Epidemic Spreading–Information Dissemination Coupling Mechanism in Heterogeneous Areas

With COVID-19 continuing to rage around the world, there is a spread of epidemic-related information on social networking platforms. This phenomenon may inhibit or promote the scale of epidemic transmission. This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission. We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission. We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission. The simulation results showed that the higher the information dissemination rate, the larger the scale of information dissemination and the smaller the scale of epidemic transmission. In addition, the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic, the smaller the scale of information dissemination and the smaller the scale of epidemic transmission. Moreover, the greater the probability of individuals moving across regions, the larger the spread of the epidemic and information. Finally, the higher the probability of an individual taking preventive behavior, the smaller the spread of the epidemic and information. Therefore, it is possible to suppress epidemic spread by increasing the information dissemination rate, epidemic recovery rate, and probability of individuals taking preventive behavior, while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.     

Cooperative Perception Optimization Based on Self-Checking Machine Learning

In the process of spectrum perception, in order to realize accurate perception of the channel state, the method of multi-node cooperative perception can usually be used. However, the first problem to be considered is how to complete information fusion and obtain more accurate and reliable judgment results based on multi-node perception results. The ideas put forward in this paper are as follows: firstly, the perceived results of each node are obtained on the premise of limiting detection probability and false alarm probability. Then, on the one hand, the weighted fusion criterion of decision-making weight optimization of each node is realized based on a genetic algorithm, and the useless nodes also can be screened out to reduce energy loss; on the other hand, through the linear fitting ability of RBF neural network, the self-inspection of the perceptive nodes can be realized to ensure the normal operation of the perceptive work of each node. What's more, the real-time training data can be obtained by spectral segmentation technology to ensure the real-time accuracy of the optimization results. Finally, the simulation results show that this method can effectively improve the accuracy and stability of channel perception results, optimize the structure of the cooperative network and reduce energy consumption.     

Influence of network dynamics on the spread of sexually transmitted diseases

Network epidemiology often assumes that the relationships defining the social network of a population are static. The dynamics of relationships is only taken indirectly into account by assuming that the relevant information to study epidemic spread is encoded in the network obtained, by considering numbers of partners accumulated over periods of time roughly proportional to the infectious period of the disease. On the other hand, models explicitly including social dynamics are often too schematic to provide a reasonable representation of a real population, or so detailed that no general conclusions can be drawn from them. Here, we present a model of social dynamics that is general enough so its parameters can be obtained by fitting data from surveys about sexual behaviour, but that can still be studied analytically, using mean-field techniques. This allows us to obtain some general results about epidemic spreading. We show that using accumulated network data to estimate the static epidemic threshold lead to a significant underestimation of that threshold. We also show that, for a dynamic network, the relative epidemic threshold is an increasing function of the infectious period of the disease, implying that the static value is a lower bound to the real threshold. A practical example is given of how to apply the model to the study of a real population.     

An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment

Distributed denial-of-service (DDoS) is a rapidly growing problem with the fast development of the Internet. There are multitude DDoS detection approaches, however, three major problems about DDoS attack detection appear in the big data environment. Firstly, to shorten the respond time of the DDoS attack detector; secondly, to reduce the required compute resources; lastly, to achieve a high detection rate with low false alarm rate. In the paper, we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and solve aforementioned problems. We define a network flow abnormal index as PDRA with the percentage of old IP addresses, the increment of the new IP addresses, the ratio of new IP addresses to the old IP addresses and average accessing rate of each new IP address. We design an IP address database using sequential storage model which has a constant time complexity. The autoregressive integrated moving average (ARIMA) trending prediction module will be started if and only if the number of continuous PDRA sequence value, which all exceed an PDRA abnormal threshold (PAT), reaches a certain preset threshold. And then calculate the probability that is the percentage of forecasting PDRA sequence value which exceed the PAT. Finally we identify the DDoS attack based on the abnormal probability of the forecasting PDRA sequence. Both theorem and experiment show that the method we proposed can effectively reduce the compute resources consumption, identify DDoS attack at its initial stage with higher detection rate and lower false alarm rate.     

