Revisiting the chevrel phase: Impact of dispersion corrections on the properties of Mo6S8 for cathode applications

While the Mo6S8 chevrel phase is frequently used as cathode material in Mg--ion batteries, theoretical studies on this material are comparatively scarce. The particular structure of the Mo6S8 phase, with rather loosely connected cluster entities, points to the important role of dispersion forces in this material. However, so far this aspect has been completely neglected in the discussion of Mo6S8 as cathode material for mono- and multivalent-ion batteries. In this work we therefore have studied the impact of dispersion forces on stability and kinetics of Mo6S8 intercalation compounds. For this purpose, a series of charge carriers (Li, Na, K, Mg, Ca, Zn, Al) has been investigated. Interestingly, dispersion forces are observed to only slightly affect the lattice spacing of the chevrel phase, nevertheless having a significant impact on insertion voltage and in particular on the charge carrier mobility in the material. Moreover, upon varying the charge carriers in the chevrel phase, their diffusion barriers are observed to scale linearly with the ion size, almost independent of the charge of the considered ions. This indicates a rather unique and geometry dominated diffusion mechanism in the chevrel phase. The consequences of these findings for the ion mobility in the chevrel phase will be carefully discussed.

New Betweenness Centrality Node Attack Strategies for Real-World Complex Weighted Networks

In this work, we introduce a new node attack strategy removing nodes with the highest conditional weighted betweenness centrality (CondWBet), which combines the weighted structure of the network and the node’s conditional betweenness. We compare its efficacy with well-known attack strategies from literature over five real-world complex weighted networks. We use the network weighted efficiency (WEFF) like a measure encompassing the weighted structure of the network, in addition to the commonly used binary-topological measure, i.e., the largest connected cluster (LCC). We find that if the measure is WEFF, the CondWBet strategy is the best to decrease WEFF in 3 out of 5 cases. Further, CondWBet is the most effective strategy to reduce WEFF at the beginning of the removal process, whereas the Strength that removes nodes with the highest sum of the link weights first shows the highest efficacy in the final phase of the removal process when the network is broken into many small clusters. These last outcomes would suggest that a better attacking in weighted networks strategy could be a combination of the CondWBet and Strength strategies.

A Fully Connected Cluster with Minimal Transmission Power for IoT Using Electrostatic Discharge Algorithm

In the emerging age of the Internet of Things (IoT), energy-efficient and reliable connection among sensor nodes gain prime importance. Wireless engineers encounter a trade-off between sensors energy requirement and their reliable full connectivity. Consequently, the need to find the optimal solution draws the attention of many researchers. In this paper, the Electrostatic Discharge Algorithm (ESDA) is proposed, implemented, and applied to minimize energy needs of a sensor node while ensuring the fully-connectedness of each node. The obtained results show that the proposed method achieves better results than those found in the literature using the particle swarm optimization method in terms of energy savings and reliable connectivity.

Local hypergraph clustering using capacity releasing diffusion

Local graph clustering is an important machine learning task that aims to find a well-connected cluster near a set of seed nodes. Recent results have revealed that incorporating higher order information significantly enhances the results of graph clustering techniques. The majority of existing research in this area focuses on spectral graph theory-based techniques. However, an alternative perspective on local graph clustering arises from using max-flow and min-cut on the objectives, which offer distinctly different guarantees. For instance, a new method called capacity releasing diffusion (CRD) was recently proposed and shown to preserve local structure around the seeds better than spectral methods. The method was also the first local clustering technique that is not subject to the quadratic Cheeger inequality by assuming a good cluster near the seed nodes. In this paper, we propose a local hypergraph clustering technique called hypergraph CRD (HG-CRD) by extending the CRD process to cluster based on higher order patterns, encoded as hyperedges of a hypergraph. Moreover, we theoretically show that HG-CRD gives results about a quantity called motif conductance, rather than a biased version used in previous experiments. Experimental results on synthetic datasets and real world graphs show that HG-CRD enhances the clustering quality.

Identifying the potential for cross-fishery spillovers: a network analysis of Alaskan permitting patterns

Many fishers own a portfolio of permits across multiple fisheries, creating an opportunity for fishing effort to adjust across fisheries and enabling impacts from a policy change in one fishery to spill over into other fisheries. In regions with a large and diverse number of permits and fisheries, joint-permitting can result in a complex system, making it difficult to understand the potential for cross-fishery substitution. In this study, we construct a network representation of permit ownership to characterize interconnectedness among Alaska commercial fisheries due to cross-fishery permitting. The Alaska fisheries network is highly connected, suggesting that most fisheries are vulnerable to cross-fishery spillovers from network shocks, such as changes to policies or fish stocks. We find that fisheries with similar geographic proximity are more likely to be a part of a highly connected cluster of susceptible fisheries. We use a case study to show that preexisting network statistics can be useful for identifying the potential scope of policy-induced spillovers. Our results demonstrate that network analysis can improve our understanding of the potential for policy-induced cross-fishery spillovers.

High Availability Performance on OpenDayLight SDN Controller Platform (OSCP) Clustering and OpenDayLight with Heartbeat-Distributed Replicated Block Device (DRBD)

In this day people are asking for a reliable network when technology at its limit. Software-Defined Network (SDN) is an answer to that problem of network development where all the control over the network becomes centralized. However, all services controlled by a centralized controller have a big disadvantage if the controller dies. The High Availability (HA) is the solution. HA controller is divided into master and slave, when master controller is down then slave controller will respond to replace the function of master controller. In this research the system will be made by using two methods namely OpenDayLight SDN Controller Platform (OSCP) clustering and Heartbeat-DRBD (Distributed Replicated Block Device). OSCP clustering is a feature on OpenDayLight controller that is ready to be used and only need to be configured, with OSCP the main and backup controller clustering will be on connected cluster. Heartbeat-DRBD is an application commonly used to create High Availability systems on a server, but in this study will be used for the controller, Heartbeat will monitoring the main controller and if indicated to be down will move the resources to the backup controller with the DRBD application. From the simulation result shows that OSCP Clustering Failover and Failback average Time is 17 seconds while Heartbeat-DRBD is 23-45 seconds depends on how many switch and host are.While QoS parameters on both method have simillar value.it can be concluded that the High Availability system with OSCP Clustering method is more stable and good rather than Heartbeat-DRBD method to apply in a network.

Graph analysis of cell clusters forming vascular networks

This manuscript describes the experimental observation of vasculogenesis in chick embryos by means of network analysis. The formation of the vascular network was observed in the area opaca of embryos from 40 to 55 h of development. In the area opaca endothelial cell clusters self-organize as a primitive and approximately regular network of capillaries. The process was observed by bright-field microscopy in control embryos and in embryos treated with Bevacizumab (Avastin ® ), an antibody that inhibits the signalling of the vascular endothelial growth factor (VEGF). The sequence of images of the vascular growth were thresholded, and used to quantify the forming network in control and Avastin-treated embryos. This characterization is made by measuring vessels density, number of cell clusters and the largest cluster density. From the original images, the topology of the vascular network was extracted and characterized by means of the usual network metrics such as: the degree distribution, average clustering coefficient, average short path length and assortativity, among others. This analysis allows to monitor how the largest connected cluster of the vascular network evolves in time and provides with quantitative evidence of the disruptive effects that Avastin has on the tree structure of vascular networks.