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Most of the existing influence maximization problem in social networks only focus on single relationship social networks, that is, there is only one relationship in social networks. However, in reality, there are often many relationships among users of social networks, and these relationships jointly affect the propagation of network information and its final scope of influence. Based on the classical linear threshold model and combined with various relationships between network nodes, in this paper MRSN-LT propagation model is proposed to model the influence propagation process between nodes in multiple relationships social networks. Then, MRSN-RRset algorithm based on reverse reachable set is proposed to solve the problem of low computational performance caused by greedy algorithm in the research process of traditional influence maximization. Finally, the experimental results on real data sets show that the proposed method has better influence propagation scope and greater computational performance improvement.

The novel coronavirus is a new type of virus, and its transmission characteristics are different from the previous virus. Based on the SEIR transmission model, this paper redefines the latent state as close contacts state, introduces an asymptomatic infection state, and considers the influence of time on the state transition parameters in the model, proposing a new transmission model. The experimental results show that the fitting accuracy of the model has significantly improved. Compared with the traditional model, the fitting error was reduced by 8.3%-47.6%. Also, this study uses the US epidemic data as the training set to predict the development of the US epidemic, and the forecast results show that the US epidemic cannot be quickly controlled in a short time. However, the number of active cases will usher in a rapid decline after August 2021.

Gastric cancer (GC) remains one of the prevalent causes of cancer-related deaths globally. Long non-coding RNAs (lncRNAs) have been associated with different cancers. The polarization of macrophages towards the M2 (alternatively activated) phenotype promotes immunologic tolerance and can induce gastric tumorigenesis. Thus far, lncRNAs have been shown to modulate the differentiation of immune cells. Here, we investigated the biological effects of LINC00665 on the progression of GC and explored the mechanisms underlying its ability to mediate the polarization of macrophages towards the M2 phenotype. We report that the levels of LINC00665 were increased in GC tissues. Furthermore, this increase in LINC00665 expression could be associated with decreased overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS). Using cell-based macrophage polarization models, we demonstrated that LINC00665 upregulation in GC cells facilitated the polarization of macrophages towards the M2 but not M1 (classically activated) phenotype. Furthermore, the loss of LINC00665 prevented the M2 polarization of macrophages. Mechanically, we identified that Wnt1 was the downstream target of LINC00665. Additionally, LINC00665 could directly interact with the transcription factor BTB domain and CNC homology 1 (BACH1). The interaction between LINC00665 and BACH1 resulted in the activation and binding of BACH1 to the Wnt1 promoters. Furthermore, BACH1 silencing could inhibit GC progression, which highlighted a crucial role for BACH1 in LINC00665-mediated Wnt1 activation. In addition, genetic Wnt1 overexpression effectively abolished the repression of Wnt signaling after BACH1 depletion and mediated GC development by supporting M2 macrophage polarization. In conclusion, we report that LINC00665 modulates M2 macrophage polarization and suggest that it may facilitate macrophage-dependent GC progression.

Online labor platforms (OLPs) can use algorithms to strengthen the control of the labor process. In fact, they construct work circumstances with higher work requirements and pressure. Workers’ autonomy in behavior is limited, which will have a great influence on their labor psychology. In this paper, taking the online take-out platform as an example and by using a qualitative study of take-out riders’ delivery processes, which were supplemented by semi-structured, in-depth interviews with online platform executives and engineers, we used grounded theory to explore the influencing factors of OLPs’ algorithmic management on take-out riders’ working psychology. The quantitative analysis results showed that, in the context of conflict between work autonomy and algorithmic management, platform workers experienced psychological tensions relating to work satisfaction, compensation, and belonging. Our research contributes to protect public health and labor rights of OLP workers.

In order to reduce the energy consumption and the cost of wireless sensor networks (WSNs) deployed in linear areas and prolong the life of the network, a deployment strategy of nodes in WSNs based on “X” partition was proposed in this paper. The monitored area was partitioned based on “X” shape, and the sensor nodes were deployed, so as to make the whole area be covered and the number of nodes deployed be reduced. At the same time, the monitoring units are divided and compressed to balance and save energy of the network and prolong life of the network. Through experiment verification, compared with traditional partition deployment strategy, the deployment cost of network can be reduced effectively by the proposed strategy. In terms of the life of the network, the proposed strategy is longer than the diamond partition strategy more than 50%.