Vortex Formation in Neutron-Irradiated Rotating Superfluid 3He-B

A convenient method to create vortices in meta-stable vortex-free superflow of ³He-B is to irradiate with thermal neutrons. The vortices are then formed in a rapid non-equilibrium process with distinctive characteristics. Two competing explanations have been worked out about this process. One is the Kibble-Zurek mechanism of defect formation in a quench-cooled second order phase transition. The second builds on the instability of the moving front between superfluid and normal ³He, which is created by the heating from the neutron absorption event. The most detailed measurements with single-vortex resolution have been performed at temperatures close to T_c. In the first half of this report we summarize the two models and then show that the experimentally observed vortices originate from the Kibble-Zurek mechanism. In the second half we present new results from low temperatures. They also weakly support the Kibble-Zurek origin, but in addition display superfluid turbulence as a new phenomenon. Below 0.6 T_c the damping of vortex motion from the normal component is reduced sufficiently so that turbulent vortex dynamics become possible. Here a single absorbed neutron may transfer the sample from the meta-stable vertex-free to the equilibrium vortex state. The probability of a neutron to initiate a turbulent transition grows with increasing superflow velocity and decreasing temperature. PACS numbers: 47.32, 67.40, 67.57, 98.80.     

Reliability of a Multi‐State Computer Network Through k Minimal Paths Within Tolerable Error Rate and Time Threshold

A computer system is usually modeled as a network topology where each branch denotes a transmission medium and each vertex represents a station of servers. Each branch has multiple capacities/states due to failure, partial failure, and maintenance. Such a network is named a multi‐state computer network (MSCN). From the viewpoint of quality management, transmission error rate and transmission time are both critical performance indicators to assess Internet quality for system managers and customers. Within both tolerable error rate and time threshold, the addressed problem is concentrated on an MSCN for computing the probability that d units of data can be sent through multiple minimal paths simultaneously. Such a probability is named system reliability. A solution procedure including an efficient algorithm based on MPs is proposed to derive the lower boundary vectors (LBVs) meeting the requirements. Then system reliability, which is represented as the probability of union of subsets, can be subsequently evaluated by the LBVs. Copyright © 2015 John Wiley & Sons, Ltd.     

Integrated production maintenance and quality model for imperfect processes

In this paper, we develop an integrated model for the joint optimization of the economic production quantity, the economic design of x¯-control chart, and the optimal maintenance level. This is done for a deteriorating process where the in-control period follows a general probability distribution with increasing hazard rate. In the proposed model, Preventive Maintenance (PM) activities reduce the shift rate to the out-of-control state proportional to the PM level. Compared to the case with no PM, the extra cost of maintenance results in lower quality control cost which may lead to lower overall expected cost. These issues are illustrated using an example of a Weibull shock model with an increasing hazard rate.     

基于粒子群算法的水声通信网络能量优化方法

水声通信网络节点功耗是影响网络节点寿命的重要因素之一。针对水声通信网络的能量优化问题,基于网络节点发射功率与传输距离的非线性关系,结合改进的粒子群算法建立了一种能量优化方法。该方法改进了网络模型,在网络运行中根据存活节点数量和节点剩余能量的变化情况,自适应动态优化每个节点的信息传输路径。仿真结果表明,所提优化方法能有效降低网络节点总功耗,延缓首个节点的死亡,减缓网络中节点的死亡速率,也即减缓了网络有效覆盖面积随着网络运行而减小的速率。     

Complex contagion process in spreading of online innovation

Diffusion of innovation can be interpreted as a social spreading phenomenon governed by the impact of media and social interactions. Although these mechanisms have been identified by quantitative theories, their role and relative importance are not entirely understood, as empirical verification has so far been hindered by the lack of appropriate data. Here we analyse a dataset recording the spreading dynamics of the world''s largest Voice over Internet Protocol service to empirically support the assumptions behind models of social contagion. We show that the rate of spontaneous service adoption is constant, the probability of adoption via social influence is linearly proportional to the fraction of adopting neighbours, and the rate of service termination is time-invariant and independent of the behaviour of peers. By implementing the detected diffusion mechanisms into a dynamical agent-based model, we are able to emulate the adoption dynamics of the service in several countries worldwide. This approach enables us to make medium-term predictions of service adoption and disclose dependencies between the dynamics of innovation spreading and the socio-economic development of a country.     

A comparative analysis of synthetic genetic oscillators

Synthetic biology is a rapidly expanding discipline at the interface between engineering and biology. Much research in this area has focused on gene regulatory networks that function as biological switches and oscillators. Here we review the state of the art in the design and construction of oscillators, comparing the features of each of the main networks published to date, the models used for in silico design and validation and, where available, relevant experimental data. Trends are apparent in the ways that network topology constrains oscillator characteristics and dynamics. Also, noise and time delay within the network can both have constructive and destructive roles in generating oscillations, and stochastic coherence is commonplace. This review can be used to inform future work to design and implement new types of synthetic oscillators or to incorporate existing oscillators into new designs.     

