Macroporous materials: microfluidic fabrication, functionalization and applications

This article provides an up-to-date highly comprehensive overview (594 references) on the state of the art of the synthesis and design of macroporous materials using microfluidics and their applications in different fields

Neural network-based intrinsic structure relationship of TC20 titanium alloy for medical applications

Isothermal constant strain rate compression experiments were carried out on TC20 titanium alloy using a Gleeble- 1500 thermal simulation tester to investigate its high temperature flow behaviour at deformation temperatures of 750 - 900 °C and strain rates of 0,001 - 1 s-1. The results show that the flow stress basically decreases with increasing deformation temperature and increases with increasing strain rate. The correlation coefficients and mean relative errors were 0,998 and 5,06 % respectively, proving that the BP neural network-based intrinsic structure model is effective in predicting the flow stress of the alloy

In situ photogalvanic acceleration of optofluidic kinetics: a new paradigm for advanced photocatalytic technologies

A multiscale-designed optofluidic reactor is demonstrated in this work, featuring an overall reaction rate constant of 1.32 s¯¹ for photocatalytic decolourization of methylene blue, which is an order of magnitude higher as compared to literature records. A novel performance-enhancement mechanism of microscale in situ photogalvanic acceleration was found to be the main reason for the superior optofluidic performance in the photocatalytic degradation of dyes as a model reaction

Neural network-based intrinsic structure relationship of TC20 titanium alloy for medical applications

Isothermal constant strain rate compression experiments were carried out on TC20 titanium alloy using a Gleeble- 1500 thermal simulation tester to investigate its high temperature flow behaviour at deformation temperatures of 750 - 900 °C and strain rates of 0,001 - 1 s-1. The results show that the flow stress basically decreases with increasing deformation temperature and increases with increasing strain rate. The correlation coefficients and mean relative errors were 0,998 and 5,06 % respectively, proving that the BP neural network-based intrinsic structure model is effective in predicting the flow stress of the alloy

Computational comparison of two mouse draft genomes and the human golden path

BACKGROUND: The availability of both mouse and human draft genomes has marked the beginning of a new era of comparative mammalian genomics. The two available mouse genome assemblies, from the public mouse genome sequencing consortium and Celera Genomics, were obtained using different clone libraries and different assembly methods. RESULTS: We present here a critical comparison of the two latest mouse genome assemblies. The utility of the combined genomes is further demonstrated by comparing them with the human 'golden path' and through a subsequent analysis of a resulting conserved sequence element (CSE) database, which allows us to identify over 6,000 potential novel genes and to derive independent estimates of the number of human protein-coding genes. CONCLUSION: The Celera and public mouse assemblies differ in about 10% of the mouse genome. Each assembly has advantages over the other: Celera has higher accuracy in base-pairs and overall higher coverage of the genome; the public assembly, however, has higher sequence quality in some newly finished bacterial artificial chromosome clone (BAC) regions and the data are freely accessible. Perhaps most important, by combining both assemblies, we can get a better annotation of the human genome; in particular, we can obtain the most complete set of CSEs, one third of which are related to known genes and some others are related to other functional genomic regions. More than half the CSEs are of unknown function. From the CSEs, we estimate the total number of human protein-coding genes to be about 40,000. This searchable publicly available online CSEdb will expedite new discoveries through comparative genomics

The Role of Atomic Structures on the Oxygen Corrosion of Polycrystalline Copper Surface

AbstractThe mechanical property of materials for pressure vessel, like steel, Ti, Cu and their alloys always turns out to be poor in the severely corrosive environment. The knowledge of oxygen corrosion on metal surface at atomic level is still lack. Using reactive molecular dynamic simulation, the oxygen corrosion behavior on polycrystalline copper is studied at the early stage of oxidation. Results indicate a higher reactivity at the grain boundary. The preferential dissociation of oxygen molecules at grain boundary is ascribed to the diffusion-related trapping effect and dissociation barriers. In addition, the difference of oxygen corrosion between grain boundary and grain on copper surface is elucidated in terms of the atomic-structure-related radial distribution functions. This study directly shows us the origin of intergranular oxygen corrosion and provides us useful information for the corrosion prevention, especially in the situation that the atomic structure changes under the thermal or mechanical loadings

Bayesian deep reinforcement learning via deep kernel learning

© 2018, the Authors. Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems could benefit from RL, e.g., industrial robotics, medical treatment, and trade execution. As a representative model-free RL algorithm, deep Q-network (DQN) has recently achieved great success on RL problems and even exceed the human performance through introducing deep neural networks. However, such classical deep neural network-based models cannot well handle the uncertainty in sequential decision-making and then limit their learning performance. In this paper, we propose a new model-free RL algorithm based on a Bayesian deep model. To be specific, deep kernel learning (i.e., a Gaussian process with deep kernel) is adopted to learn the hidden complex action-value function instead of classical deep learning models, which could encode more uncertainty and fully take advantage of the replay memory. The comparative experiments on standard RL testing platform, i.e., OpenAI-Gym, show that the proposed algorithm outweighs the DQN. Further investigations will be directed to applying RL for supporting dynamic decision-making in complex environments

Discovering the core semantics of event from social media

© 2015 Elsevier B.V. As social media is opening up such as Twitter and Sina Weibo,1 large volumes of short texts are flooding on the Web. The ocean of short texts dilutes the limited core semantics of event in cyberspace by redundancy, noises and irrelevant content on the web, which make it difficult to discover the core semantics of event. The major challenges include how to efficiently learn the semantic association distribution by small-scale association relations and how to maximize the coverage of the semantic association distribution by the minimum number of redundancy-free short texts. To solve the above issues, we explore a Markov random field based method for discovering the core semantics of event. This method makes semantics collaborative computation for learning association relation distribution and makes information gradient computation for discovering k redundancy-free texts as the core semantics of event. We evaluate our method by comparing with two state-of-the-art methods on the TAC dataset and the microblog dataset. The results show our method outperforms other methods in extracting core semantics accurately and efficiently. The proposed method can be applied to short text automatic generation, event discovery and summarization for big data analysis