Evaluation of biomass as bio-additive in 3D printing

The petrol-based polymer has been widely applied in current daily life. The end-of-life of polymeric products has drawn environmental concerns. One of the solutions to such issues is to use bio-renewable materials to replace or reduce the use of petrol-based materials. Lignocellulosic materials are one of the potential candidates. Along with the features of 3D printing and the unique properties of biomass, 3D-printed biomass-based materials could be promising in preparing sustainable alternatives. In this dissertation, lignin and other biomass were applied to various 3D printing techniques for sustainable composites. Stereolithography (SLA) was first used, and the kraft softwood lignin was incorporated into the commercial clear resin. The printed composites showed a reinforcing effect after fully curing. Inspired by such a reinforcement, wood flour, where lignin is isolated, was also applied as a biomass filler. The 3D-printed wood flour composites showed a unique stress-whitening phenomenon, which displayed a potential for stress indicators. To further improve the loading amount of lignin in 3D printing, fused depositional modeling (FDM) was also involved. To improve the interfacial interaction between lignin and the polymer matrix, organosolv hardwood lignin was demethylated with improved phenolic hydroxyl groups. Enriched phenolic hydroxyl structures improved the interfacial interaction, thereby promoting tensile performance. A similar demethylation was executed on the softwood lignin. Due to the inherent difference in the functional groups, the obtained enriched polyphenol structures showed different but positive influences on tensile properties. In addition, the enhanced phenolic structures also showed great anti-aging performance, which brings more functions to the resultant composites, making lignin/polymer composites a promising functional and renewable material for the sustainable materials world

Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition

Motion representation plays a vital role in human action recognition in videos. In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach. The OFF is derived from the definition of optical flow and is orthogonal to the optical flow. The derivation also provides theoretical support for using the difference between two frames. By directly calculating pixel-wise spatiotemporal gradients of the deep feature maps, the OFF could be embedded in any existing CNN based video action recognition framework with only a slight additional cost. It enables the CNN to extract spatiotemporal information, especially the temporal information between frames simultaneously. This simple but powerful idea is validated by experimental results. The network with OFF fed only by RGB inputs achieves a competitive accuracy of 93.3% on UCF-101, which is comparable with the result obtained by two streams (RGB and optical flow), but is 15 times faster in speed. Experimental results also show that OFF is complementary to other motion modalities such as optical flow. When the proposed method is plugged into the state-of-the-art video action recognition framework, it has 96:0% and 74:2% accuracy on UCF-101 and HMDB-51 respectively. The code for this project is available at https://github.com/kevin-ssy/Optical-Flow-Guided-Feature.Comment: CVPR 2018. code available at https://github.com/kevin-ssy/Optical-Flow-Guided-Featur

3 reasons Midwest farmers hurt by the U.S.-China trade war still support Trump

America’s farmers have borne the brunt of China’s retaliation in the trade war that President Donald Trump launched in 2018

Spectrum and Retention Time Prediction for N-Glycopeptides Using Deep Learning

Sequencing proteins and glycans have important clinical applications, as glycosylation is shown to play a significant role in cellular communication and immune response. Certain glycans are linked to the diagnosis of cancer as well as targeted immunotherapy. Mass spectrometry is a powerful tool that helps us gain insight into peptide sequences and glycan structures, by using database search, spectral library, or de novo sequencing. Spectrum and retention time prediction using deep learning has gained popularity with studies on non-glycosylated peptides and has been shown to improve database search results via rescoring. This thesis proposes deep learning models to predict spectrum and retention time for N-glycopeptides and then discusses the applications of these models with respect to glycopeptide sequencing. Chapter 3 presents a graph deep learning model to predict fragment ion intensities of observed spectrums and define a spectrum representation for glycan fragments with up to three cleavages. The spectrum prediction model has a median cosine similarity of 0.921, which is 20% higher than previous attempts at glycopeptide spectrum prediction. For retention time prediction in Chapter 4, we propose a model with two parallel encoders for both peptide and glycan input and apply transfer learning for the sequence encoder. The retention time prediction model has a Pearson correlation of 1.0, which is higher than the previous 0.98 and 0.96 attempts. We also introduce the 95 percentile delta as an evaluation metric, as well as discuss the interpretability of our model. Finally in Chapter 5, we apply our spectrum and retention time prediction models in glycopeptide sequencing pipelines, including database search and de novo search. We show that our model improves identification by rescoring and has the potential to be used as a filter for false positives. We also demonstrate that our model improves de novo identification when used in the scoring function

A Decision Support System for Assessing Oil Spill Vulnerability in Texas Coastal Regions

Oil spill disasters may have devastating impacts for coastal communities and the ecosystem (Baade et al., 2007). For instance, the coastal regions of the United States, especially the Gulf region, have been devastated by oil spill events of varying sizes, resulting in significant ecological and economic losses (Baade et al., 2007; Smith et al., 2010). In this study, a human-centered multi-criteria decision-making (MCDM) framework is proposed and then present an example assessment of oil spill vulnerability utilizing the framework to support oil spill risk-informed decision-making in Texas coastal areas and the Western Planning Area (WPA) in the Gulf of Mexico. This work aims to assess oil spill vulnerability by defining three major conceptualizations of vulnerability: socioeconomic vulnerability, environment vulnerability, and vulnerability of spill impact risk through the Blowout and Spill Occurrence Model (BLOSOM) simulation. Furthermore, a decision support system is developed with an MCDM framework by combining spatiotemporal simulation results from BLOSOM and the vulnerability indexes. The proposed framework is applied to identify areas prone to oil spill disasters with the combination of spatial-temporal analysis and a customized multi-criteria evaluation. Areas with higher vulnerability scores in the case study are considered more vulnerable to oil spill impacts and should be considered with high priority in emergency response. Results indicated that communities in Galveston, Freeport, and Corpus Christi areas, are under great threat from oil spill disasters under conditions similar to those in the simulation. Moreover, the proposed framework emphasizes human-centered design and collaborative decision-making where different decision-makers can select the data that are important for their decision goals and assign weights for the evaluation criteria to generate an overall vulnerability score from vulnerability indexing to improve the performance of collaborative decision-making and eventually facilitate oil spill response and management

Average distance in a hierarchical scale-free network: an exact solution

Various real systems simultaneously exhibit scale-free and hierarchical structure. In this paper, we study analytically average distance in a deterministic scale-free network with hierarchical organization. Using a recursive method based on the network construction, we determine explicitly the average distance, obtaining an exact expression for it, which is confirmed by extensive numerical calculations. The obtained rigorous solution shows that the average distance grows logarithmically with the network order (number of nodes in the network). We exhibit the similarity and dissimilarity in average distance between the network under consideration and some previously studied networks, including random networks and other deterministic networks. On the basis of the comparison, we argue that the logarithmic scaling of average distance with network order could be a generic feature of deterministic scale-free networks.Comment: Definitive version published in Journal of Statistical Mechanic