11 research outputs found
Multiangle social network recommendation algorithms and similarity network evaluation
Multiangle social network recommendation algorithms (MSN) and a new assessmentmethod, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithmfromresource point (UBR), user-based algorithmfromtag point (UBT), resource-based algorithm fromtag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels
Overview of the TCV tokamak experimental programme
The tokamak a configuration variable (TCV) continues to leverage its unique shaping capabilities, flexible heating systems and modern control system to address critical issues in preparation for ITER and a fusion power plant. For the 2019-20 campaign its configurational flexibility has been enhanced with the installation of removable divertor gas baffles, its diagnostic capabilities with an extensive set of upgrades and its heating systems with new dual frequency gyrotrons. The gas baffles reduce coupling between the divertor and the main chamber and allow for detailed investigations on the role of fuelling in general and, together with upgraded boundary diagnostics, test divertor and edge models in particular. The increased heating capabilities broaden the operational regime to include T (e)/T (i) similar to 1 and have stimulated refocussing studies from L-mode to H-mode across a range of research topics. ITER baseline parameters were reached in type-I ELMy H-modes and alternative regimes with \u27small\u27 (or no) ELMs explored. Most prominently, negative triangularity was investigated in detail and confirmed as an attractive scenario with H-mode level core confinement but an L-mode edge. Emphasis was also placed on control, where an increased number of observers, actuators and control solutions became available and are now integrated into a generic control framework as will be needed in future devices. The quantity and quality of results of the 2019-20 TCV campaign are a testament to its successful integration within the European research effort alongside a vibrant domestic programme and international collaborations
Automatic Clustering of Social Tag using Community Detection
Automatically clustering social tags into semantic communities would greatly boost the ability of Web services search engines to retrieve the most relevant ones at the same time improve the accuracy of tag-based service recommendation. In this paper, we first investigate the different collaborative intention between co-occurring tags in Seekda as well as their dynamical aspects. Inspired by the relationships between co-occurring tags, we designed the social tag network. By analyzing the networks constructed, we show that the social tag network have scale free properties. In order to identify densely connected semantic communities, we then introduce a novel graph-based clustering algorithm for weighted networks based on the concept of edge betweenness with high enough intensity. Finally, experimental results on real world datasets show that our algorithm can effectively discovers the semantic communities and the resulting tag communities correspond to meaningful topic domains
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EFMLNet: Fusion Model Based on End-to-End Mutual Information Learning for Hybrid EEG-fNIRS Brain-Computer Interface Applications
Electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS), both portable and non-invasive, enhance brain-computer interface (BCI) performance by integrating their spatial and temporal benefits when combined together. However, the fusion of these two signals still faces challenges. To fully unitize the complementarity of EEG and fNIRS for improved performance in EEG-fNIRS BCI, we propose an EEG-fNIRS fusion network based on end-to-end mutual information learning, named EFMLNet. In the model, EEG and fNIRS data are fed into their respective feature extractors for the extraction of temporal and spatial information. Furthermore, their complementary information is fused by two parallel mutual learning modules. We conducted classification experiments on a publicly available BCI dataset based on motor imagery (MI) task and achieved a cross-subject classification accuracy of 71.52%. This result surpasses the performance of most existing fusion methods and demonstrates the potential for real-time hybrid BCI systems
Performance of Rod-Shaped Ce MetalâOrganic Frameworks for Defluoridation
The performance of a Ce(III)-4,4âČ,4âł-((1,3,5-triazine-2,4,6-triyl) tris (azanediyl)) tribenzoic acidâorganic framework (Ce-H3TATAB-MOFs) for capturing excess fluoride in aqueous solutions and its subsequent defluoridation was investigated in depth. The optimal sorption capacity was obtained with a metal/organic ligand molar ratio of 1:1. The morphological characteristics, crystalline shape, functional groups, and pore structure of the material were analyzed via SEM, XRD, FTIR, XPS, and N2 adsorptionâdesorption experiments, and the thermodynamics, kinetics, and adsorption mechanism were elucidated. The influence of pH and co-existing ions for defluoridation performance were also sought. The results show that Ce-H3TATAB-MOFs is a mesoporous material with good crystallinity, and that quasi-second kinetic and Langmuir models can describe the sorption kinetics and thermodynamics well, demonstrating that the entire sorption process is a monolayer-governed chemisorption. The Langmuir maximum sorption capacity was 129.7 mg gâ1 at 318 K (pH = 4). The adsorption mechanism involves ligand exchange, electrostatic interaction, and surface complexation. The best removal effect was reached at pH 4, and a removal effectiveness of 76.57% was obtained under strongly alkaline conditions (pH 10), indicating that the adsorbent has a wide range of applications. Ionic interference experiments showed that the presence of PO43â and H2PO4â in water have an inhibitory effect on defluoridation, whereas SO42â, Clâ, CO32â, and NO3â are conducive to the adsorption of fluoride due to the ionic effect
Synthesis of a Novel P/N/S-Containing Flame Retardant and Its Application in Epoxy Resin: Thermal Property, Flame Retardance, and Pyrolysis Behavior
The
combination of DOPO and 2-aminobenzothiazole (ABZ) was designed
to develop P/N/S-containing flame retardant DOPO-ABZ, and its chemical
structure was confirmed by HRMS, FTIR, and <sup>1</sup>H and <sup>31</sup>P NMR. The reduced thermal stability of EP/DOPO-ABZ formulations
was found through DSC and TGA, as compared to that of EP. Fire properties
were evaluated by LOI, UL-94, and cone calorimeter tests, respectively.
The results indicated that DOPO-ABZ imparted flame retardance to EP,
and that EP/7.5 wt % DOPO-ABZ passed the V-0 rating, and acquired
a LOI value of 33.5%; moreover, when the loading of DOPO-ABZ increased
to 10 wt %, it could further suppress the heat release and smoke release
of the curved epoxy resin. Finally, the flame-retardant mechanism
was studied by TG-FTIR and py-GC/MS, disclosing that DOPO-ABZ exerted
predominant gaseous-phase activity of fire inhibition via generating
phosphorus-containing free radicals and nitrogen/sulfur-containing
volatiles