An adsorbed gas estimation model for shale gas reservoirs via statistical learning

Shale gas plays an important role in reducing pollution and adjusting the structure of world energy. Gas content estimation is particularly significant in shale gas resource evaluation. There exist various estimation methods, such as first principle methods and empirical models. However, resource evaluation presents many challenges, especially the insufficient accuracy of existing models and the high cost resulting from time-consuming adsorption experiments. In this research, a low-cost and high-accuracy model based on geological parameters is constructed through statistical learning methods to estimate adsorbed shale gas conten

Stress-strain analysis of Aikou rockfill dam with asphalt-concrete core

AbstractAikou rockfill dam with asphalt-concrete core is situated in a karst area in Chongqing City, China. In order to study the operative conditions of the rockfill dam, especially those of the asphalt-concrete core, the Duncan model is adopted to compute the stress and strain of both the rockfill dam and the asphalt-concrete core after karst grouting and other treatments. The results indicate that the complicated stress and deformation of both the dam body and the core are within reasonable ranges. It is shown that structure design and foundation treatment of the dam are feasible and can be used as a reference for other similar projects

Spray Layer-by-Layer Assembled Clay Composite Thin Films as Selective Layers in Reverse Osmosis Membranes

Spray layer-by-layer assembled thin films containing laponite (LAP) clay exhibit effective salt barrier and water permeability properties when applied as selective layers in reverse osmosis (RO) membranes. Negatively charged LAP platelets were layered with poly(diallyldimethylammonium) (PDAC), poly(allylamine) (PAH), and poly(acrylic acid) (PAA) in bilayer and tetralayer film architectures to generate uniform films on the order of 100 nm thick that bridge a porous poly(ether sulfone) support to form novel RO membranes. Nanostructures were formed of clay layers intercalated in a polymeric matrix that introduced size-exclusion transport mechanisms into the selective layer. Thermal cross-linking of the polymeric matrix was used to increase the mechanical stability of the films and improve salt rejection by constraining swelling during operation. Maximum salt rejection of 89% was observed for the tetralayer film architecture, with an order of magnitude increase in water permeability compared to commercially available TFC-HR membranes. These clay composite thin films could serve as a high-flux alternative to current polymeric RO membranes for wastewater and brackish water treatment as well as potentially for forward osmosis applications. In general, we illustrate that by investigating the composite systems accessed using alternating layer-by-layer assembly in conjunction with complementary covalent cross-linking, it is possible to design thin film membranes with tunable transport properties for water purification applications.Center for Clean Water and Clean Energy at MIT and KFUPM (project R5-CW-08

Assessing Relevance of Tweets for Risk Communication

Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness

Springback analysis of AA5754 after hot stamping: experiments and FE modelling

In this paper, the springback of the aluminium alloy AA5754 under hot stamping conditions was characterised under stretch and pure bending conditions. It was found that elevated temperature stamping was beneficial for springback reduction, particularly when using hot dies. Using cold dies, the flange springback angle decreased by 9.7 % when the blank temperature was increased from 20 to 450 °C, compared to the 44.1 % springback reduction when hot dies were used. Various other forming conditions were also tested, the results of which were used to verify finite element (FE) simulations of the processes in order to consolidate the knowledge of springback. By analysing the tangential stress distributions along the formed part in the FE models, it was found that the springback angle is a linear function of the average through-thickness stress gradient, regardless of the forming conditions used

Predicting Protein Residue-Residue Contacts Using Random Forests and Deep Networks

Background: The ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the computational complexity of predicting the structure of proteins and even to help identify functionally important regions of proteins. These predictions are becoming especially important given the relatively low number of experimentally determined protein structures compared to the amount of available protein sequence data. Results: Here we have developed and benchmarked a set of machine learning methods for performing residue-residue contact prediction, including random forests, direct-coupling analysis, support vector machines, and deep networks (stacked denoising autoencoders). These methods are able to predict contacting residue pairs given only the amino acid sequence of a protein. According to our own evaluations performed at a resolution of +/− two residues, the predictors we trained with the random forest algorithm were our top performing methods with average top 10 prediction accuracy scores of 85.13% (short range), 74.49% (medium range), and 54.49% (long range). Our ensemble models (stacked denoising autoencoders combined with support vector machines) were our best performing deep network predictors and achieved top 10 prediction accuracy scores of 75.51% (short range), 60.26% (medium range), and 43.85% (long range) using the same evaluation. These tests were blindly performed on targets from the CASP11 dataset; and the results suggested that our models achieved comparable performance to contact predictors developed by groups that participated in CASP11. Conclusions: Due to the challenging nature of contact prediction, it is beneficial to develop and benchmark a variety of different prediction methods. Our work has produced useful tools with a simple interface that can provide contact predictions to users without requiring a lengthy installation process. In addition to this, we have released our C++ implementation of the direct-coupling analysis method as a standalone software package. Both this tool and our RFcon web server are freely available to the public at http://dna.cs.miami.edu/RFcon/

Growth and development symposium: Stem and progenitor cells in animal growth: The regulation of beef quality by resident progenitor cells

© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. The intramuscular adipose tissue deposition in the skeletal muscle of beef cattle is a highly desired trait essential for high-quality beef. In contrast, the excessive accumulation of crosslinked collagen in intramuscular connective tissue contributes to beef toughness. Recent studies revealed that adipose tissue and connective tissue share an embryonic origin in mice and may be derived from a common immediate bipotent precursor in mice and humans. Having the same linkages in the development of adipose tissue and connective tissue in beef, the lineage commitment and differentiation of progenitor cells giving rise to these tissues may directly affect beef quality. It has been shown that these processes are regulated by some key transcription regulators and are subjective to epigenetic modifications such as DNA methylation, histone modifications, and microRNAs. Continued exploration of relevant regulatory pathways is very important for the identification of mechanisms influencing meat quality and the development of proper management strategies for beef quality improvement

An Ensemble Multilabel Classification for Disease Risk Prediction

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance

Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie