Alzheimer’s disease (AD) is a progressive disease that causes problems of cognitive and memory functions decline. Patients with AD usually lose their ability to manage their daily life. Exploring the progression of the brain from normal controls (NC) to AD is an essential part of human research. Although connection changes have been found in the progression, the connection mechanism that drives these changes remains incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncovers the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a mutual information brain network model (MINM) from the perspective of graph theory to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiments show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

Accurate malignancy level classification of lung nodules can reduce lung cancer mortality rate effectively. In this study, we present a fast and efficient CAD system to improve the performance of nodules malignancy level classification. Firstly, to reduce false positives (FPs), we propose a novel vessel segmentation method which measures vessel likelihood by tubular-like structures discriminating from multiple views. The method can recognize irregular vascular structures robustly and sensitively, and achieve fast vessel segmentation. In addition, a mathematical description for 3D pulmonary entities using neighbor centroids clustering is provided as a fundamental condition for spatial feature extraction. To optimize features extraction, we formulate a gray values cumulative function and a patches selection function based on the mathematical description, to generate axial spatial outline and spatial density distribution samples of the entities, respectively. Then, we use Edge Orientation Histogram (EOH) to extract edge features from the spatial outline and propose a multi-scale path LBP (MSPLBP) to extract the texture feature of the density distribution samples. Finally, the fused EOH and MSPLBP are classified into 6 malignancy levels by three state-of-the-art classifiers. The experimental results show that the vessel segmentation method achieves an average F1_Score of 78.14% and AUC value under PR curves of 0.8149. Moreover, our system reaches an average accuracy of 95.88% and consumes average 176.26 seconds for evaluating a CT set on malignancy level classification. These results indicate that the system can segment vessels exactly, and classify the malignancy level of nodules efficiently. Our system is the potential to be a powerful tool for early diagnosis of lung cancer.

Wireless sensor networks (WSNs) deployed in harsh environments, i.e., battlefield and natural disasters areas, often suffer from the problems of the deliberate attack, hardware failure, and energy depletion. It is crucial to propose the fault-tolerant and energy-efficient evolution models to accomplish tasks of applications in these environments. Previous studies have reported that scale-free (SF) topologies can improve the fault-tolerance of WSNs. However, energy-efficiency is less concerned in most of these SF WSNs models. In addition, previous models took only node degree into account in calculating connection probabilities between two nodes, topological characteristics, which can enhance reliable communication, are rarely considered. To address these shortcomings, we present three new SF-evolution mechanisms (ECN, ELP, and ELCP) of large-scale WSNs from the novel aspect of link prediction. Specifically, three well-known link prediction methods, which are topology concerned, named common neighbors (CN), local path (LP), and local community paradigm (LCP) are adopted in the corresponding models to enhance energy-efficiency of SF-WSNs. Meanwhile, residual energy and degree capacity are taken into consideration in the proposed models to balance the node energy consumption. We also explore the degree distribution of networks generated by our models and present the theoretical derivation of it. The experimental results demonstrate that our proposed models can generate SF-WSNs topologies with better fault-tolerance and higher energy-efficiency by comparing with a candidate clustering-based algorithm and other two SF enhancing algorithms. Moreover, ELCP’s performance is the best among the three models, followed by ELP and ECN.

Edge nodes provide service cooperatively in edge computing, where third-party nodes are common. However, they cannot be fully trusted and can intentionally alter service results (e.g., edge tampering). Although some mechanisms can help detect edge tampering, they come with additional detection overhead. It is essential to note that most edge nodes are willing to serve honestly. Therefore, it is reasonable to decrease the detection frequency for those nodes, which helps reduce the overhead. Reputation is the common way to evaluate trustworthiness. In this paper, we propose a reputation incentive scheme called RIETD, which evaluates the reputations of edge nodes using their detection results. Moreover, RIETD is loosely coupled with detection mechanisms as an external service. Reputation is the fundamental parameter in RIETD, as it determines an edge node’s appraisal weight on other nodes, personalized detection strategy, and node’s obtained revenues in one service. We demonstrate that RIETD does not significantly reduce the overall detection capability while the overhead is reduced effectively. For instance, when the tampering rate of edge nodes is 10%, and the target detection rate is 90% of a specific detection mechanism, RIETD reduces overhead by approximately 60%. If a full-reputation node is detected to have tampered with the results, its reputation recovery time is similar to the time required for reputation to improve from 0 to 1. Moreover, a node’s expected revenue is lower than that of an honest node, emphasizing the importance of serving honestly and continuously for edge nodes to earn higher revenue.