Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, especially deep learning, can infer a good prediction by integrating multi-source datasets like satellite data, climate data, soil data, and so on. However, there are some bottleneck challenges to improve accuracy. First, the popular remote sensing data used for yield prediction fall into two major groups, time-series data, and constant data. Surprisingly little attention has been devoted to deep learning networks that can integrate the two kinds of data effectively; second, both temporal and spatial features play a role in affecting the yields. But most of the existing approaches employed either CNN (convolutional neural network) or RNN (recurrent neural network). CNN cannot learn temporal patterns, while RNN barely can learn spatial characteristics. This work proposed a novel multi-level deep learning (MLDL) model coupling RNN and CNN to extract both spatial and temporal features. The inputs include both time-series remote sensing data and soil property data and the model outputs yield. We experimented with the model in U.S. Corn Belt states and used it to predict corn yield from 2013 to 2016 at the county-level. The results approve the effectiveness and advantages of the proposed approach over the other methods. In the future, the model will be used on other crops like soybean and winter wheat to assist agriculture decision making.

About 15-25% of aging population suffers from swallowing difficulties, and this creates an increasing market need for food mass customization. Food industry is investigating mass customization techniques to meet individual needs on taste, nutrition and mouthfeel. Three dimensional (3D) food printing is a potential solution to overcome drawbacks of current food customization techniques such as lower production efficiency and high manufacturing cost. This study introduces the first generation food printer concept designs and functional prototypes that target to revolutionize customized food fabrication by 3D printing (3DP). Different from robotics-based food manufacturing technologies designed to automate manual processes for mass production, 3D food printing integrates 3DP and digital gastronomy technique to customize food products. This introduces artistic capabilities into domestic cooking, and extends customization capabilities to industrial culinary sector. Their applications in domestic cooking or catering services can not only provide an engineering solution for customized food design and personalized nutrition control, but also have potential to reconfigure customized food supply chains. In this paper, the selected prototypes are reviewed based on fabrication platforms and printing materials. A detailed discussion on specific 3DP technologies and their associate dispensing/printing process for 3D customized food fabrication with single and multi-material applications are reported. Lastly, impacts of food printing on customized food fabrication, personalized nutrition, food supply chain, and food processing technologies are reported and discussed.

Current surgical treatments for meniscal tears suffer from subsequent degeneration of knee joints, limited donor organs and inconsistent post-treatment results. Three clinical scaffolds (Menaflex CMI, Actifit^® scaffold and NUsurface^® Meniscus Implant) are available on the market, but additional data are needed to properly evaluate their safety and effectiveness. Thus, many scaffold-based research activities have been done to develop new materials, structures and fabrication technologies to mimic native meniscus for cell attachment and subsequent tissue development, and restore functionalities of injured meniscus for long-term effects. This study begins with a synopsis of relevant structural features of meniscus and goes on to describe the critical considerations. Promising advances made in the field of meniscal scaffolding technology, in terms of biocompatible materials, fabrication methods, structure design and their impact on mechanical and biological properties are discussed in detail. Among all the scaffolding technologies, additive manufacturing (AM) is very promising because of its ability to precisely control fiber diameter, orientation, and pore network micro-architecture to mimic the native meniscus microenvironment.

Accurate bathymetric data is crucial for marine and coastal ecosystems. A lot of studies have been carried out for nearshore bathymetry using satellite data. The approach adopted extensively in shallow water depths estimation has recently been one of empirical models. However, the linear empirical model is simple and only takes limited band information at each bathymetric point into consideration. It may be not suitable for complex environments. In this paper, a deep learning framework was proposed for nearshore bathymetry (DL-NB) from ICESat-2 LiDAR and Sentinel-2 Imagery datasets. The bathymetric points from the spaceborne ICESat-2 LiDAR were extracted instead of in situ measurements. By virtue of the two-dimensional convolutional neural network (2D CNN), DL-NB can make full use of the initial multi-spectral information of Sentinel-2 at each bathymetric point and its adjacent areas during the training. Based on the trained model, the bathymetric maps of several study areas were produced including the Appalachian Bay (AB), Virgin Islands (VI), and Cat Island (CI) of the United States. The performance of DL-NB was evaluated by empirical method, machine learning method and multilayer perceptron (MLP). The results indicate that the accuracy of the DL-NB is better than comparative methods can in nearshore bathymetry. After quantitative analysis, the RMSE of DL-NB could achieve 1.01 m, 1.80 m and 0.28 m in AB, VI and CI respectively. Given the same data conditions, the proposed method can be applied for high precise global scale and multitemporal nearshore bathymetric maps generation, which are beneficial to marine environmental change assessment and conservation.