Three-dimensional convolutional network (3DCNN) is an essential field of motion recognition research. The research work of this paper optimizes the traditional three-dimensional convolution network, introduces the self-attention mechanism, and proposes a new network model to analyze and process complex human motion videos. In this study, the average frame skipping sampling and scaling and the one-hot encoding are used for data pre-processing to retain more features in the limited data. The experimental results show that this paper innovatively designs a lightweight three-dimensional convolutional network combined with an attention mechanism framework, and the number of parameters of the model is reduced by more than 90% to only about 1.7 million. This study compared the performance of different models in different classifications and found that the model proposed in this study performed well in complex human motion video classification. Its recognition rate increased by 1%–8% compared with the C3D model.

The human brain is an organic which whole consisting of hundreds of billions of neurons, and the neural network is the information path of the brain for processing information. Network characteristics will change along with network topology or node change, and the change may lead to brain lesions. Using EEG to construct brain network can effectively discover the internal mechanism of improvement in human brain, but the traditional method of constructing brain network is difficult to reflect the network complexity because of the small number of network nodes. In this paper, a new algorithm of brain network construction based on chaotic time series is proposed. The method estimates neuronal dynamic equations and neuronal networks topology by using only noisy time series. By analyzing the network constructed based on phase space reconstruction of Lorenz system, network characteristics that the average path length increase as embedding dimension, and the cluster coefficient on the contrary are found. The topology and statistical characteristics of the network constructed from neuronal chaotic bursting can reflect time-evolution of time series. Through analyzing the real epileptic EEG signals dataset using the proposed complex networks construction method, we found that epileptic seizure is different from seizure-free intervals in network topology and statistical characteristics. Epileptic EEG of before, during and after a tonic-clonic seizure indicate that cluster coefficients of the sliding local networks significantly increased during a seizure, so the result can provide clonic seizure prediction. The proposed method of complex network topology estimation and complex network construction of time series can depict the dynamic characteristics of brain network, and provide a new idea for the treatment of mental illness.

In this paper, electroencephalography data are used to establish a functional network connecting correlated human brain regions. Through analysis, it is found that the resulting network shows statistical characteristics of a complex network: its clustering coefficient is orders of magnitude larger than that of the equivalent random network, which is typical of a small-world network, and the distribution of degree is close to that of a scale-free network. All these characteristics reflect important functional information about brain states. For alcohol addicts, the characteristic indices of their brains are obviously different from those of the control group. The information entropy and standard information entropy of the brain neural network are also defined to measure the characteristics of the complex network. This gives a new criterion for clinical diagnosis and treatment of encephalopathy. Calculation results indicate that the brain neural network information entropy of alcohol addicts is quite distinct from that of the control group.

In order to solve the problem of safe transmission of telemedicine information in complex field environment, a new communication protocol of wireless sensor network (WSN) is designed in this paper. After the design of the communication protocol, the typical field environment is selected for testing. Based on the propagation characteristics of the wireless channel, the propagation model and attenuation law of WSN radio frequency signal in complex field environment are studied under the premise of fixed communication rate, antenna gain, communication distance, and other parameters. The errors in the actual test are corrected in time to guide the deployment of WSN. The experimental data are fitted by the least square method and linear regression analysis. The experimental results show that the channel models of telemedicine information wireless signals in complex field environment are quite different in different altitudes and a different climate space.

Most of the existing influence maximization algorithms are not suitable for large-scale social networks due to their high time complexity or limited influence propagation range. Therefore, a D-RIS (dynamic-reverse reachable set) influence maximization algorithm is proposed based on the independent cascade model and combined with the reverse reachable set sampling. Under the premise that the influence propagation function satisfies monotonicity and submodularity, the D-RIS algorithm uses an automatic debugging method to determine the critical value of the number of reverse reachable sets, which not only obtains a better influence propagation range but also greatly reduces the time complexity. The experimental results on the two real datasets of Slashdot and Epinions show that D-RIS algorithm is close to the CELF (cost-effective lazy-forward) algorithm and higher than RIS algorithm, HighDegree algorithm, LIR algorithm, and pBmH (population-based metaheuristics) algorithm in influence propagation range. At the same time, it is significantly better than the CELF algorithm and RIS algorithm in running time, which indicates that D-RIS algorithm is more suitable for large-scale social network.

With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship.

Different from previous complex network models based on graph theory, multi-functional complex network model (MFCN) only uses nodes to represent, and the content of graph theory is no longer applicable in it. In order to better apply MFCN, the storage structure, construction algorithm and traversal algorithm are defined and verified in the paper. The advantages of MFCN over the previous complex network models in constructing network are discussed.