Modelling gambiense human African trypanosomiasis infection in villages of the Democratic Republic of Congo using Kolmogorov forward equations

Stochastic methods for modelling disease dynamics enable the direct computation of the probability of elimination of transmission. For the low-prevalence disease of human African trypanosomiasis (gHAT), we develop a new mechanistic model for gHAT infection that determines the full probability distribution of the gHAT infection using Kolmogorov forward equations. The methodology allows the analytical investigation of the probabilities of gHAT elimination in the spatially connected villages of different prevalence health zones of the Democratic Republic of Congo, and captures the uncertainty using exact methods. Our method provides a more realistic approach to scaling the probability of elimination of infection between single villages and much larger regions, and provides results comparable to established models without the requirement of detailed infection structure. The novel flexibility allows the interventions in the model to be implemented specific to each village, and this introduces the framework to consider the possible future strategies of test-and-treat or direct treatment of individuals living in villages where cases have been found, using a new drug.     

Large-scale initiated chemical vapor deposition of poly(glycidyl methacrylate) thin films

The initiated chemical vapor deposition (iCVD) of poly(glycidyl methacrylate) (PGMA) was scaled up using dimensionless analysis. In the first stage, PGMA was deposited onto a large stationary substrate and a deposition rate as high as 85 nm/min was achieved. It was found that the deposition rate increases with increasing filament temperature, whereas the deposition rate and the number-average molecular weight decrease with increasing substrate temperature. In the second stage, PGMA was deposited onto a moving substrate. At speeds between 20 mm/min and 60 mm/min, the deposition rate on the moving substrate was found to be equal to the deposition rate on the stationary substrate. Fourier transform infrared spectroscopy showed that the epoxide functionality of the PGMA films was retained during the iCVD process. Since the iCVD polymerization of different vinyl monomers all use similar parameters, this scale up can be applied to the scale up of other vinyl monomers such as 2-hydroxyethyl methacrylate and perfluoroalkyl ethyl methacrylate.     

Acquisition of satellite navigation signals using dynamically chosen measurements

A new approach to sequential acquisition of satellite navigation signals is presented where the collection of measurement statistics is steered dynamically during the acquisition process. The process consists of a fixed number of operation cycles during which the state of the receiver is maintained as a vector of conditional probabilities of acquisition hypotheses, each hypothesis being associated with a code delay and a Doppler frequency. During each operation cycle, one hypothesis is singled out by applying a suitable policy on the state vector. The down-converted satellite signal is then despread and frequency shifted according to the hypothesis. The resulting signal is integrated and squared to form a statistic that is used to update the state vector according to Bayes? theorem. After completing the cycles, a decision is made in favour of the hypothesis corresponding to the highest posterior probability. Two policies are investigated, one based on the maximisation of one-step posterior probability and the other based on the maximisation of information gain. Simulation results are presented indicating that the proposed approach provides a significant performance advantage over standard techniques for a wide range of signal-to-noise ratios.     

Extracting knowledge patterns with a social network analysis approach: an alternative methodology for assessing the impact of power inventors

This paper proposes a new, alternative analysis of patent data in order to extract knowledge patterns from inventors’ collaboration networks. Indeed, moving from a basic network analysis, we provide new developments to map and study co-inventorship. The goal of this research is to provide an overall understanding of the dynamics concerning knowledge flows in inventive activities. We show how the network of inventors is, on average, increasing in size: more and more inventors are contributing to technology innovations and they are more connected to each other. We also show to what extent inventors from different countries tend to cooperate with their local peers or internationally. Furthermore, an analysis of the clustering of inventors is carried out to show differences across countries in the structure of inventors’ communities, with a particular focus on the dynamics of collaboration for power inventors (i.e. star inventors).     

An investigation into the links between island patterns and the mechanisms of thin film deposition

Statistical analysis of pictures of island arrays grown on a substrate during thin-film deposition can yield useful information to the experimentalist such as the shape of the island size distribution. We present an investigation of how this type of information can in turn be used to identify the most important mechanisms governing the growth process. We have used a computational model based on deposition, diffusion, and aggregation (DDA) for the following situations: (1) normal DDA where the island size distributions scale with coverage and are distinguished by the critical island size; (2) DDA plus monomer evaporation, which yields scaling island size distributions that lie between a powerlaw and the normal DDA results depending on the evaporation rate; (3) DDA with a finite probability of islands absorbing monomers, which yields similar distributions to case (2) but crucially does not display the scaling with coverage; and (4) DDA with mobile islands where an even more dramatic departure from scaling is observed.     

