Showing papers by "Carleton University published in 2019"
01 Jan 2019
TL;DR: Machine learning is evolved from a collection of powerful techniques in AI areas and has been extensively used in data mining, which allows the system to learn the useful structural patterns and models from training data as discussed by the authors.
Abstract: Machine learning is evolved from a collection of powerful techniques in AI areas and has been extensively used in data mining, which allows the system to learn the useful structural patterns and models from training data Machine learning algorithms can be basically classified into four categories: supervised, unsupervised, semi-supervised and reinforcement learning In this chapter, widely-used machine learning algorithms are introduced Each algorithm is briefly explained with some examples
1,668 citations
Carleton University1, Michigan State University2, University of Saskatchewan3, University of California, Santa Barbara4, Federation University Australia5, University of Colorado Boulder6, McMaster University7, Mount Allison University8, University of Washington9, Cardiff University10, Queen's University11, Leibniz Association12, University of Hong Kong13
TL;DR: Efforts to reverse global trends in freshwater degradation now depend on bridging an immense gap between the aspirations of conservation biologists and the accelerating rate of species endangerment.
Abstract: In the 12 years since Dudgeon et al. (2006) reviewed major pressures on freshwater ecosystems, the biodiversity crisis in
the world’s lakes, reservoirs, rivers, streams and wetlands has deepened. While lakes, reservoirs and rivers cover only
2.3% of the Earth’s surface, these ecosystems host at least 9.5% of the Earth’s described animal species. Furthermore,
using the World Wide Fund for Nature’s Living Planet Index, freshwater population declines (83% between 1970 and
2014) continue to outpace contemporaneous declines in marine or terrestrial systems. The Anthropocene has brought
multiple new and varied threats that disproportionately impact freshwater systems. We document 12 emerging threats
to freshwater biodiversity that are either entirely new since 2006 or have since intensified: (i) changing climates; (ii)
e-commerce and invasions; (iii) infectious diseases; (iv) harmful algal blooms; (v) expanding hydropower; (vi) emerging
contaminants; (vii) engineered nanomaterials; (viii) microplastic pollution; (ix) light and noise; (x) freshwater salinisation;
(xi) declining calcium; and (xii) cumulative stressors. Effects are evidenced for amphibians, fishes, invertebrates, microbes,
plants, turtles and waterbirds, with potential for ecosystem-level changes through bottom-up and top-down processes.
In our highly uncertain future, the net effects of these threats raise serious concerns for freshwater ecosystems. However,
we also highlight opportunities for conservation gains as a result of novel management tools (e.g. environmental flows,
environmental DNA) and specific conservation-oriented actions (e.g. dam removal, habitat protection policies,managed
relocation of species) that have been met with varying levels of success.Moving forward, we advocate hybrid approaches
that manage fresh waters as crucial ecosystems for human life support as well as essential hotspots of biodiversity and
ecological function. Efforts to reverse global trends in freshwater degradation now depend on bridging an immense gap
between the aspirations of conservation biologists and the accelerating rate of species endangerment.
1,230 citations
TL;DR: Using multiple and independent monitoring networks, population losses across much of the North American avifauna over 48 years are reported, including once-common species and from most biomes, demonstrating a continuing avifaunal crisis.
Abstract: Species extinctions have defined the global biodiversity crisis, but extinction begins with loss in abundance of individuals that can result in compositional and functional changes of ecosystems. Using multiple and independent monitoring networks, we report population losses across much of the North American avifauna over 48 years, including once-common species and from most biomes. Integration of range-wide population trajectories and size estimates indicates a net loss approaching 3 billion birds, or 29% of 1970 abundance. A continent-wide weather radar network also reveals a similarly steep decline in biomass passage of migrating birds over a recent 10-year period. This loss of bird abundance signals an urgent need to address threats to avert future avifaunal collapse and associated loss of ecosystem integrity, function, and services.
950 citations
TL;DR: Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced.
Abstract: Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.
627 citations
TL;DR: This survey investigates some of the work that has been done to enable the integrated blockchain and edge computing system and discusses the research challenges, identifying several vital aspects of the integration of blockchain andEdge computing: motivations, frameworks, enabling functionalities, and challenges.
