Origin of Low Thermal Conductivity in Nuclear Fuels

Using a novel many-body approach, we report lattice dynamical properties of UO2 and PuO2 and uncover various contributions to their thermal conductivities. Via calculated Grueneisen constants, we show that only longitudinal acoustic modes having large phonon group velocities are efficient heat carriers. Despite the fact that some optical modes also show their velocities which are extremely large, they do not participate in the heat transfer due to their unusual anharmonicity. Ways to improve thermal conductivity in these materials are discussed.Comment: 4 pages, 3 figures, 1 tabl

U(1)-decoupling, KK and BCJ relations in N=4\mathcal{N}=4 SYM

We proved the color reflection relation, U(1)-decoupling, Kleiss-Kuijf and Bern-Carrasco-Johansson relation for color-ordered $\mathcal{N}=4$ Super Yang-Mills theory using $\mathcal{N}=4$ SYM version BCFW recursion relation, which depends only on the general properties of super-amplitudes. This verified the conjectured matter fields BCJ relation. We also show that color reflection relation and U(1)-decoupling relation are special cases of KK relation, if we consider the KK relation as a general relation, then the former two relations come out naturally as the special cases.Comment: 17 page

Potential Models and Lattice Gauge Current-Current Correlators

We compare current-current correlators in lattice gauge calculations with correlators in different potential models, for a pseudoscalar charmonium in the quark-gluon plasma. An important ingredient in the evaluation of the current-current correlator in the potential model is the basic principle that out of the set of continuum states, only resonance states and Gamow states with lifetimes of sufficient magnitudes can propagate as composite objects and can contribute to the current-current correlator. When the contributions from the bound states and continuum states are properly treated, the potential model current-current correlators obtained with the potential proposed in Ref. [11] are consistent with the lattice gauge correlators. The proposed potential model thus gains support to be a useful tool to complement lattice gauge calculations for the study of $Q\bar Q$ states at high temperatures.Comment: 18 pages, 4 figures, to be published in Physcial Review

Classifying Crises-Information Relevancy with Semantics

Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis

Polarization Induced Switching Effect in Graphene Nanoribbon Edge-Defect Junction

With nonequilibrium Green's function approach combined with density functional theory, we perform an ab initio calculation to investigate transport properties of graphene nanoribbon junctions self-consistently. Tight-binding approximation is applied to model the zigzag graphene nanoribbon (ZGNR) electrodes, and its validity is confirmed by comparison with GAUSSIAN03 PBC calculation of the same system. The origin of abnormal jump points usually appearing in the transmission spectrum is explained with the detailed tight-binding ZGNR band structure. Transport property of an edge defect ZGNR junction is investigated, and the tunable tunneling current can be sensitively controlled by transverse electric fields.Comment: 18 pages, 8 figure

Spectral functions of the Falicov-Kimball model with electronic ferroelectricity

We calculate the angular resolved photoemission spectrum of the Falicov-Kimball model with electronic ferroelectricity where $d$- and $f$-electrons have different hoppings. In mix-valence regimes, the presence of strong scattering processes between $d$-$f$ excitons and a hole, created by emission of an electron, leads to the formation of pseudospin polarons and novel electronic structures with bandwidth scaling with that of $d$-$f$ excitons. Especially, in the two-dimensional case, we find that flat regions exist near the bottom of the quasiparticle band in a wide range of the $d$- and $f$-level energy difference.Comment: 5 pages, 5 figure

Looking back to see the future: building nuclear power plants in Europe

The so-called ‘nuclear renaissance’ in Europe is promulgated by the execution of two large engineering projects involving the construction of two European Pressurized Reactors (EPRs) in Flamanville, France and Olkiluoto in Finland. As both projects have faced budget overruns and delays, this paper analyses their governance and history to derive lessons useful for the construction of future projects. Analysis indicates that the reasons for these poor outcomes are: overoptimistic estimations, first-of-a-kind (FOAK) issues and undervaluation of regulation requirements. These pitfalls have the potential to impact on many other engineering construction projects and highlight fruitful areas of further research into project performance

RsyGAN: Generative Adversarial Network for Recommender Systems

© 2019 IEEE. Many recommender systems rely on the information of user-item interactions to generate recommendations. In real applications, the interaction matrix is usually very sparse, as a result, the model cannot be optimised stably with different initial parameters and the recommendation performance is unsatisfactory. Many works attempted to solve this problem, however, the parameters in their models may not be trained effectively due to the sparse nature of the dataset which results in a lower quality local optimum. In this paper, we propose a generative network for making user recommendations and a discriminative network to guide the training process. An adversarial training strategy is also applied to train the model. Under the guidance of a discriminative network, the generative network converges to an optimal solution and achieves better recommendation performance on a sparse dataset. We also show that the proposed method significantly improves the precision of the recommendation performance on several datasets