Simulation Technology for Hydrodynamic and Water Quality in the Main Canal

The hydrodynamic and water quality simulation technology can be used for predicting the pollutant diffusion process after a sudden water pollution accident, and for analyzing the effect of emergency operation measures. The MRP features a long route, a variety of buildings, etc.; therefore, a set of hydrodynamic and water quality models that are applicable to the main canal of the MRP was independently developed based on 1-D open canal hydrodynamic and water quality theory and with various types of buildings as inner boundaries. Through calibration and verification, these models can be applied to the simulation of hydraulic and water quality response process under any operation conditions in the main canal of the MRP

Efficient Transposition of the piggyBac (PB) Transposon in Mammalian Cells and Mice

SummaryTransposable elements have been routinely used for genetic manipulation in lower organisms, including generating transgenic animals and insertional mutagenesis. In contrast, the usage of transposons in mice and other vertebrate systems is still limited due to the lack of an efficient transposition system. We have tested the ability of piggyBac (PB), a DNA transposon from the cabbage looper moth Trichoplusia ni, to transpose in mammalian systems. We show that PB elements carrying multiple genes can efficiently transpose in human and mouse cell lines and also in mice. PB permits the expression of the marker genes it carried. During germline transposition, PB could excise precisely from original insertion sites and transpose into the mouse genome at diverse locations, preferably transcription units. These data provide a first and critical step toward a highly efficient transposon system for a variety of genetic manipulations including transgenesis and insertional mutagenesis in mice and other vertebrates

Improving Social Media Popularity Prediction with Multiple Post Dependencies

Social Media Popularity Prediction has drawn a lot of attention because of its profound impact on many different applications, such as recommendation systems and multimedia advertising. Despite recent efforts to leverage the content of social media posts to improve prediction accuracy, many existing models fail to fully exploit the multiple dependencies between posts, which are important to comprehensively extract content information from posts. To tackle this problem, we propose a novel prediction framework named Dependency-aware Sequence Network (DSN) that exploits both intra- and inter-post dependencies. For intra-post dependency, DSN adopts a multimodal feature extractor with an efficient fine-tuning strategy to obtain task-specific representations from images and textual information of posts. For inter-post dependency, DSN uses a hierarchical information propagation method to learn category representations that could better describe the difference between posts. DSN also exploits recurrent networks with a series of gating layers for more flexible local temporal processing abilities and multi-head attention for long-term dependencies. The experimental results on the Social Media Popularity Dataset demonstrate the superiority of our method compared to existing state-of-the-art models

Muscle atrophy in transgenic mice expressing a human TSC1 transgene

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116374/1/feb2s0014579306010866.pd

A computationally-efficient sandbox algorithm for multifractal analysis of large-scale complex networks with tens of millions of nodes

Multifractal analysis (MFA) is a useful tool to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. One of the widely used methods for fractal analysis is box-covering. It is known to be NP-hard. More severely, in comparison with fractal analysis algorithms, MFA algorithms have much higher computational complexity. Among various MFA algorithms for complex networks, the sandbox MFA algorithm behaves with the best computational efficiency. However, the existing sandbox algorithm is still computationally expensive. It becomes challenging to implement the MFA for large-scale networks with tens of millions of nodes. It is also not clear whether or not MFA results can be improved by a largely increased size of a theoretical network. To tackle these challenges, a computationally-efficient sandbox algorithm (CESA) is presented in this paper for MFA of large-scale networks. Our CESA employs the breadth-first search (BFS) technique to directly search the neighbor nodes of each layer of center nodes, and then to retrieve the required information. Our CESA's input is a sparse data structure derived from the compressed sparse row (CSR) format designed for compressed storage of the adjacency matrix of large-scale network. A theoretical analysis reveals that the CESA reduces the time complexity of the existing sandbox algorithm from cubic to quadratic, and also improves the space complexity from quadratic to linear. MFA experiments are performed for typical complex networks to verify our CESA. Finally, our CESA is applied to a few typical real-world networks of large scale.Comment: 19 pages, 9 figure

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Study on failure warning of tool magazine and automatic tool changer

Tool magazine and automatic tool changer (ATC) is used to store and change tools in a machining center, which plays an important role in automatic manufacturing. Therefore, the stability and reliability of tool magazine and ATC is very important to a machining center. Failures of tool magazine and ATC would increase ramp-up repair time and repair cost. So early warning system of failures for tool magazine and ATC becomes a research hotspot. The main vibration signals of tool magazine and ATC would occur obviously when the tool arm grasps a tool holder, draws a tool holder out of a tool into spindle or tool pocket and inserts a tool holder into spindle or tool pocket. To predict failures of tool magazine and ATC and improve the availability of machining center, a vibration test procedure and calculation method of vibration signal threshold of pull nails looseness which can lead to tool falling failures are proposed based on the vibration detection theory. Then, the vibration signals of tool changing are analyzed and the relationship between the maximum amplitude of vibration signals and the looseness severity of pull nails is also illustrated. The final experiment results show that the tool falling failure warning method is feasible to reduce the failures of tool magazine and ATC through the early warning system based on the threshold of vibration signals

Inter-Particle Electronic and Ionic Modifications of the Ternary Ni-Co-Mn Oxide for Efficient and Stable Lithium Storage

A combined electronic and ionic interparticular modification strategy is designed for the improvement of lithium storage in the layer structured ternary Ni-Co-Mn oxide (LiNi0.6Co0.2Mn0.2O2) in the form of spherical particles. In this design, a thin layer of the ion conducting polypropylene carbonate is applied to wrap the individual oxide particles for three purposes: (1) prevention of direct stacking and packing between oxide particles that will otherwise impede or block ions from accessing all the surface of the oxide particles, (2) provision of additional ionic pathways between the oxide particles, and (3) stabilization of the oxide particles during lithium storage and release. The design includes also the use of nitrogen doped carbon nanotubes for electronic connection between the polymer coated individual spheres of the layered nickel-rich LiNi0.6Co0.2Mn0.2O2. According to the physicochemical and electrochemical characterizations, and laboratory battery tests, it can be concluded that the LiNi0.6Co0.2Mn0.2O2 composite has a unique porous structure that is assembled by the polymer coated ternary oxide microspheres and the nitrogen-doped carbon nanotube networks. Significant improvements are achieved in both the ionic and electronic conductivities (double or more increase), and in discharge specific capacity (201.3 mAh·g−1 at 0.1 C, improved by 13.28% compared to the non-modified LiNi0.6Co0.2Mn0.2O2), rate performance and cycling stability (94.40% in capacity retention after 300 cycles at 1.0 C)