Abstract: Blockchain, as the underlying technology of crypto-currencies, has attracted significant attention. It has been adopted in numerous applications, such as smart grid and Internet-of-Things. However, there is a significant scalability barrier for blockchain, which limits its ability to support services with frequent transactions. On the other side, edge computing is introduced to extend the cloud resources and services to be distributed at the edge of the network, but currently faces challenges in its decentralized management and security. The integration of blockchain and edge computing into one system can enable reliable access and control of the network, storage, and computation distributed at the edges, hence providing a large scale of network servers, data storage, and validity computation near the end in a secure manner. Despite the prospect of integrated blockchain and edge computing systems, its scalability enhancement, self organization, functions integration, resource management, and new security issues remain to be addressed before widespread deployment. In this survey, we investigate some of the work that has been done to enable the integrated blockchain and edge computing system and discuss the research challenges. We identify several vital aspects of the integration of blockchain and edge computing: motivations, frameworks, enabling functionalities, and challenges. Finally, some broader perspectives are explored.
488 citations
TL;DR: A comprehensive survey on the literature involving blockchain technology applied to smart cities, from the perspectives of smart citizen, smart healthcare, smart grid, smart transportation, supply chain management, and others is provided.
Abstract: In recent years, the rapid urbanization of world’s population causes many economic, social, and environmental problems, which affect people’s living conditions and quality of life significantly. The concept of “smart city” brings opportunities to solve these urban problems. The objectives of smart cities are to make the best use of public resources, provide high-quality services to the citizens, and improve the people’s quality of life. Information and communication technology plays an important role in the implementation of smart cities. Blockchain as an emerging technology has many good features, such as trust-free, transparency, pseudonymity, democracy, automation, decentralization, and security. These features of blockchain are helpful to improve smart city services and promote the development of smart cities. In this paper, we provide a comprehensive survey on the literature involving blockchain technology applied to smart cities. First, the related works and background knowledge are introduced. Then, we review how blockchain technology is applied in the realm of smart cities, from the perspectives of smart citizen, smart healthcare, smart grid, smart transportation, supply chain management, and others. Finally, some challenges and broader perspectives are discussed.
472 citations
TL;DR: A unified framework for a UAV-assisted emergency network is established in disasters by jointly optimized to provide wireless service to ground devices with surviving BSs and multihop UAV relaying to realize information exchange between the disaster areas and outside through optimizing the hovering positions of UAVs.
Abstract: Reliable and flexible emergency communication is a key challenge for search and rescue in the event of disasters, especially for the case when base stations are no longer functioning. Unmanned aerial vehicle (UAV)-assisted networking is emerging as a promising method to establish emergency networks. In this article, a unified framework for a UAV-assisted emergency network is established in disasters. First, the trajectory and scheduling of UAVs are jointly optimized to provide wireless service to ground devices with surviving BSs. Then the transceiver design of UAV and establishment of multihop ground device-to-device communication are studied to extend the wireless coverage of UAV. In addition, multihop UAV relaying is added to realize information exchange between the disaster areas and outside through optimizing the hovering positions of UAVs. Simulation results are presented to show the effectiveness of these three schemes. Finally, open research issues and challenges are discussed.
447 citations
TL;DR: The wild bootstrap was originally developed for regression models with heteroskedasticity of unknown form and has been extended to models estimated by instrumental variables over the past 30 years as discussed by the authors.
Abstract: The wild bootstrap was originally developed for regression models with heteroskedasticity of unknown form. Over the past 30 years, it has been extended to models estimated by instrumental variables...
436 citations
TL;DR: This paper provides a comprehensive survey on the literature involving machine learning algorithms applied to SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security.
Abstract: In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.
436 citations
Paul Valéry University, Montpellier III1, University of Toulouse2, King Juan Carlos University3, University of Göttingen4, Research Institute of Organic Agriculture5, Université Paris-Saclay6, Centre national de la recherche scientifique7, Agrocampus Ouest8, Lund University9, University of La Rochelle10, Institut national de la recherche agronomique11, University of Paris12, Carleton University13, University of Montpellier14, University of Rennes15, University of Lleida16, University of Alicante17, University of Murcia18, British Trust for Ornithology19, Spanish National Research Council20
TL;DR: This study provides large-scale, multitrophic, cross-regional evidence that increasing crop heterogeneity can be an effective way to increase biodiversity in agricultural landscapes without taking land out of agricultural production.
