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The inherent structure and connectivity of a group are important features of social networks. Finding the densest subgraphs of a graph directly maps to revealing the densest communities of a social network. Various techniques, e.g., edge density, k-core, near-cliques and k-cliques, have been developed to characterize graphs and extract the densest subgraphs of the graphs. However, as extraction of subgraphs with constraints is NP-hard, these techniques face a major difficulty of processing big and/or streaming data sets from social networks. This demands new methods from the expert and intelligent systems perspective for computation of the densest subgraph problem (DSP) with big and/or streaming data. The most recent method for this purpose is the “Sampling” method. It samples the big data sets, thus reducing the data space and consequently speeding up the DSP computation. But the sampled data inevitably miss out many useful data items. A new approach is presented in this paper for accelerated DSP computation with big and/or steaming data through data space reduction without loss of useful information. It uses a sliding window of small graphs with a fixed number of edges. Then, it filters out the least connected edges for each small graph. While the small graphs are processed, subgraphs are incrementally put together to reveal the densest subgraphs. Finally, the data space previously filtered out is checked for recovery of globally important edges. The approach is incorporated with existing subgraph extraction techniques for scalable and efficient DSP computation with improved accuracy. It is demonstrated for four subgraph extraction techniques over four Twitter data sets, and is shown to outperform the “sampling” method.

In this work, we simultaneously obtained a large d33 and high TC in a lead-free piezoelectric system of (1-x)K0.5Na0.5NbO3–xBi0.5Na0.5Zr0.85Sn0.15O3 [(1-x)KNN–xBNZS]. This lead-free piezoelectric system was synthesized by conventional solid-state method. We investigated the microstructure and electrical properties of this system. The rhombohedral-tetragonal (R-T) phase coexistence is demonstrated in the ceramics with 0.05 ≤ x ≤ 0.06. Owing to the R–T phase coexistence as well as the enhancement of ferroelectric and dielectric properties, the ceramics with x = 0.05 showed a large d33 of ∼350 pC/N together with a high TC of ∼315 °C, thereby illustrating that it is an effective way to obtain both large d33 and high TC in KNN-based ceramics. It is believed that such a ceramic system with large d33 and high TC is one of the promising candidates for piezoelectric devices.

Purpose Early detection and intervention of lymphedema is essential for improving the quality of life of breast cancer survivors. Previous studies have shown that patients have symptoms such as arm tightness and arm heaviness before experiencing obvious limb swelling. Thus, this study aimed to develop a symptom-warning model for the early detection of breast cancer-related lymphedema. Methods A cross-sectional study was conducted at a tertiary hospital in Beijing between April 2017 and December 2018. A total of 24 lymphedema-associated symptoms were identified as candidate predictors. Circumferential measurements were used to diagnose lymphedema. The data were randomly split into training and validation sets with a 7:3 ratio to derive and evaluate six machine learning models. Both the discrimination and calibration of each model were assessed on the validation set. Results A total of 533 patients were included in the study. The logistic regression model showed the best performance for early detection of lymphedema, with AUC = 0.889 (0.840–0.938), sensitivity = 0.771, specificity = 0.883, accuracy = 0.825, and Brier scores = 0.141. Calibration was also acceptable. It has been deployed as an open-access web application, allowing users to estimate the probability of lymphedema individually in real time. The application can be found at https://apredictiontoolforlymphedema.shinyapps.io/dynnomapp/ . Conclusion The symptom-warning model developed by logistic regression performed well in the early detection of lymphedema. Integrating this model into an open-access web application is beneficial to patients and healthcare providers to monitor lymphedema status in real-time.

Nano-grained ceramics have their unique mechanical characteristics that are not commonly found in their coarse-grained counterparts. In this study, nano-grained YAG transparent ceramics (NG-YAG) were prepared by low-temperature high-pressure technique (LTHP). The peak profile analysis of the X-ray diffraction was employed to investigate the compressive yield strength of NG-YAG. During the temperature at 450 °C, the residual micro-strain (RMS) increased with increasing loading pressure. However when the loading pressure was exceeded to 4.0 GPa the RMS exhibited a severe negative slop. The temperature effects on the compressive yield strength were also studied. It shows that the compressive yield strength of NG-YAG is 4.0 GPa and 5.0 GPa respectively at 450 °C and 350 °C. More importantly according to this investigation, a feasible technique to study the nano-grained ceramics is provided.

We propose two new SVDD models which improve the robustness to noise.Cutoff distance-based local density can mitigate the effect of noise towards SVDD.Tolerated gap of SVDD with e-insensitive loss can improve generalization performance. As an example of one-class classification methods, support vector data description (SVDD) offers an opportunity to improve the performance of outlier detection and reduce the loss caused by outlier occurrence in many real-world applications. However, due to limited outliers, the SVDD model is built only by using the normal data. In this situation, SVDD may easily lead to over fitting when the normal data contain noise or uncertainty. This paper presents two types of new SVDD methods, named R-SVDD and eNR-SVDD, which are constructed by introducing cutoff distance-based local density of each data sample and the e-insensitive loss function with negative samples. We have demonstrated that the proposed methods can improve the robustness of SVDD for data with noise or uncertainty by extensive experiments on ten UCI datasets. The experimental results have shown that the proposed eNR-SVDD is superior to other existing outlier detection methods in terms of the detection rate and the false alarm rate. Meanwhile, the proposed R-SVDD can also achieve a better outlier detection performance with only normal data. Finally, the proposed methods are successfully used to detect the image-based conveyor belt fault.

A new system of (1− x )K 0.5 Na 0.5 NbO 3 – x Bi 0.5 Na 0.5 Zr 0.8 Ti 0.2 O 3 [KNN– x BNZT] lead-free piezoelectric ceramics has been prepared by the conventional solid state sintering method. Systematic investigations on the microstructure, crystalline structure as well as dielectric, piezoelectric and ferroelectric properties were carried out. The ceramics have a coexistence of orthorhombic and tetragonal phases near room temperature. Remarkably strong piezoelectricity of d 33 =280 pC/N and k p =38.8% has been obtained in the KNN–0.04BNZT ceramic, together with a high T C of 342 °C. It was considered that the observed strong piezoelectricity might be primarily ascribed to its orthorhombic–tetragonal phase transition near room temperature.