Carleton University

About: Carleton University is a education organization based out in Ottawa, Ontario, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 15852 authors who have published 39650 publications receiving 1106610 citations.

Low-energy-threshold analysis of the Phase I and Phase II data sets of the Sudbury Neutrino Observatory

Abstract: Results are reported from a joint analysis of Phase I and Phase II data from the Sudbury Neutrino Observatory. The effective electron kinetic energy threshold used is Teff=3.5 MeV, the lowest analysis threshold yet achieved with water Cherenkov detector data. In units of 106 cm-2 s-1, the total flux of active-flavor neutrinos from 8B decay in the Sun measured using the neutral current (NC) reaction of neutrinos on deuterons, with no constraint on the 8B neutrino energy spectrum, is found to be FNC=5.140-0.158+0.160(stat)-0.117+0.132(syst). These uncertainties are more than a factor of 2 smaller than previously published results. Also presented are the spectra of recoil electrons from the charged current reaction of neutrinos on deuterons and the elastic scattering of electrons. A fit to the Sudbury Neutrino Observatory data in which the free parameters directly describe the total 8B neutrino flux and the energy-dependent e survival probability provides a measure of the total 8B neutrino flux F8B=5.046-0.152+0.159(stat)-0.123+0.107(syst). Combining these new results with results of all other solar experiments and the KamLAND reactor experiment yields best-fit values of the mixing parameters of 12=34.06-0.84+1.16 degrees and m212=7.59-0.21+0.2010-5 eV2. The global value of 8B is extracted to a precision of -2.95+2.38%. In a three-flavor analysis the best fit value of sin213 is 2.00-1.63+2.0910-2. This implies an upper bound of sin213<0.057 (95% C.L.).

Cooperative Computation Offloading and Resource Allocation for Blockchain-Enabled Mobile-Edge Computing: A Deep Reinforcement Learning Approach

Abstract: Mobile-edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues; 2) cooperative computation offloading; and 3) dynamic optimization. To address the security and privacy issues, we employ the blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this article, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multiobjective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size, and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an asynchronous advantage actor–critic-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. The simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

Advancing the surgical implantation of electronic tags in fish: a gap analysis and research agenda based on a review of trends in intracoelomic tagging effects studies

Abstract: Early approaches to surgical implantation of electronic tags in fish were often through trial and error, however, in recent years there has been an interest in using scientific research to identify techniques and procedures that improve the outcome of surgical procedures and determine the effects of tagging on individuals. Here we summarize the trends in 108 peer-reviewed electronic tagging effect studies focused on intracoleomic implantation to determine opportunities for future research. To date, almost all of the studies have been conducted in freshwater, typically in laboratory environments, and have focused on biotelemetry devices. The majority of studies have focused on salmonids, cyprinids, ictalurids and centrarchids, with a regional bias towards North America, Europe and Australia. Most studies have focused on determining whether there is a negative effect of tagging relative to control fish, with proportionally fewer that have contrasted different aspects of the surgical procedure (e.g., methods of sterilization, incision location, wound closure material) that could advance the discipline. Many of these studies included routine endpoints such as mortality, growth, healing and tag retention, with fewer addressing sublethal measures such as swimming ability, predator avoidance, physiological costs, or fitness. Continued research is needed to further elevate the practice of electronic tag implantation in fish in order to ensure that the data generated are relevant to untagged conspecifics (i.e., no long-term behavioural or physiological consequences) and the surgical procedure does not impair the health and welfare status of the tagged fish. To that end, we advocate for (1) rigorous controlled manipulations based on statistical designs that have adequate power, account for inter-individual variation, and include controls and shams, (2) studies that transcend the laboratory and the field with more studies in marine waters, (3) incorporation of knowledge and techniques emerging from the medical and veterinary disciplines, (4) addressing all components of the surgical event, (5) comparative studies that evaluate the same surgical techniques on multiple species and in different environments, (6) consideration of how biotic factors (e.g., sex, age, size) influence tagging outcomes, and (7) studies that cover a range of endpoints over ecologically relevant time periods.

Search for an additional, heavy Higgs boson in the H → ZZ decay channel at √s = 8 TeV in pp collision data with the ATLAS detector

Abstract: A search is presented for a high-mass Higgs boson in the , , , and decay modes using the ATLAS detector at the CERN Large Hadron Collider. The search uses proton-proton collision data at a centre-of-mass energy of 8 TeV corresponding to an integrated luminosity of 20.3 fb. The results of the search are interpreted in the scenario of a heavy Higgs boson with a width that is small compared with the experimental mass resolution. The Higgs boson mass range considered extends up to for all four decay modes and down to as low as 140 , depending on the decay mode. No significant excess of events over the Standard Model prediction is found. A simultaneous fit to the four decay modes yields upper limits on the production cross-section of a heavy Higgs boson times the branching ratio to boson pairs. 95 % confidence level upper limits range from 0.53 pb at GeV to 0.008 pb at GeV for the gluon-fusion production mode and from 0.31 pb at GeV to 0.009 pb at GeV for the vector-boson-fusion production mode. The results are also interpreted in the context of Type-I and Type-II two-Higgs-doublet models.

Big Data in food and agriculture

Abstract: Farming is undergoing a digital revolution. Our existing review of current Big Data applications in the agri-food sector has revealed several collection and analytics tools that may have implications for relationships of power between players in the food system (e.g. between farmers and large corporations). For example, Who retains ownership of the data generated by applications like Monsanto Corproation's Weed I.D. “app”? Are there privacy implications with the data gathered by John Deere's precision agricultural equipment? Systematically tracing the digital revolution in agriculture, and charting the affordances as well as the limitations of Big Data applied to food and agriculture, should be a broad research goal for Big Data scholarship. Such a goal brings data scholarship into conversation with food studies and it allows for a focus on the material consequences of big data in society.