A multilayer network diffusion-based model for reviewer recommendation

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

Pattern dynamics of network-organized system with cross-diffusion

Cross-diffusion is a ubiquitous phenomenon in complex networks, but it is often neglected in the study of reaction–diffusion networks. In fact, network connections are often random. In this paper, we investigate pattern dynamics of random networks with cross-diffusion by using the method of network analysis and obtain a condition under which the network loses stability and Turing bifurcation occurs. In addition, we also derive the amplitude equation for the network and prove the stability of the amplitude equation which is also an effective tool to investigate pattern dynamics of the random network with cross diffusion. In the meantime, the pattern formation consistently matches the stability of the system and the amplitude equation is verified by simulations. A novel approach to the investigation of specific real systems was presented in this paper. Finally, the example and simulation used in this paper validate our theoretical results.

Study on the Diffusion Performance of Standard E-Government Information Systems

Standard e-government information system(SEIS) including mobile-government applications are playing more and more important roles in the establishing of national e-government framework. It can be beneficial not only for avoiding redundant e-government IS development but also for improving collaboration among government agencies. Two research questions were explored: what are the factors influencing the performance of SEIS? Will mandatory SEIS create a better performance than non-mandatory SEIS? Specifically, the use of five categories of IS aspects--information system quality, online service quality, offline service quality, diffusion modes and standard network size—is proposed to understand the performance of SEIS through applying both survey study and simulation study. The results show that information system quality and online service quality of SEIS have strong effects on users' expectation and users' satisfaction, which thereafter promotes the performance of SEIS. Government agencies' offline service quality shows a significant effect on users' satisfaction while not on users' expectation. Furthermore, the diffusion speed of SEIS in non-mandatory and mandatory modes and the standard network size also have great influence on the utility of SEIS.

Empirical Study of ＂Double Threshold＂ Modified Model on New Product Diffusion

The innovations actually diffuse among social network nowadays.Individual heterogeneity,interactions between individuals and network topology influence a lot.We established a "double threshold" modified model and took the number of neighbors,neighbors' adoption and the cost-benefit parameters as crucial influencing factors.The diffusion of DaLingTong(CDMA450)products in MeiShan city of SiChuan province during 2004 to 2007 has been used to verity the model on Matlab.The validation results fit the actual diffusion pattern of DaLingTong(CDMA450) products very well.The results indicate that there exists a "tipping point(threshold)" in the process of innovation diffusion.If the initial adoption quantity is larger than the tipping point,then the product will spread to a large portion of people,otherwise is will collapse to zero.The model can effectively predict the diffusion of new products,and can influence the diffusion process by changing the value of the parameters.

IoT Enabled Microgrid Framework Using a Novel Dispersal Diffusion Artificial Neural Network Controller for PV Systems and Wind Energy to Minimize Electrical Faults

A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production.Today,plenty of work is being focussed on Doubly Fed Induction Generators(DFIG)utilized in wind energy systems.DFIG is found to be the best option in the Wind Energy Conversion Systems(WECS)to mitigate the issues caused by power converters.In this work,a new Artificial Neural Network(ANN)is proposed with the Diffusion and Dispersal strategy that works on Maximum Power Point Tracking(MPPT)along with Wind Energy Conversion System(WECS)to minimize electrical faults.The controller focus was not just to increase performance but also to reduce damage owing to any phase to phase fault or Phase to phase to ground fault.To ensure optimal MPPT for the proposed WECS,ANN achieves the optimal PI controller parameters for the indirect control of active and reactive power of DFIG.The optimal allocation and size of the DGs within the distributed system and for MPPT control are obtained using a population of agents.The generated solutions are evaluated and on being successful,the agents test their hypothesis again to create a positive feedback mechanism.Simulations are carried out,and the proposed IoT framework efficiency indicates performance improvement and faster recovery against faults by 9 percent for phase to ground fault and by 7.35 percent for phase to phase fault.

Epidemic-Like Proximity-Based Traffic Offloading

Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.

Planning for selective amygdalohippocampectomy involving less neuronal fiber damage based on brain connectivity using tractography

Temporal lobe resection is an important treatment option for epilepsy that involves removal of potentially essential brain regions. Selective amygdalohippocampectomy is a widely performed temporal lobe surgery. We suggest starting the incision for selective amygdalohippocampectomy at the inferior temporal gyrus based on diffusion magnetic resonance imaging（MRI） tractography. Diffusion MRI data from 20 normal participants were obtained from Parkinson＇s Progression Markers Initiative（PPMI） database（www.ppmi-info.org）. A tractography algorithm was applied to extract neuronal fiber information for the temporal lobe, hippocampus, and amygdala. Fiber information was analyzed in terms of the number of fibers and betweenness centrality. Distances between starting incisions and surgical target regions were also considered to explore the length of the surgical path. Middle temporal and superior temporal gyrus regions have higher connectivity values than the inferior temporal gyrus and thus are not good candidates for starting the incision. The distances between inferior temporal gyrus and surgical target regions were shorter than those between middle temporal gyrus and target regions. Thus, the inferior temporal gyrus is a good candidate for starting the incision. Starting the incision from the inferior temporal gyrus would spare the important（in terms of betweenness centrality values） middle region and shorten the distance to the target regions of the hippocampus and amygdala.

Rapid proton diffusion in hydroxyl functionalized imidazolium ionic liquids

There is considerable interest in using ionic liquids(ILs) as protic electrolytes. However, the reported proton transfer rate in ILs is quite slow. In this study, we report functionalizing imidazolium ILs with alcohol hydroxyls, aiming at constructing hydrogen bonding networks in the electrolyte, can stimulate fast proton hopping transfer. For demonstration, the diffusion of proton and Cl. in 1-(3-hydroxypropyl)-3-methylimidazolium tetrafluoroboride(C_3OHmimBF_4) were studied using cyclic voltammetry and potentiostatic method at 30 °C. The diffusion coefficient of proton is about one order of magnitude higher than that of Cl. in the same electrolyte, and about 5 times that of proton in the non-hydydroxyl 1-(butyl)-3-methylimidazolium tetrafluoroboride(BmimBF_4) when normalized to the diffusion coefficients of Cl. in respective ILs. In the meantime, 1H NMR spectra revealed a strong hydrogen bonding interaction between proton and C_3OHmimBF_4 which is absent between proton and BmimBF_4, thus the significantly higher diffusion coefficient of proton in C_3OHmimBF_4 may suggest the formation of effective hydrogen bonding networks, enabling rapid proton hopping via the Grotthuss mechanism.

Identifying the Key Reference of a Scientific Publication

The most fundamental way to measure the impact of a scientific publication is using the number of citations it received.Though citation count and its variants are widely adopted,they have been pointed out to be poor proxies for a paper's quality because a citation might result from different reasons.It is thus crucial to quantify the true relevance of the cited papers to the citing paper.There are already some eforts in the literature devoted to addressing this isue,yet a well-accepted method is still lacking,possibly due to the absence of standard ground truth data for comparing different methods.In this paper,we propose a simple method using a local diffusion process on citation networks for identifying the key references for each scientific publication.The effectiveness and of the method are validated in a subset of the American Physical Society data in which the key references are mentioned in the abstract of papers.We further define an effective citation metric for quantifying the actual impact of each paper and its evolution.The effective citation metric additionally reveals the citation preference of research at journal and country levels.