In order to solve the cascading failure problem of real networks, it is important to study the robustness of coupled networks. In this paper, a multi-subnet complex complex network model is used to build a cascading failure model on the coupled network. The influence of network size, adjustable parameters, and coupling factors on the robustness of the coupled network is mainly studied. Research shows that when both subnets are BA networks, the greater the contribution of the coupling relationship to the initial load of the nodes, and adopt heterogeneous coupling, networks will more robust.

The influence maximization problem (IMP) is defined to select the node group with the maximum propagation range from the social network through using an information propagation model. However, the existing influence maximization algorithm is unfitting for large size networks because of the high time complexity. A SRIS algorithm based on reverse reachable set is proposed, greatly improves the running time efficiency without compromising its asymptotic performance. Finally, the experiment simulation results indicated that SRIS model was more suitable for large-scale social networks.

A real society has multiple social relationships between users, but existing social network recommendation algorithms often only introduce a social relationship into the recommendation system. This paper introduces a variety of social relationships into the recommendation system based on a multi-subnet composite complex network model. Based on the analysis of the experimental results on the Epinions dataset, a recommendation algorithm introducing multiple social relationships has a significantly higher recommendation accuracy than a recommendation algorithm.

Social recommendation algorithm is a common tool for recommending interesting or potentially useful items to users amidst the sea of online information. The users usually have various relationships, each of which has its unique impact on the recommendation results. It is unlikely to make accurate recommendations solely based on one relationship. Based on user-item bipartite graph, this paper establishes a multisubnet composited complex network (MSCCN) of multiple user relationships and then extends the mass diffusion (MD) algorithm into a novel intelligent recommendation algorithm. Two public online datasets, namely, Epinions and FilmTrust, were selected to verify the effect of the proposed algorithm. The results show that the proposed intelligent recommendation algorithm with two types of relationships made much more accurate recommendations than that with a single relationship and the traditional MD algorithm.

Opinion leaders in online social networks are important for various fields such as public opinion propagation, marketing management, administrative science and even politics. There are often many kinds of relationships in an online social network. Detecting and identifying opinion leaders depending on any one kind of relationship is inaccurate. In this paper, node importance analysis in multi-relationship online social networks was proposed by signalling based on Multi-subnet Composited Complex Networks Model, and considering the characteristics of multiple relationships which would interrelate with each other. Through node importance under multiple relationships, the novel opinion leader detecting algorithm in multi-relationship online social networks is proposed and approved to be efficient by experiments described in this paper.

Diabetic patients with high glucose exhibit vascular smooth muscle cell (VSMC) alteration. Thrombotic disease is related to erosion of an unstable plaque, the instability of which leads to ruptures, for example, a thin fibrous cap derived from VSMCs. VSMC proliferation, migration and invasion are related to thrombotic diseases, including atherosclerosis. MicroRNA‑19a (miR‑19a) has been reported to have pleiotropic functions in cancer cell survival, apoptosis and migration. The present study aimed to investigate the effect of miR‑19a on VSMC proliferation, migration and invasion, and its mechanism. Cell Counting Kit‑8 and a propidium iodide kit were used to determine the proliferation and cycle of VSMCs. A cell migration assay was performed by scratching and Matrigel was used in a cell invasion assay. miR‑19a binding to Ras homolog family member B (RHOB), and their protein and mRNA expressions were determined by performing a dual luciferase assay, western blotting and reverse transcription‑quantitative PCR, respectively. It was demonstrated that miR‑19a promoted the proliferation, migration and invasion of VSMCs, promoted the expressions of dual specificity phosphatase Cdc25A (CDC25A), cyclinD1, matrix metalloproteinase (MMP)‑2, MMP‑9, α‑smooth muscle actin (α‑SMA) and smooth muscle 22α (SM22α), and inhibited suppressor of cytokine signaling 3 and RHOB expressions in VSMCs, while miR‑19a had no effect on the expression of T‑cell intracellular antigen‑1. The miR‑19a site bound to the RHOB gene position and inhibited RHOB to promote VSMC proliferation, invasion and migration, and increased MMP‑2, MMP‑9, α‑SMA and SM22α expressions. The present study suggested that miR‑19a could promote VSMC proliferation, migration and invasion via the cyclinD1/CDC25A and MMP/α‑SMA/SM22α signaling pathways. Moreover, miR‑19a promoted proliferation, migration and invasion via the MMP/α‑SMA/SM22α signaling pathway by inhibiting RHOB, suggesting that miR‑19a is a possible regulatory factor of RHOB.

