Suchergebnisse

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above. ; When technologies of software-defined networks (SDNs) provide a chance to improve the quality of service (QoS) of publish/subscribe middlewares, new chances are also arising for adversaries to attack the networks and the middlewares. We here propose a cross-layer access control solution to protect the publish/subscribe middleware over SDNs. Applications over a publish/subscribe middleware interact by an indirect, anonymous and multicast event communication paradigm, where we hope that the applications, the middleware, and the underlying network collaborate to realize the access control of reading/writing events. The key issue is how to use the flow matching capability of SDN switches to efficiently and securely enforce complex authorization policies that include multiple conjunction and disjunction structures. It is required to resist against the collusion attacks of SDN controllers and subscribers when the middleware/network is partially delegated to enforce the authorization policies of publishers. In our cross-layer solution, a policy representation method is presented to encode authorization policies into flow entries with high data compression and security, and a two-party computation method is presented to carry out secret sharing for defeating malicious SDN controllers and subscribers. Finally, our solution is evaluated to show its effectiveness. ; This work is supported by the National Natural Science Foundation of China (no. 61372115), the National Key Research and Development Program of China (No. 2018YFB1003800), and EU H2020 DOMINOES Project (No. 771066). ; Peer-reviewed ; Post-print

Abstract. We applied Gravity Recovery and Climate Experiment (GRACE) Tellus products in combination with Global Land Data Assimilation System (GLDAS) simulations and data from reports, to analyze variations in terrestrial water storage (TWS) in China as a whole and eight of its basins from 2003 to 2013. Amplitudes of TWS were well restored after scaling, and showed good correlations with those estimated from models at the basin scale. TWS generally followed variations in annual precipitation; it decreased linearly in the Huai River basin (−0.56 cm yr−1) and increased with fluctuations in the Changjiang River basin (0.35 cm yr−1), Zhujiang basin (0.55 cm yr−1) and southeast rivers basin (0.70 cm yr−1). In the Hai River basin and Yellow River basin, groundwater exploitation may have altered TWS's response to climate, and TWS kept decreasing until 2012. Changes in soil moisture storage contributed over 50 % of variance in TWS in most basins. Precipitation and runoff showed a large impact on TWS, with more explained TWS in the south than in the north. North China and southwest rivers region exhibited long-term TWS depletions. TWS has increased significantly over recent decades in the middle and lower reaches of Changjiang River, southeastern coastal areas, as well as the Hoh Xil, and the headstream region of the Yellow River in the Tibetan Plateau. The findings in this study could be helpful to climate change impact research and disaster mitigation planning.

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above. ; Person re-identification aims at matching individuals across multiple camera views under surveillance systems. The major challenges lie in the lack of spatial and temporal cues, which makes it difficult to cope with large variations of lighting conditions, viewing angles, body poses and occlusions. How to extract multimodal features including facial features, physical features, behavioral features, color features, etc is still a fundamental problem in person re-identification. In this paper, we propose a novel Convolutional Neural Network, called Asymmetric Filtering-based Dense Convolutional Neural Network (AF D-CNN) to learn powerful features, which can extract different levels' features and take advantage of identity information. Moreover, instead of using typical metric learning methods, we obtain the ranking lists by merging Joint Bayesian and re-ranking techniques which do not need dimensionality reduction. Finally, extensive experiments show that our proposed architecture performs well on four popular benchmark datasets (CUHK01, CUHK03, Market-1501, DukeMTMC-reID). ; This work is supported by the National Natural Science Foundation of China (NSFC) Grants U1706218, 61602229, 41606198, 61501417 and 41706010, Natural Science Foundation of Shandong Provincial ZR2016FM13, ZR2016FB02. H. Zhou was supported in part by the European Union's Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement No 720325 FoodSmartphone, the UK EPSRC under Grant EP/N011074/1 and the Royal Society-Newton Advanced Fellowship under Grant NA160342. ; Peer-reviewed ; Post-print

Abstract. A tsunami generated by large-volume landslide can propagate across the ocean and flood communities around the basin. The evolution of landslide-generated tsunamis is affected by the effects of frequency dispersion and involves processes of different temporal and spacial scales. In this paper, we develop a numerical approach employing the weakly nonlinear and fully nonlinear Boussinesq-type theories and nested computational grids. The propagation in a large domain is simulated with the weakly nonlinear model in a geographical reference frame. The nearshore wave evolution and runup are computed with the fully nonlinear model. Nested grids are employed to zoom simulations from larger to smaller domains at successively increasing resolutions. The models and the nesting scheme are validated for theoretical analysis, laboratory experiments and a historical tsunami event. By applying this approach, we also investigate the potential tsunami impact on the US east coast due to the possible landslide on La Palma Island. The scenario employed in this study represents an event of extremely low probability.

