Milewicz David L. Reich Souvik Sen Jul | 2010 | Journal of the American College of Cardiology2010，，14： | 1 |
| 9 | Direct synthesis of propylene oxide with CO2 as the solvent显示文摘 | Tiberiu Danciu Eric J Beckman Dan Hancu etc | 2003 | Angew Chem Int Ed2003，42，10： | 1 |
| 10 | Supercritical and near-critical CO2 in green chemical synthesis and processing 显示文摘 | Eric J Beckman | 2004 | Supercri Fluids2004，28，23： | 1 |
| 11 | Direct synthesis of H2O2 from O2 and H2 over precioux metal loaded TS-1 in CO2显示文摘 | Chen Qunlai Beckman Eric J | 2007 | Green Chemistry2007，9，： | 1 |
| 12 | 2010 ACCF/AHA/AATS/ACR/ASA/SCA/SCAI/SIR/STS/SVM Guidelines for the Diagnosis and Management of Patients With Thoracic Aortic Disease显示文摘 | Loren F. Hiratzka George L. Bakris Joshua A. Beckman Robert M. Bersin Vincent F. Carr Donald E. Casey Kim A. Eagle Luke K. Hermann Eric M. Isselbacher Ella A. Kazerooni Nicholas T. Kouchoukos Bruce W. Lytle Dianna M. Milewicz David L. Reich Souvik Sen Jul | 2010 | Journal of the American College of Cardiology2010，，14： | 1 |
| 13 | A new peptide-based urethane polymer: synthesis, biodegradation, and potential to support cell growth in vitro 显示文摘 | Jian Ying Zhang Eric J Beckman Nicholas P Piesco | 2000 | Biomaterials2000，21，： | 1 |
| 14 | Supercritical and near-critical CO2 in green chemical synthesis and processing 显示文摘 | Eric J Beckman | 2004 | The Journal of Supercritieal Fluids2004，28，23： | 1 |
| 15 | An Improved Deep Fusion CNN for Image Recognition显示文摘With the development of Deep Convolutional Neural Networks(DCNNs),the extracted features for image recognition tasks have shifted from low-level features to the high-level semantic features of DCNNs.Previous studies have shown that the deeper the network is,the more abstract the features are.However,the recognition ability of deep features would be limited by insufficient training samples.To address this problem,this paper derives an improved Deep Fusion Convolutional Neural Network(DF-Net)which can make full use of the differences and complementarities during network learning and enhance feature expression under the condition of limited datasets.Specifically,DF-Net organizes two identical subnets to extract features from the input image in parallel,and then a well-designed fusion module is introduced to the deep layer of DF-Net to fuse the subnet’s features in multi-scale.Thus,the more complex mappings are created and the more abundant and accurate fusion features can be extracted to improve recognition accuracy.Furthermore,a corresponding training strategy is also proposed to speed up the convergence and reduce the computation overhead of network training.Finally,DF-Nets based on the well-known ResNet,DenseNet and MobileNetV2 are evaluated on CIFAR100,Stanford Dogs,and UECFOOD-100.Theoretical analysis and experimental results strongly demonstrate that DF-Net enhances the performance of DCNNs and increases the accuracy of image recognition. | Rongyu Chen Lili Pan Cong Li Yan Zhou Aibin Chen Eric Beckman | 2020 | Computers, Materials & Continua2020，，11： | 0 |
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