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Dynamic network visualization is crucial to understand network evolving behavior. Massive sequence view (MSV) is a classic technique for visualizing dynamic networks and provides users with a fine-grained presentation of time-varying communication trend from both node pair and global network levels. However, MSV is vulnerable to visual clutter caused by overlapping edges, failing to show clear patterns or trends. This paper presents an edge sampling method, using the edge overlapping degree (EOD) concept, to reduce visual clutter in MSV while preserving the time-varying features of network communication. Referring to accept–reject sampling, we use kernel density estimation to characterize the time-varying features between node pairs and generate EOD probability density functions to accomplish sampling in a bottom-up manner. To enhance the sampling effect, we also consider the edge length factor and streaming processing. The case studies on two dynamic network data sets demonstrate that our method can significantly improve the overall readability of MSV and clearly reveal the temporal features of both node pairs and global network. A quantitative evaluation comparing with two other sampling methods using three real-world data sets indicates that our method can well balance visual clutter reduction and temporal feature preservation.

Reverse-engineering bar charts extracts textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. In this paper, we propose a neural network-based method for reverse-engineering bar charts. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. This approach improves the efficiency of textual information extraction. We design an encoder-decoder framework that integrates convolutional and recurrent neural networks to extract numeric information. We further introduce an attention mechanism into the framework to achieve high accuracy and robustness. Synthetic and real-world datasets are used to evaluate the effectiveness of the method. To the best of our knowledge, this work takes the lead in constructing a complete neural network-based method of reverse-engineering bar charts.

Massive Sequence View(MSV) is an important timeline-based technique for dynamic network visualization. However, it often suffers from severe visual clutter when limited screen space holds excessive network edges. Inspired by the use of graph sampling in static graph analysis, we propose to utilize graph sampling to reduce visual clutter in MSV. An edge sampling method based on accept-reject random sampling is designed for visualizing dynamic network via MSV. The method is able to improve the overall readability of MSV while preserving time varying network behaviors. It is also a preliminary attempt to apply graph-sampling technique into dynamic network analysis.

The infiltration casting method is used for the preparation of three-dimensional co-continuous network structure of graphite/cast steel composite material in atmospheric pressure. Using modal analysis method, flake graphite cast iron specimen and composite sample are compared about damping factors. The results show that amplitude attenuation of the composite material is more quickly than that of flake graphite cast iron specimens. Supported in two rigid points state, the first step damping ratio of the composite material is 2.1 times of flake graphite cast iron. At the same time, its tensile strength is 1.54 to 1.68 times that of flake graphite cast iron, its hardness reaches more than 86%, and its bulk density is reduced to less than 94%. The composite not only has good vibration damping and anti-friction performance, but also has comprehensive mechanical properties exceeding the level of flake graphite cast iron. It is possible to replace flake graphite cast iron for manufacturing important mechanical equipment castings such as fuselage and guide rail to obtain better anti-wear / vibration damping effect.

In the later stage of development, most oilfields faced high water cut issue after treatments. Meanwhile, how to effectively control oil wells water breakthrough after fracturing so as to increase oil production has become a big challenge for production engineers. This paper introduced a novel selective proppant which can effectively block water from entering well bore after fracturing. Based on "eggshell principle" and interfacial tension theory, this novel proppant was developed with a new monolayer strong hydrophobic interface treatment technique. This special processing technique could not only reduce the proppant's flow resistance of oil, but also increase its flow resistance of water. As the result, this novel proppant showed the characteristics of excellent lipophilicity and hydrophobicity as well. Experiments indicated that oil water diversion ratio of selective proppant was greater than 1.2, while the ordinary proppant was less than 0.7. The oil wettability index of selective proppant was greater than 0.8, while the ordinary proppant was less than 0.3. The main characteristics of this supporting agent was small apparent density, low broken rate, and high flow capacity. The proppant surface is oil wet, which has the characteristics of oil increasing precipitation. Experiments showed that the seepage velocity of water in selective proppant was obviously lower than in quartz sand, selective proppant with water resistance. Field test was carried out in 11 wells with selective proppant fracturing. A group of conventional fracturing wells were selected as contrasted wells with similar reservoir and operating conditions. After fracturing with novel proppant, the average water cut of producers was decreased by 7.2% per well, the average oil production was increased by 6.3 tons per well, and the average valid fracturing a period was 14.1 months. As for contrasted wells with conventional fracturing proppant, the average single well water cut decline was only 0.4%, the average single well daily oil 3.1 tons, an average period of only 5.8 months. The novel proppant showed remarkable advantages in improving oil production and decreasing water cut. This technique has a great future for mature oilfield development.