Abstract: Agricultural landscape homogenization has detrimental effects on biodiversity and key ecosystem services. Increasing agricultural landscape heterogeneity by increasing seminatural cover can help to mitigate biodiversity loss. However, the amount of seminatural cover is generally low and difficult to increase in many intensively managed agricultural landscapes. We hypothesized that increasing the heterogeneity of the crop mosaic itself (hereafter “crop heterogeneity”) can also have positive effects on biodiversity. In 8 contrasting regions of Europe and North America, we selected 435 landscapes along independent gradients of crop diversity and mean field size. Within each landscape, we selected 3 sampling sites in 1, 2, or 3 crop types. We sampled 7 taxa (plants, bees, butterflies, hoverflies, carabids, spiders, and birds) and calculated a synthetic index of multitrophic diversity at the landscape level. Increasing crop heterogeneity was more beneficial for multitrophic diversity than increasing seminatural cover. For instance, the effect of decreasing mean field size from 5 to 2.8 ha was as strong as the effect of increasing seminatural cover from 0.5 to 11%. Decreasing mean field size benefited multitrophic diversity even in the absence of seminatural vegetation between fields. Increasing the number of crop types sampled had a positive effect on landscape-level multitrophic diversity. However, the effect of increasing crop diversity in the landscape surrounding fields sampled depended on the amount of seminatural cover. Our study provides large-scale, multitrophic, cross-regional evidence that increasing crop heterogeneity can be an effective way to increase biodiversity in agricultural landscapes without taking land out of agricultural production.
277 citations
Carleton University1, National Autonomous University of Mexico2, Université de Sherbrooke3, State University of Santa Cruz4, University of Ottawa5, University of Southampton6, University of British Columbia7, University of California, Davis8, Concordia University9, University of Manitoba10, University of Montpellier11, University of São Paulo12, University of Queensland13, Commonwealth Scientific and Industrial Research Organisation14, University of Tennessee15, Canadian Wildlife Service16, John Carroll University17
TL;DR: In a review of landscape-scale empirical studies, Fahrig as mentioned in this paper found that ecological responses to habitat fragmentation per se (fragmentation independent of habitat amount) were usually non-significant (>70% of responses) and that 76% of significant relationships were positive, with species abundance, occurrence, richness, and other response variables increasing with habitat fragmentation.
TL;DR: In this article, a search for high-mass dielectron and dimuon resonances in the mass range of 250 GeV to 6 TeV was performed at the Large Hadron Collider.
TL;DR: The Belle II detector as mentioned in this paper is a state-of-the-art detector for heavy flavor physics, quarkonium and exotic states, searches for dark sectors, and many other areas.
Abstract: The Belle II detector will provide a major step forward in precision heavy flavor physics, quarkonium and exotic states, searches for dark sectors, and many other areas. The sensitivity to a large number of key observables can be improved by about an order of magnitude compared to the current measurements, and up to two orders in very clean search measurements. This increase in statistical precision arises not only due to the increased luminosity, but also from improved detector efficiency and precision for many channels. Many of the most interesting observables tend to have very small theoretical uncertainties that will therefore not limit the physics reach. This book has presented many new ideas for measurements, both to elucidate the nature of current anomalies seen in flavor, and to search for new phenomena in a plethora of observables that will become accessible with the Belle II dataset. The simulation used for the studiesinthis book was state ofthe artat the time, though weare learning a lot more about the experiment during the commissioning period. The detector is in operation, and working spectacularly well.
University of Florida1, Commonwealth Scientific and Industrial Research Organisation2, University of Freiburg3, Natural History Museum of Geneva4, Smithsonian Institution5, Brigham Young University6, Queen Mary University of London7, Boise State University8, China Agricultural University9, Florida Museum of Natural History10, Carleton University11
TL;DR: It is demonstrated that the most recent common ancestor of Lepidoptera is considerably older than previously hypothesized, and it is shown that multiple lineages of moths independently evolved hearing organs well before the origin of bats, rejecting the hypothesis that lepidopteran hearing organs arose in response to these predators.