A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function

The existing network security situation assessment methods cannot effectively assess the Distributed denial-of-service (DDoS) attack situation. In order to solve these problems, we propose a DDoS attack situation assessment method via optimized cloud model based on influence function. Firstly, according to the state change characteristics of the IP addresses which are accessed by new and old user respectively, this paper defines a fusion feature value. Then, based on this value, we establish a V-Support Vector Machines (V-SVM) classification model to analyze network flow for identifying DDoS attacks. Secondly, according to the change of new and old IP addresses, we propose three evaluation indexes. Furthermore, we propose index weight calculation algorithm to measure the importance of different indexes. According to the fusion index, which is optimized by the weighted algorithm, we define the Risk Degree (RD) and calculate the RD value of each network node. Then we obtain the situation information of the whole network according to the RD values, which are from each network nodes with different weights. Finally, the whole situation information is classified via cloud model to quantitatively assess the DDoS attack situation. The experimental results show that our method can not only improve the detection rate and reduce the missing rate of DDoS attacks, but also access the DDoS attack situation effectively. This method is more accurate and flexible than the existing methods.     

Production policies under deteriorating process conditions

This paper examines a single-stage production system that manufactures multiple products under deteriorating equipment conditions. The machine condition worsens with production, and improves with maintenance. The condition of the process can be in any one of several discrete states, and transitions from state to state follow a semi-Markov process. In many production environments, the quality or yield of output depends heavily on the condition of the production process. The problem considers the trade-offs between manufacturing products that have a higher profit, a longer processing time, and therefore, a higher deterioration probability versus products that have a smaller profit, shorter processing time with a lower process deterioration probability. The firm needs to determine the optimal production choice in each state in a way that maximizes the long-run expected average reward per unit time.

The paper makes three sets of contributions. First, it introduces the concept of critical ratios for the firm's manufacturing decision at each state regarding whether to switch from one product to another. Second, through the use of critical ratios, the main result shows that the optimal production choice for each state can be determined independently of the actions taken in other states, despite the complex interconnections between the production decisions and state transitions. Third, the paper provides generalizations that illustrate the depth, scope and richness of the proposed solution technique by extending the model in the number of machine states, to settings where maintenance is performed in intermediate states, and to settings where transition probabilities are influenced by both mean and variance of processing times.     

The Effects of Electric Field on a Triangular Bound Potential Quantum Dot Qubit

On the condition of electron-LO-phonon strong coupling in a triangular bound potential quantum dot, we obtain the eigenenergy and eigenfuctions of the ground state and the first-excited state by using the Pekar type of variational method. This two-level system in a quantum dot can be employed as a qubit, which is a basic unit for quantum information operation and storage. Our numerical results indicate that the oscillation period of this qubit is an increasing function of the confinement length and the electric field. The influence of electric field on the period of oscillation becomes greater when the confinement length is increased. The electron probability density of the qubit is an increasing function of the electron-LO-phonon coupling constant. On the contrary, it is a decreasing function of the electric field. Meanwhile, the electron probability density varies periodically with the polar angle.     

Evaluation of the reliability of transport networks based on the stochastic flow of moving objects

In transport networks, human beings are moving objects whose moving direction is stochastic in emergency situations. Based on this idea, a new model—stochastic moving network (SMN) is proposed. It is different from binary-state networks and stochastic-flow networks. The flow of SMNs has multiple-saturated states, that correspond to different flow values in each arc. In this paper, we try to evaluate the system reliability, defined as the probability that the saturated flow of the network is not less than a given demand d. Based on this new model, we obtain the flow probability distribution of every arc by simulation. An algorithm based on the blocking cutset of the SMN is proposed to evaluate the network reliability. An example is used to show how to calculate the corresponding reliabilities for different given demands of the SMN. Simulation experiments of different size were made and the system reliability precision was calculated. The precision of simulation results also discussed.     

Edge states intermediate between laminar and turbulent dynamics in pipe flow

We studied the dynamics near the boundary between laminar and turbulent dynamics in pipe flow. This boundary contains invariant dynamical states that are attracting when the dynamics is confined to the boundary. These states can be found by controlling a single quantity, in our case the energy content. The edge state is dominated by two downstream vortices and shows intrinsic chaotic dynamics. With increasing Reynolds number the separation between the edge state and turbulence increases. We can track it down to Re=1900, where the turbulent lifetimes are short enough that spontaneous decay can also be seen in experiments.