Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. ...Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.展开更多
The PBFT (Practical Byzantine Fault Tolerance, PBFT) consensus algorithm, which addressed the issue of malicious nodes sending error messages to disrupt the system operation in distributed systems, was challenging to ...The PBFT (Practical Byzantine Fault Tolerance, PBFT) consensus algorithm, which addressed the issue of malicious nodes sending error messages to disrupt the system operation in distributed systems, was challenging to support massive network nodes, the common participation over all nodes in the consensus mechanism would lead to increased communication complexity, and the arbitrary selection of master nodes would also lead to inefficient consensus. This paper offered a PBFT consensus method (Role Division-based Practical Byzantine Fault Tolerance, RD-PBFT) to address the above problems based on node role division. First, the nodes in the system voted with each other to divide the high reputation group and low reputation group, and determined the starting reputation value of the nodes. Then, the mobile node in the group was divided into roles according to the high reputation value, and a total of three roles were divided into consensus node, backup node, and supervisory node to reduce the number of nodes involved in the consensus process and reduced the complexity of communication. In addition, an adaptive method was used to select the master nodes in the consensus process, and an integer value was introduced to ensure the unpredictability and equality of the master node selection. Experimentally, it was verified that the algorithm has lower communication complexity and better decentralization characteristics compared with the PBFT consensus algorithm, which improved the efficiency of consensus.展开更多
Aim: Assess the role of hybrid modality SPECT/CT versus planar scintigraphy in sentinel lymph node (SLN) identification in patients with breast cancer. Methods: Planar scintigraphy and hybrid modality SPECT/CT were pe...Aim: Assess the role of hybrid modality SPECT/CT versus planar scintigraphy in sentinel lymph node (SLN) identification in patients with breast cancer. Methods: Planar scintigraphy and hybrid modality SPECT/CT were performed in 23 women with breast cancer (mean age 59.5 years with range 25 - 82 years) with invasive breast cancer (T0, T1 and T2), without clinical evidence of axillary lymph node metastases (N0) and no remote metastases (M0), radiocolloid was injected in four subareolar sites. Planar and SPECT/CT images were separately interpreted. Results: SLNs were detected on lymphoscintigraphy in all patients (100%), taking into consideration both techniques (planar and SPECT-CT images). Planar images identified 45 SLNs in 23 women, with a mean of 1.95 per patient, whereas 56 SLNs were detected on SPECT/CT, increasing this mean to 2.43 per patient. Drainage to internal mammary lymph nodes was seen in 4 patients (17.39%). However, two foci of uptake were identified on planar image as hot SLN in two patients (8.69%);while they have been found as a false positive non-nodal site of uptake on SPECT/CT. Conclusion: SPECT/CT is more focused than planar scintigraphy in the detection of SLN in patients with breast cancer. It detects some lymph nodes not visible on planar images, excludes false positive uptake and exactly locates axillary and non-axillary SLNs.展开更多
文摘Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.
文摘The PBFT (Practical Byzantine Fault Tolerance, PBFT) consensus algorithm, which addressed the issue of malicious nodes sending error messages to disrupt the system operation in distributed systems, was challenging to support massive network nodes, the common participation over all nodes in the consensus mechanism would lead to increased communication complexity, and the arbitrary selection of master nodes would also lead to inefficient consensus. This paper offered a PBFT consensus method (Role Division-based Practical Byzantine Fault Tolerance, RD-PBFT) to address the above problems based on node role division. First, the nodes in the system voted with each other to divide the high reputation group and low reputation group, and determined the starting reputation value of the nodes. Then, the mobile node in the group was divided into roles according to the high reputation value, and a total of three roles were divided into consensus node, backup node, and supervisory node to reduce the number of nodes involved in the consensus process and reduced the complexity of communication. In addition, an adaptive method was used to select the master nodes in the consensus process, and an integer value was introduced to ensure the unpredictability and equality of the master node selection. Experimentally, it was verified that the algorithm has lower communication complexity and better decentralization characteristics compared with the PBFT consensus algorithm, which improved the efficiency of consensus.
文摘Aim: Assess the role of hybrid modality SPECT/CT versus planar scintigraphy in sentinel lymph node (SLN) identification in patients with breast cancer. Methods: Planar scintigraphy and hybrid modality SPECT/CT were performed in 23 women with breast cancer (mean age 59.5 years with range 25 - 82 years) with invasive breast cancer (T0, T1 and T2), without clinical evidence of axillary lymph node metastases (N0) and no remote metastases (M0), radiocolloid was injected in four subareolar sites. Planar and SPECT/CT images were separately interpreted. Results: SLNs were detected on lymphoscintigraphy in all patients (100%), taking into consideration both techniques (planar and SPECT-CT images). Planar images identified 45 SLNs in 23 women, with a mean of 1.95 per patient, whereas 56 SLNs were detected on SPECT/CT, increasing this mean to 2.43 per patient. Drainage to internal mammary lymph nodes was seen in 4 patients (17.39%). However, two foci of uptake were identified on planar image as hot SLN in two patients (8.69%);while they have been found as a false positive non-nodal site of uptake on SPECT/CT. Conclusion: SPECT/CT is more focused than planar scintigraphy in the detection of SLN in patients with breast cancer. It detects some lymph nodes not visible on planar images, excludes false positive uptake and exactly locates axillary and non-axillary SLNs.