Abstract: Butterflies and moths (Lepidoptera) are one of the major superradiations of insects, comprising nearly 160,000 described extant species. As herbivores, pollinators, and prey, Lepidoptera play a fundamental role in almost every terrestrial ecosystem. Lepidoptera are also indicators of environmental change and serve as models for research on mimicry and genetics. They have been central to the development of coevolutionary hypotheses, such as butterflies with flowering plants and moths' evolutionary arms race with echolocating bats. However, these hypotheses have not been rigorously tested, because a robust lepidopteran phylogeny and timing of evolutionary novelties are lacking. To address these issues, we inferred a comprehensive phylogeny of Lepidoptera, using the largest dataset assembled for the order (2,098 orthologous protein-coding genes from transcriptomes of 186 species, representing nearly all superfamilies), and dated it with carefully evaluated synapomorphy-based fossils. The oldest members of the Lepidoptera crown group appeared in the Late Carboniferous (∼300 Ma) and fed on nonvascular land plants. Lepidoptera evolved the tube-like proboscis in the Middle Triassic (∼241 Ma), which allowed them to acquire nectar from flowering plants. This morphological innovation, along with other traits, likely promoted the extraordinary diversification of superfamily-level lepidopteran crown groups. The ancestor of butterflies was likely nocturnal, and our results indicate that butterflies became day-flying in the Late Cretaceous (∼98 Ma). Moth hearing organs arose multiple times before the evolutionary arms race between moths and bats, perhaps initially detecting a wide range of sound frequencies before being co-opted to specifically detect bat sonar. Our study provides an essential framework for future comparative studies on butterfly and moth evolution.
TL;DR: Simulations results show that the proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of the IIeT.
Abstract: Recent advances in the industrial Internet of things (IIoT) provide plenty of opportunities for various industries. To address the security and efficiency issues of the massive IIoT data, blockchain is widely considered as a promising solution to enable data storing/processing/sharing in a secure and efficient way. To meet the high throughput requirement, this paper proposes a novel deep reinforcement learning (DRL)-based performance optimization framework for blockchain-enabled IIoT systems, the goals of which are threefold: 1) providing a methodology for evaluating the system from the aspects of scalability, decentralization, latency, and security; 2) improving the scalability of the underlying blockchain without affecting the system's decentralization, latency, and security; and 3) designing a modulable blockchain for IIoT systems, where the block producers, consensus algorithm, block size, and block interval can be selected/adjusted using the DRL technique. Simulations results show that our proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of the IIoT.
TL;DR: An exclusion limit on the H→invisible branching ratio of 0.26(0.17_{-0.05}^{+0.07}) at 95% confidence level is observed (expected) in combination with the results at sqrt[s]=7 and 8 TeV.
Abstract: Dark matter particles, if sufficiently light, may be produced in decays of the Higgs boson. This Letter presents a statistical combination of searches for H→invisible decays where H is produced according to the standard model via vector boson fusion, Z(ll)H, and W/Z(had)H, all performed with the ATLAS detector using 36.1 fb^{-1} of pp collisions at a center-of-mass energy of sqrt[s]=13 TeV at the LHC. In combination with the results at sqrt[s]=7 and 8 TeV, an exclusion limit on the H→invisible branching ratio of 0.26(0.17_{-0.05}^{+0.07}) at 95% confidence level is observed (expected).
TL;DR: This paper considers a cognitive vehicular network that uses the TVWS band, and forms a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time, and designs an algorithm called DDORV to tackle the joint optimization problem.
Abstract: The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes.
TL;DR: In this article, an improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail, including corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies.
Abstract: This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb−1 of proton-proton collision data collected at √s=13 TeV between 2015 and 2017.
TL;DR: In this article, the ATLAS Collaboration during Run 2 of the Large Hadron Collider (LHC) was used to identify jets containing b-hadrons, and the performance of the algorithms was evaluated in the s...
Abstract: The algorithms used by the ATLAS Collaboration during Run 2 of the Large Hadron Collider to identify jets containing b-hadrons are presented. The performance of the algorithms is evaluated in the s ...
TL;DR: The scalability issue is discussed from the perspectives of throughput, storage and networking, and existing enabling technologies for scalable blockchain systems are presented.
Abstract: In the past decade, crypto-currencies such as Bitcoin and Litecoin have developed rapidly. Blockchain as the underlying technology of these digital crypto-currencies has attracted great attention from academia and industry. Blockchain has many good features, such as trust-free, transparency, anonymity, democracy, automation, decentralization and security. Despite these promising features, scalability is still a key barrier when the blockchain technology is widely used in real business environments. In this article, we focus on the scalability issue, and provide a brief survey of recent studies on scalable blockchain systems. We first discuss the scalability issue from the perspectives of throughput, storage and networking. Then, existing enabling technologies for scalable blockchain systems are presented. We also discuss some research challenges and future research directions for scalable blockchain systems.