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above. ; Detailed knowledge regarding sensor based technologies for the detection of food contamination often remains concealed within scientific journals or divided between numerous commercial kits which prevents optimal connectivity between companies and end-users. To overcome this barrier The End user Sensor Tree (TEST) has been developed. TEST is a comprehensive, interactive platform including over 900 sensor based methods, retrieved from the scientific literature and commercial market, for aquatic-toxins, mycotoxins, pesticides and microorganism detection. Key analytical parameters are recorded in excel files while a novel classification system is used which provides, tailor-made, experts' feedback using an online decision tree and database introduced here. Additionally, a critical comparison of reviewed sensors is presented alongside a global perspective on research pioneers and commercially available products. The lack of commercial uptake of the academically popular electrochemical and nanomaterial based sensors, as well as multiplexing platforms became very apparent and reasons for this anomaly are discussed. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 720325. The authors would also like to acknowledge BioMensio Limited, Finland for the sponsorship of the MPhil for Philana Nolan. ; Peer-reviewed ; Post-print

The nodal-line semimetals have attracted immense interest due to the unique electronic structures such as the linear dispersion and the vanishing density of states as the Fermi energy approaching the nodes. Here, we report temperature-dependent transport and scanning tunneling microscopy (spectroscopy) [STM(S)] measurements on nodal-line semimetal ZrSiSe. Our experimental results and theoretical analyses consistently demonstrate that the temperature induces Lifshitz transitions at 80 and 106 K in ZrSiSe, which results in the transport anomalies at the same temperatures. More strikingly, we observe a V-shaped dip structure around Fermi energy from the STS spectrum at low temperature, which can be attributed to co-effect of the spin-orbit coupling and excitonic instability. Our observations indicate the correlation interaction may play an important role in ZrSiSe, which owns the quasi-two-dimensional electronic structures. © 2020 American Physical Society. ; This work was supported by the National Key R&D Program (Grants No. 2016YFA0300404, No. 2016YFA0401803, No. 2017YFA0303201, No. 2015CB921103, and No. 2019YFA0308602), the National Nature Science Foundation of China (Grants No. 11674326, No. 11674331, No. 11774351, No. 11874357, No. 11625415, No. 11374260, No. U1432139, No. U1832141, and No. U1932217), Key Research Program of Frontier Sciences, CAS (Grant No. QYZDB-SSW-SLH015), the "Strategic Priority Research Program (B)" of the Chinese Academy of Sciences, Grant No. XDB33030100, the "100 Talents Project" of the Chinese Academy of Sciences, CASHIPS Director's Fund (Grant No. BJPY2019B03) and Science Challenge Project (Grant No. TZ2016001). A portion of this work was supported by the High Magnetic Field Laboratory of Anhui Province, the Fundamental Research Funds for the Central Universities in China, the European Research Council under the European Union's Seventh Framework Program (FP/2007-2013) through ERC Grant No. 338957 and by NWO via Spinoza Prize, and the Cluster of Excellence "The Hamburg Centre for Ultrafast Imaging (CUI)" of the German Science Foundation (DFG).

© 2015 Macmillan Publishers Limited. Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific. ; We thank Ron Ronimus, Paul Newton, and Christina Moon for reading and commenting on the manuscript. We thank all who provided assistance that allowed Global Rumen Census collaborators to supply samples and metadata (Supplemental Text 1). AgResearch was funded by the New Zealand Government as part of its support for the Global Research Alliance on Agricultural Greenhouse Gases. The following funding sources allowed Global Rumen Census collaborators to supply samples and metadata, listed with the primary contact(s) for each funding source: Agencia Nacional de Investigación e Innovación, Martín Fraga; Alberta Livestock and Meat Agency, Canada, Tim A. McAllister; Area de Ciencia y Técnica, Universidad Juan A Maza (Resolución Proy. N° 508/2012), Diego Javier Grilli; Canada British Columbia Ranching Task Force Funding Initiative, John Church; CNPq, Hilário Cuquetto Mantovani, Luiz Gustavo Ribeiro Pereira; FAPEMIG, Hilário Cuquetto Mantovani; FAPEMIG, PECUS RumenGases, Luiz Gustavo Ribeiro Pereira; Cooperative Research Program for Agriculture Science & Technology Development (project number PJ010906), Rural Development Administration, Republic of Korea, Sang-Suk Lee; Dutch Dairy Board & Product Board Animal Feed, André Bannink, Kasper Dieho, Jan Dijkstra; Ferdowsi University of Mashhad, Vahideh Heidarian Miri; Finnish Ministry of Agriculture and Forestry, Ilma Tapio; Instituto Nacional de Tecnología Agropecuaria, Argentina (Project PNBIO1431044), Silvio Cravero, María Cerón Cucchi; Irish Department of Agriculture, Fisheries and Food, Alexandre B. De Menezes; Meat & Livestock Australia; and Department of Agriculture, Fisheries & Forestry (Australian Government), Chris McSweeney; Ministerio de Agricultura y desarrollo sostenible (Colombia), Olga Lucía Mayorga; Montana Agricultural Experiment Station project (MONB00113), Carl Yeoman; Multistate project W-3177 Enhancing the competitiveness of US beef (MONB00195), Carl Yeoman; NSW Stud Merino Breeders' Association, Alexandre Vieira Chaves; Queensland Enteric Methane Hub, Diane Ouwerkerk; RuminOmics, Jan Kopecny, Ilma Tapio; Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government and the Technology Strategy Board, UK, R. John Wallace; Science Foundation Ireland (09/RFP/GEN2447), Sinead Waters; Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación, Mario A. Cobos-Peralta; Slovenian Research Agency (project number J1-6732 and P4-0097), Blaz Stres; Strategic Priority Research Program, Climate Change: Carbon Budget and Relevant Issues (Grant No.XDA05020700), ZhiLiang Tan; The European Research Commission Starting Grant Fellowship (336355—MicroDE), Phil B. Pope; The Independent Danish Research Council (project number 4002-00036), Torsten Nygaard Kristensen; and The Independent Danish Research Council (Technology and Production, project number 11-105913), Jan Lassen. These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Peer Reviewed