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In order to effectively prevent and combat online public opinion crises triggered by major emergencies, this paper explores the dissemination mechanism of uncertain information on online social platforms. According to the decision-making behavior of netizens after receiving uncertain information, they are divided into eight categories. Considering that there will be a portion of netizens who clarify uncertain information after receiving it, this paper proposes a SEFTFbTbMR model of uncertain information clarification behavior. The propagation dynamics equations of the model are given based on the theory of differential equations, the basic regeneration number R0 of the model is calculated, and the existence and stability of the equilibrium point of the model are analyzed. The theoretical analysis of the model is validated using numerical simulation software, and sensitivity analysis is performed on the parameters related to R0. In order to reduce the influence caused by uncertain information, the optimal control strategy of the model is proposed using the Hamiltonian function. It is found that the dissemination of uncertain information among netizens can be suppressed by strengthening the regulation of social platforms, improving netizens’ awareness of identifying the authenticity of information, and encouraging netizens to participate in the clarification of uncertain information. The results of this work can provide a theoretical basis for future research on the uncertain information dissemination mechanism triggered by major emergencies. In addition, the results can also provide methodological support for the relevant government departments to reduce the adverse effects caused by uncertain information in the future.

To study the propagation pattern of congestion risk in the traffic network and enhance risk control capabilities, a model has been developed. This model takes into account the probabilities of five threats (the risk occurrence probability; the risk of loss; the unpredictability of risk; the uncontrollability of risk; the transferability of risk) in the traffic network to define the risk entropy and determine the risk capacity, analyze the mechanism of congestion risk propagation, and explore the impact of risk resistance, the average degree of risk capacity at intersections, and the degree of correlation on congestion risk propagation. Further, a control method model for risk propagation is proposed. Numerical simulation results demonstrate that the risk resistance parameter θ can inhibit the propagation of congestion risk during traffic congestion. The highest efficiency in controlling risk propagation is achieved when θ reaches a threshold value θ*. Furthermore, the average degree of intersection risk capacity α shows a positive correlation with θ* and a negative correlation with control efficiency. However, the degree of association ω has a negative effect on risk propagation control, decreasing the degree of association between nodes aids in risk propagation control.

With the development of wireless sensor network (WSN) technologies, WSNs have been applied in many areas. In all WSN technologies, localization is a crucial problem. Traditional localization approaches in WSNs mainly focus on calculating the current location of sensor nodes or mobile objects. In this paper, we study the problem of future location prediction in WSNs. We assume the location histories of mobile objects as a rating matrix and then use a random walk based social recommender algorithm to predict the future locations of mobile objects. Experiments show that the proposed algorithm has better prediction accuracy and can solve the rating matrix sparsity problem more effectively than related works.

To study the propagation pattern of congestion risk in the traffic network and enhance risk control capabilities, a model has been developed. This model takes into account the probabilities of five threats (the risk occurrence probability; the risk of loss; the unpredictability of risk; the uncontrollability of risk; the transferability of risk) in the traffic network to define the risk entropy and determine the risk capacity, analyze the mechanism of congestion risk propagation, and explore the impact of risk resistance, the average degree of risk capacity at intersections, and the degree of correlation on congestion risk propagation. Further, a control method model for risk propagation is proposed. Numerical simulation results demonstrate that the risk resistance parameter θ can inhibit the propagation of congestion risk during traffic congestion. The highest efficiency in controlling risk propagation is achieved when θ reaches a threshold value θ*. Furthermore, the average degree of intersection risk capacity α shows a positive correlation with θ* and a negative correlation with control efficiency. However, the degree of association ω has a negative effect on risk propagation control, decreasing the degree of association between nodes aids in risk propagation control. [ABSTRACT FROM AUTHOR]

In this present study, the effects of polyacrylonitrile fibres and high content (50%) of Reclaimed Asphalt Pavement (RAP) on the mechanical properties of asphalt mixtures in binder and base layers were studied. This is the first time that the effect of high RAP content on the mechanical properties of fibre reinforced asphalt mixtures (FRAM) was evaluated, while the low temperature performance was not usually conducted in previous studies. Results indicated that the addition of polyacrylonitrile fibres worsens the moisture susceptibility resistance, but improves the rutting, stiffness properties, fatigue performances, and cumulative fatigue damage of the mixtures with and without RAP. No significant effects on the low temperature cracking resistance were found when fibre was used, while a remarkable improvement in the crack propagation resistance properties was observed. Therefore, the use of polyacrylonitrile fibres improves in general the mechanical responses of the mixture, especially in the stiffness response. [ABSTRACT FROM AUTHOR]

Improving the ecological governance capacity is necessary in order to achieve high-quality economic development. As the main part of the promotion of social and economic development, enterprises' inefficient investment (overinvestment and underinvestment) has impeded the development of the economy chronically and steadily. Therefore, improving the efficiency of enterprises' capital allocation has become a problem that needs to be studied and solved urgently. This article mainly studies the influence of ecological governance capability on the enterprise capital configuration efficiency and mechanism of action by using Shanghai and Shenzhen A-share listed companies data from 2011 to 2017. The study found that ecological governance capability can remarkably strengthen the corporate capital allocation efficiency, and it can do so by improving corporate social responsibility (CSR). This conclusion is supported after the replacement of robustness tests, for example, the enterprise capital configuration efficiency and the measurement indicators of CSR. The heterogeneity test indicates that the ecological governance can obviously forward the enterprise capital configuration efficiency in state-owned enterprises and less-polluting enterprises. These findings can provide some theoretical support to the mechanism of ecological governance capacity effect on corporate capital allocation efficiency from the aspect of corporate social responsibility. [ABSTRACT FROM AUTHOR]