TL;DR: A near-optimal algorithm, named Energy Cost Optimization via Trade (ECO-Trade), is proposed, which coordinates P2P energy trading among the smart homes with a Demand Side Management (DSM) system and shows that cost savings do not always increase linearly with an increase in the renewables and storage penetration rate.
TL;DR: A novel blockchain-based framework with an adaptive block size for video streaming with mobile edge computing (MEC) and an incentive mechanism to facilitate collaboration among content creators, video transcoders, and consumers is proposed.
Abstract: Blockchain-based video streaming systems aim to build decentralized peer-to-peer networks with flexible monetization mechanisms for video streaming services. On these blockchain-based platforms, video transcoding, which is computationally intensive and time-consuming, is still a major challenge. Meanwhile, the block size of the underlying blockchain has significant impacts on the system performance. Therefore, this paper proposes a novel blockchain-based framework with an adaptive block size for video streaming with mobile edge computing (MEC). First, we design an incentive mechanism to facilitate collaboration among content creators, video transcoders, and consumers. In addition, we present a block size adaptation scheme for blockchain-based video streaming. Moreover, we consider two offloading modes, i.e., offloading to the nearby MEC nodes or a group of device-to-device (D2D) users, to avoid the overload of MEC nodes. Then, we formulate the issues of resource allocation, scheduling of offloading, and adaptive block size as an optimization problem. We employ a low-complexity alternating direction method of the multipliers-based algorithm to solve the problem in a distributed fashion. Simulation results are presented to show the effectiveness of the proposed scheme.
TL;DR: This paper simultaneously tackles the issues of content caching strategy, computation offloading policy, and radio resource allocation, and propose a joint optimization solution for the fog-enabled IoT and uses the actor–critic reinforcement learning framework to solve the joint decision-making problem.
Abstract: The cloud-based Internet of Things (IoT) develops rapidly but suffer from large latency and backhaul bandwidth requirement, the technology of fog computing and caching has emerged as a promising paradigm for IoT to provide proximity services, and thus reduce service latency and save backhaul bandwidth. However, the performance of the fog-enabled IoT depends on the intelligent and efficient management of various network resources, and consequently the synergy of caching, computing, and communications becomes the big challenge. This paper simultaneously tackles the issues of content caching strategy, computation offloading policy, and radio resource allocation, and propose a joint optimization solution for the fog-enabled IoT. Since wireless signals and service requests have stochastic properties, we use the actor–critic reinforcement learning framework to solve the joint decision-making problem with the objective of minimizing the average end-to-end delay. The deep neural network (DNN) is employed as the function approximator to estimate the value functions in the critic part due to the extremely large state and action space in our problem. The actor part uses another DNN to represent a parameterized stochastic policy and improves the policy with the help of the critic. Furthermore, the Natural policy gradient method is used to avoid converging to the local maximum. Using the numerical simulations, we demonstrate the learning capacity of the proposed algorithm and analyze the end-to-end service latency.
TL;DR: This review sets out to understand the reactivity of diradicals and how that may differ from monoradicals, and examines activation energies of prototypical radical reactions for representative organic diradICALs and diradicaloids in their two lowest spin states.
Abstract: This review sets out to understand the reactivity of diradicals and how that may differ from monoradicals. In the first part of the review, we delineate the electronic structure of a diradical with its two degenerate or nearly degenerate molecular orbitals, occupied by two electrons. A classification of diradicals based on whether or not the two SOMOs can be located on different sites of the molecule is useful in determining the ground state spin. Important is a delocalized to localized orbital transformation that interchanges "closed-shell" to "open-shell" descriptions. The resulting duality is useful in understanding the dual reactivity of singlet diradicals. In the second part of the review, we examine, with a consistent level of theory, activation energies of prototypical radical reactions (dimerization, hydrogen abstraction, and addition to ethylene) for representative organic diradicals and diradicaloids in their two lowest spin states. Differences and similarities in reactivity of diradicals vs monoradicals, based on either a localized or delocalized view, whichever is suitable, are then discussed. The last part of this review begins with an extensive, comparative, and critical survey of available measures of diradical character and ends with an analysis of the consequences of diradical character for selected diradicaloids.
