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Physical process-based groundwater flow models are the major tools for studying fluid-flow behavior and for simulating the hydrological responses of water levels and spring discharge to human- and/or nature-induced triggers such as pumping and recharge. Such models are built with deep understanding of the physical processes and are based on geological models, developed by integrating data from geology, geophysics, and geochemistry. However, data-driven models can be built with limited data, eliminating the need for a detailed understanding of the physics. In this research, a data-driven model is built for simulating hydraulic responses (both groundwater levels and spring discharges) in a complex groundwater flow system of the Edwards Aquifer in Central Texas, USA, with the recurrent neural network (RNN) technique. The model is first trained and validated with the observation data of four targets—water levels from two index wells, and spring flow rates from San Marcos and Comal springs—from 2001 through 2015. The model is then used to predict the hydrological responses for the drought of record (1947–1958). The performance of the RNN model for the training, validation and prediction period is then quantitatively compared to that of the physical process-based MODFLOW model in terms of four statistical measures. The statistical measures suggest that the RNN model performs almost as well as the MODFLOW model. With further improvements, a data-driven model may be a surrogate to (or integrate with) a physical process-based model for simulating hydrological responses in the Edwards Aquifer.

In situ 3D printing technologies is a new frontier for highly personalized medicine, which requires suitable bioink with rheology, biocompatibility, and gelation kinetics to support the right shape and mechanical properties of the printed construct. To this end, a facile design of thermo/photo dual cure composite hydrogel was proposed using MHBC and soluble collagen in this study. M/C composite hydrogel exhibited rapid thermo-induced sol-gel transition and contraction, tunable mechanical properties, proper microstructure and biodegradability for 3D cell culture, as well as improve cyto-compatibility, all of which were dependent upon the methacrylation degree of MHBC and M/C ratios. The printability of the optimal formulation (3% MHBC/1% collagen) was validated by its mild printing condition, rapid gelation of bioink at 37 °C and simple postprocessing manipulation. Both desirable printability and cyto-compatibility enable M/C composite hydrogel a potential candidate as bioink to be applied for in situ 3D bioprinting.

As a framework for tissue engineering regeneration, the characteristics of cell scaffold materials directly affect cell adhesion, migration and metabolism. In this study, we have fabricated decellularized and decalcified fish scale-derived scaffolds and determined its basic physicochemical properties to serve as cell scaffolds in tissue engineering. Scanning electron microscopy (SEM) results showed that there were radial grooves and ring ridges on the surface of the scale-derived scaffolds, which could simulate three-dimensional microenvironment for cells culture. Similarity to the bone extracellular matrix, the main components of the fish scales were hydroxyapatite (HA) and type I collagen fibers, which were conducive to cells spreading and proliferation. Moreover, for culturing L929 cells and rat bone marrow mesenchymal stem cells (BMSCs), the fish scales as cell scaffolds exhibited high cytocompatibility to enhance cells adhesion and proliferation, and also displayed the ability to guide cells migration along the ridge channels. Accordingly, the results suggested that the fish scale-derived scaffolds had a great potential as a natural extracellular matrix for tissue engineering.

Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs. This paper presents a novel memory-augmented NMT (M-NMT) architecture, which stores knowledge about how words (usually infrequently encountered ones) should be translated in a memory and then utilizes them to assist the neural model. We use this memory mechanism to combine the knowledge learned from a conventional statistical machine translation system and the rules learned by an NMT system, and also propose a solution for out-of-vocabulary (OOV) words based on this framework. Our experiments on two Chinese-English translation tasks demonstrated that the M-NMT architecture outperformed the NMT baseline by $9.0$ and $2.7$ BLEU points on the two tasks, respectively. Additionally, we found this architecture resulted in a much more effective OOV treatment compared to competitive methods.

It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. In addition, it is found that the memory mechanism provides interesting flexibility that can be used to generate poems with different styles.