Server consolidation is critical for energy efficiency of cloud-scale computing. In production environments like Aliyun, which is one of the largest public cloud platforms in the world, server consolidation has several challenges. First, the widespread use of local storage remarkably increases the migration cost (time). Second, the resource utilization of service instances varies over time, which may result in migration oscillation. Third, server consolidation must follow practical constraints. E.g., instances can only be migrated within a maintenance window, and both the resource utilization and the number of instances on a server are bounded. This paper designs and implements C4, a Cost-Efficient Consolidating Service for Aliyun’s Cloud-Scale Computing, which Aliyun uses to consolidate servers. We analyze user pattern, resource utilization, and migration cost in Aliyun, showing that traditional utilization-based consolidation approaches cannot meet the needs in production environments, especially for the local-storage-based computing. This motivates us to propose the migration cost model, by which to select servers with the minimum migration time to release. We use the Worst-Fit heuristic to migrate instances to balance the load. Evaluation shows that C4 achieves cost-efficient, load-balanced, and oscillation-free consolidating service. We describe experience with over one year of C4 production deployment, lessons learned, and areas for future work. [ABSTRACT FROM AUTHOR]

Natural catastrophe is a common phenomenon by which the living as well as nonliving entities belong to the environment faces the calamity. But the human beings can presume the disaster and take major steps to prevent it. There are many technologies available to foresee and prevent the abnormalities or variations in uncovered open regions. Wireless Sensor and Actuator Networks are exploited to monitor the physical and environmental conditions and is preferred due to its cost effectiveness, faster data transfer and accurate computation of required parameter for prediction and prevention. The WSANs are utilizing conditions such as temperature, sound, pressure, location to predict the Geological, Hydrological, and Meteorological disasters. The work mainly aims at studying about the various interpolation techniques and its advantages, disadvantages. The paper finally produces a comparative study of those interpolation techniques and finding an optimal interpolation method for wireless sensor networks. [ABSTRACT FROM AUTHOR]

Ferromanganese nodules and crusts contain relatively high concentration of rare earth elements (REE) and yttrium (REY), with a growing interest in exploitation as an alternative to land-based REY resources. On the basis of comprehensive geochemical approach, the abundance and distribution of REY in the ferromanganese nodules from the South China Sea are analyzed. The results indicate that the REY contents in ferromanganese deposits show a clear geographic regularity. Total REY contents range from 69.1×10-6 to 2 919.4×10-6, with an average value of 1 459.5×10-6. Especially, the enrichment rate of Ce content is high, accounting for almost 60% of the total REY. This REE enrichment is controlled mainly by the sorption of ferromanganese oxides and clay minerals in the nodules and crusts. Moreover, the total REY are higher in ferromanganese deposits of hydrogenous origin than of diagenetic origin. Finally, Light REE (LREE) and heavy REE (HREE) oxides of the ferromanganese deposits in the study area can be classified into four grades: non-enriched type, weakly enriched type, enriched type, and extremely enriched type. According to the classification criteria of rare earth resources, the Xisha and Zhongsha platform-central deep basin areas show a great potential for these rare earth metals. [ABSTRACT FROM AUTHOR]

The 2017 reviewers list includes the names of all reviewers from 15 December 2016 through 2 October 2017. All other reviewers who submit after this date will be included in our 2018 list. [ABSTRACT FROM AUTHOR]

Summary: Server consolidation is one of the critical techniques for energy‐efficiency in cloud data centers. As it is often assumed that cloud service instances (eg, Amazon EC2 instances) utilize the shared storage only. In recent years, however, cloud service providers have been providing local storage for cloud users, since local storage can offer a better performance with identified price. However, these cloud instances usually contain much more data than shared storage cloud instances. Thus, in such local storage based cloud center, the migration cost can be really high and is in dire need of an efficient resource pre‐allocation. If we can predict the resource demand in advance, the migration oscillation will be reduced to minify the migration cost. We have found that there are some related work about server consolidation based on forecasting. Unfortunately, their latest work did not consider the background of "local storage" as we mentioned above. At the same time, some research about local storage did not involve the prediction strategy, which plays a significant part in server consolidation. To address this issue, this paper proposes Losari, a consolidation method, which takes numeric forecasting and local storage architecture into consideration. Losari consolidates servers on the basis of the resource demand predicted value using a statistical learning method. We model the workload from real cloud production environment as a time series. Taking deep learning as a frame of reference, multiple deep belief networks integrated with ARIMA model was trained to study the feature of historical workload. The experimental results have showed that its average predicted error is only 10.7% in the short term, which is much lower than the most common model based on threshold (19.8%) on the same dataset. What is more, the results show that Losari not only simulates the true sequences in high accuracy but also scales the compute resource well, which demonstrated the validity of this integrated deep learning model. [ABSTRACT FROM AUTHOR]