University of Erlangen-Nuremberg1, Carleton University2, Duke University3, University of Illinois at Urbana–Champaign4, Kurchatov Institute5, University of South Dakota6, SLAC National Accelerator Laboratory7, McGill University8, TRIUMF9, Colorado State University10, Laurentian University11, University of North Carolina at Wilmington12, Indiana University13, Stanford University14, Drexel University15, University of Massachusetts Amherst16, Technische Universität München17, University of Maryland, College Park18, University of Alabama19, Wright Laboratory20, Stony Brook University21, California Institute of Technology22, University of Bern23
TL;DR: In this paper, a search for neutrinoless double-β decay (0νββ) was performed with the full EXO-200 dataset using a deep neural network to discriminate between 0νβ β and background events.
Abstract: A search for neutrinoless double-β decay (0νββ) in ^{136}Xe is performed with the full EXO-200 dataset using a deep neural network to discriminate between 0νββ and background events. Relative to previous analyses, the signal detection efficiency has been raised from 80.8% to 96.4±3.0%, and the energy resolution of the detector at the Q value of ^{136}Xe 0νββ has been improved from σ/E=1.23% to 1.15±0.02% with the upgraded detector. Accounting for the new data, the median 90% confidence level 0νββ half-life sensitivity for this analysis is 5.0×10^{25} yr with a total ^{136}Xe exposure of 234.1 kg yr. No statistically significant evidence for 0νββ is observed, leading to a lower limit on the 0νββ half-life of 3.5×10^{25} yr at the 90% confidence level.
TL;DR: In this paper, the decays of B0 s! + and B0! + have been studied using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016.
Abstract: A study of the decays B0 s ! + and B0 ! + has been performed using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016. Since the detector resolut ...
TL;DR: A preliminary assessment of human exposure to OPEs through dietary intake suggested that the OPE estimated daily intake for humans was around 880 ng/kg bw/day (95th percentile), which was well below the corresponding OPE health reference dose given by the U.S. EPA.
University of Zurich1, University of Northern British Columbia2, International Centre for Integrated Mountain Development3, University of Saskatchewan4, Yunnan University5, Indian Institutes of Technology6, Chinese Academy of Sciences7, Carleton University8, Utrecht University9, Indian Institute of Science10, COMSATS Institute of Information Technology11
TL;DR: In this article, the authors defined the presence of frozen water in its many forms: glaciers, ice caps, ice sheets, snow, permafrost, and river and lake ice.
Abstract: The cryosphere is defined by the presence of frozen water in its many forms: glaciers, ice caps, ice sheets, snow, permafrost, and river and lake ice. In the extended Hindu Kush Himalaya (HKH) region, including the Pamirs, Tien Shan and Alatua, the cryosphere is a key freshwater resource, playing a vital and significant role in local and regional hydrology and ecology. Industry, agriculture, and hydroelectric power generation rely on timely and sufficient delivery of water in major river systems; changes in the cryospheric system may thus pose challenges for disaster risk reduction in the extended HKH region.
TL;DR: A systematic literature review of a sample of 114 scholarly articles about living labs explored the origin of the living lab concept and its key paradigms and characteristics, including stakeholder roles, contexts, challenges, main outcomes, and sustainability.
TL;DR: In this article, the effects of process parameters in TIG-based WAAM for specimens created using Hastelloy X alloy (Haynes International) welding wire and 304 stainless-steel plate as the substrate were discussed.
Abstract: This paper discusses the effects of process parameters in TIG based WAAM for specimens created using Hastelloy X alloy (Haynes International) welding wire and 304 stainless-steel plate as the substrate. The Taguchi method and ANOVA were used to determine the effects of travel speed, wire feed rate, current, and argon flow rate on the responses including bead shape and size, bead roughness, oxidation levels, melt through depth, and the microstructure. Travel speed and current were found to have the largest effect on the responses. Increasing travel speed or decreasing current caused a decrease in melt through depth and an increase in roughness. Printing strategies were tested using specimens of multiple layers and no significant difference was found between printing layers in the same direction and printing layers in alternating directions. No observable interface between the layers was present suggesting a complete fusion between layers with no oxidation. Three distinct zones were identified within the three- and eight-layer samples. The zones were characterized by the size and distribution of the molybdenum carbide formations within the matrix grain formations.