Fabrication and Cross-linking of L-Aspartic Acid Functionalized Poly(ester amide)-based Tissue Engineering Scaffolds

Scaffold fabrication is essential in tissue engineering. Amino acid based poly(ester amide)s (PEAs) have been investigated as scaffold materials due to their non-toxic degradation byproducts, and easily tunable mechanical and biological properties. However, L-aspartic acid functionalized PEAs showed poor morphological stability when immersed in buffer. This work focuses on the further functionalization of L-aspartic acid based PEAs, scaffold fabrication and cross-linking of the scaffold to improve morphological stability. Photo-cross-linkable and thermally cross-linkable PEAs were synthesized successfully and characterized. Two scaffold fabrication methods were applied: 1) electrospinning was applied to photo-cross-linkable PEAs, followed by UV treatment to cross-link electrospun scaffolds; 2) solvent casting/particulate leaching was applied to thermally cross-linkable PEAs with in situ thermal cross-linking. The cross-linking degree was demonstrated with morphological change by immersing the cross-linked scaffold in phosphate-buffered saline. Photo-cross-linking turned out to be insufficient to produce scaffolds that were able to survive in aqueous environment, while in situ thermal cross-linking provided highly-cross-linked scaffold. The existence and use of residual carboxylic groups on scaffolds prepared by both electrospinning was proven by model protein conjugation. Confocal microscopy imaging showed stronger fluorescence on conjugated samples than adsorbed samples

Technology transition from traditional oil and gas reservoir simulation to the next generation energy development

Energy transition has been a focus in both scientific research and social concerns in the past decade, thanks to the urgent need of reducing carbon emissions, slowing down the abnormal speed of global climate and achieving a balance between environmental protection and economic development. Although the global energy sector is shifting from the fossil-based energy systems, including oil and gas, to the renewable energy resources like hydrogen, the necessity of conventional energy development has received increasing attentions with regard to the stable supply and maturely developed technologies. The long-history simulation techniques developed for oil and gas reservoir investigations have enabled the deeper explorations into reservoir properties and enhanced significantly the resource recovery. As a main direction in energy transition, the development of hydrogen energy is profoundly influencing the long-term reconstruction of the world’s energy supply and application system, and is accelerating the transition and generational evolution in the fields of transportation, power generation, chemicals, and housing. In this paper, three research directions are proposed as the potential focus of technology transition, where traditional oil and gas reservoir simulation technologies can be adjusted and improved to be used to benefit the development of hydrogen energy.Cited as: Zhang, T., Liu, J., Sun, S. Technology transition from traditional oil and gas reservoir simulation to the next generation energy development. Advances in Geo-Energy Research, 2023, 7(1): 69-70. https://doi.org/10.46690/ager.2023.01.0

Stability analysis of the water bridge in organic shale nanopores: A molecular dynamic study

In the last decades, shale gas development has relieved the global energy crisis and slowed global warming problems. The water bridge plays an important role in the process of shale gas diffusion, but the stability of the water bridge in the shale nanochannel has not been revealed. In this work, the molecular dynamics method is applied to study the interaction between shale gas and water bridge, and the stability can be tested accordingly. CO2 can diffuse into the liquid H2O phase, but CH4 only diffuses at the boundary of the H2O phase. Due to the polarity of H2O molecules, the water bridge presents the wetting condition according to model snapshots and one-dimensional analyses, but the main body of the water bridge in the two-dimensional contour shows the non-wetting condition, which is reasonable. Due to the effect of the molecular polarity, CO2 prefers to diffuse into kerogen matrixes and the bulk phase of water bridge. In the bulk of the water bridge, where the interaction is weaker, CO2 has a lower energy state, implies that it has a good solubility in the liquid H2O phase. Higher temperature does not facilitate the diffusion of CO2 molecules, and higher pressure brings more CO2 molecules and enhances the solubility of CO2 in the H2O phase, in addition, a larger ratio of CO2 increases its content, which does the same effects with higher pressures. The stability of the water bridge is disturbed by diffused CO2 , and its waist is the weakest position by the potential energy distribution.Cited as: Liu, J., Zhang, T., Sun, S. Stability analysis of the water bridge in organic shale nanopores: A molecular dynamic study. Capillarity, 2022, 5(4): 75-82. https://doi.org/10.46690/capi.2022.04.0

Flashlight Search Medial Axis: A Pixel-Free Pore-Network Extraction Algorithm

Pore-network models (PNMs) have become an important tool in the study of fluid flow in porous media over the last few decades, and the accuracy of their results highly depends on the extraction of pore networks. Traditional methods of pore-network extraction are based on pixels and require images with high quality. Here, a pixel-free method called the flashlight search medial axis (FSMA) algorithm is proposed for pore-network extraction in a continuous space. The search domain in a two-dimensional space is a line, whereas a surface domain is searched in a three-dimensional scenario. Thus, the FSMA algorithm follows the dimensionality reduction idea; the medial axis can be identified using only a few points instead of calculating every point in the void space. In this way, computational complexity of this method is greatly reduced compared to that of traditional pixel-based extraction methods, thus enabling large-scale pore-network extraction. Based on cases featuring two- and three-dimensional porous media, the FSMA algorithm performs well regardless of the topological structure of the pore network or the positions of the pore and throat centers. This algorithm can also be used to examine both closed- and open-boundary cases. Finally, the FSMA algorithm can search dead-end pores, which is of great significance in the study of multiphase flow in porous media

K-means Clustering Based Feature Consistency Alignment for Label-free Model Evaluation

The label-free model evaluation aims to predict the model performance on various test sets without relying on ground truths. The main challenge of this task is the absence of labels in the test data, unlike in classical supervised model evaluation. This paper presents our solutions for the 1st DataCV Challenge of the Visual Dataset Understanding workshop at CVPR 2023. Firstly, we propose a novel method called K-means Clustering Based Feature Consistency Alignment (KCFCA), which is tailored to handle the distribution shifts of various datasets. KCFCA utilizes the K-means algorithm to cluster labeled training sets and unlabeled test sets, and then aligns the cluster centers with feature consistency. Secondly, we develop a dynamic regression model to capture the relationship between the shifts in distribution and model accuracy. Thirdly, we design an algorithm to discover the outlier model factors, eliminate the outlier models, and combine the strengths of multiple autoeval models. On the DataCV Challenge leaderboard, our approach secured 2nd place with an RMSE of 6.8526. Our method significantly improved over the best baseline method by 36\% (6.8526 vs. 10.7378). Furthermore, our method achieves a relatively more robust and optimal single model performance on the validation dataset.Comment: Accepted by CVPR 2023 worksho

Moving Deep Learning into Web Browser: How Far Can We Go?

Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers. However, little is known on what and how well we can do with these frameworks for deep learning in browsers. To bridge the knowledge gap, in this paper, we conduct the first empirical study of deep learning in browsers. We survey 7 most popular JavaScript-based deep learning frameworks, investigating to what extent deep learning tasks have been supported in browsers so far. Then we measure the performance of different frameworks when running different deep learning tasks. Finally, we dig out the performance gap between deep learning in browsers and on native platforms by comparing the performance of TensorFlow.js and TensorFlow in Python. Our findings could help application developers, deep-learning framework vendors and browser vendors to improve the efficiency of deep learning in browsers