Based on cellular automaton, combine compartment model, proposed SEIDRS model that can reflect the actual propagation process of infectious diseases. This model combined with factors such as population density, sexual proportion and age structure, rules the evolution rules of the model. Using Matlab to simulate the model, the simulation results show that the larger population density, the infectious disease propagation faster; There are differences in some aspects in sexual proportion, sexual have larger number and high impact factor, make more obvious effects on the spread of infectious diseases; There are differences in age structure, the city have more aged and light people have lower defense capability to infectious diseases, the appropriate age distribution play a more active role to resist the spread of infectious diseases.

Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

Android platforms and its applications (apps) have gained tremendous popularity recently, hence the reliability of Android apps is becoming increasingly important. Due to the novelty of the Android platform, apps are prone to errors, which would affect user experience and requires frequent bug fixes. In this paper, an empirical study on bugs in some widely-used open-source Android apps from diverse categories such as media, tools and communication were performed. Based on the findings of the empirical study, an approach for automating the testing process for detecting Android apps GUI bugs was presented. We show how the approach helped to re-discover existing bugs and find new bugs, and how it could be used to prevent certain bug categories. Our empirical study and automated testing approach have the potential to help developers increase the quality of Android apps.

With the popularity of online social networks, such as Facebook, YouTube and Douban, have become a channel for fast information propagation and influence. The problem of discovering common interests shared by groups of users has become one of the central researches because it helps to connect users with common interests and can eventually influence as many users with common interests in the online social network as possible. Unfortunately, most of existing related works have only focused on the network topologies and so unable to identify the common interests of users who have no online connections. In an online social network, users tend to use descriptive tags to annotate the contents that they are interested in. User-generated tags are consistent with the content they are attached to. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. In this paper, we propose a novel common interests discovery approach based on user-generated tags to discover the set of interest-matching users whose interests are similar. The experiment results show that our approach can effectively discover user communities with common interests no matter if they have any online connections.

A new CDMA system which uses fractional chip shift expanded generalized complementary orthogonal code groups as spectrum spreading code sequences and offer high spectral efficiency is introduced in the paper. This new CDMA system is totally different with conventional CDMA system in access codes designing and spectrum spreading process. In this CDMA system, the access codes of different cells keep orthogonality, which eliminates inter cells interferences (ACI) by aperiodic crosscorrelation function (CCF). The spreading process of new CDMA system introduces inter symbols interference like overlap time division multiplexing (OVTDM) deliberately so as to improve spectral efficiency. The construction of expanded generalized complementary orthogonal code groups, shift method, principle of new CDMA system are explained respectively. Analysis and simulation results verify the validity of this new CDMA system.

For solving the problem of heterogeneous node energy and low energy efficiency in wireless sensor networks (WSNs), an effective and dynamic self-organization evolution model by introducing a complex network model is proposed in this paper. In the process of the evolution of WSN topology, it is considered that the nodes and links in the network are dynamic behaviors with increasing and decreasing, and combined this behavior with energy-awareness mechanism. The preferred attached mechanism and the anti-preferred deleted mechanism is closely related to the remaining node energy. The statistical characteristics of this network model are analyzed by theoretical calculation and mathematical simulation under different energy distribution functions. Through the simulation experiment, it can be proven that the proposed evolution model will cause the energy consumption of the whole wireless sensor network to be more balanced; at the same time, it would have an excellent performance of random node error tolerance.

Liver disease is one of the main causes of human healthy problem. Cirrhosis, of course, is the critical phase during the development of liver lesion, especially the hepatoma. Many clinical cases are still influenced by the subjectivity of physicians in some degree, and some objective factors such as illumination, scale, edge blurring will affect the judgment of clinicians. Then the subjectivity will affect the accuracy of diagnosis and the treatment of patients. In order to solve the difficulty above and improve the recognition rate of liver cirrhosis, we propose a method of multi-feature fusion to obtain more robust representations of texture in ultrasound liver images, the texture features we extract include local binary pattern(LBP), gray level co-occurrence matrix(GLCM) and histogram of oriented gradient(HOG). In this paper, we firstly make a fusion of multi-feature to recognize cirrhosis and normal liver based on parallel combination concept, and the experimental results shows that the classifier is effective for cirrhosis recognition which is evaluated by the satisfying classification rate, sensitivity and specificity of receiver operating characteristic(ROC), and cost time. Through the method we proposed, it will be helpful to improve the accuracy of diagnosis of cirrhosis and prevent the development of liver lesion towards hepatoma.