University of Regina

Author(s): Collaboration, The ATLAS; Aad, G; Abat, E; Abdallah, J; Abdelalim, AA; Abdesselam, A; Abdinov, O; Abi, BA; Abolins, M; Abramowicz, H; Acerbi, E; Acharya, BS; Achenbach, R; Ackers, M; Adams, DL; Adamyan, F; Addy, TN; Aderholz, M; Adorisio, C; Adragna, P; Aharrouche, M; Ahlen, SP; Ahles, F; Ahmad, A; Ahmed, H; Aielli, G; Åkesson, PF; Åkesson, TPA; Akimov, AV; Alam, SM; Albert, J; Albrand, S; Aleksa, M; Aleksandrov, IN; Aleppo, M; Alessandria, F; Alexa, C; Alexander, G; Alexopoulos, T; Alimonti, G; Aliyev, M; Allport, PP; Allwood-Spiers, SE; Aloisio, A; Alonso, J; Alves, R; Alviggi, MG; Amako, K; Amaral, P; Amaral, SP; Ambrosini, G; Ambrosio, G; Amelung, C; Ammosov, VV; Amorim, A; Amram, N; Anastopoulos, C; Anderson, B; Anderson, KJ; Anderssen, EC; Andreazza, A; Andrei, V; Andricek, L; Andrieux, M-L; Anduaga, XS; Anghinolfi, F; Antonaki, A; Antonelli, M; Antonelli, S; Apsimon, R; Arabidze, G; Aracena, I; Arai, Y; Arce, ATH; Archambault, JP; Arguin, J-F; Arik, E; Arik, M; Arms, KE; Armstrong, SR; Arnaud, M; Arnault, C; Artamonov, A; Asai, S; Ask, S

Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

We explore the role of lakes in carbon cycling and global climate, examine the mechanisms influencing carbon pools and transformations in lakes, and discuss how the metabolism of carbon in the inland waters is likely to change in response to climate. Furthermore, we project changes as global climate change in the abundance and spatial distribution of lakes in the biosphere, and we revise the estimate for the global extent of carbon transformation in inland waters. This synthesis demonstrates that the global annual emissions of carbon dioxide from inland waters to the atmosphere are similar in magnitude to the carbon dioxide uptake by the oceans and that the global burial of organic carbon in inland water sediments exceeds organic carbon sequestration on the ocean floor. The role of inland waters in global carbon cycling and climate forcing may be changed by human activities, including construction of impoundments, which accumulate large amounts of carbon in sediments and emit large amounts of methane to the atmosphere. Methane emissions are also expected from lakes on melting permafrost. The synthesis presented here indicates that (1) inland waters constitute a significant component of the global carbon cycle, (2) their contribution to this cycle has significantly changed as a result of human activities, and (3) they will continue to change in response to future climate change causing decreased as well as increased abundance of lakes as well as increases in the number of aquatic impoundments.

The Large Hadron Collider (LHC) at CERN will extend the frontiers of particle physics with its
\nunprecedented high energy and luminosity. Inside the LHC, bunches of up to 1011 protons (p)
\nwill collide 40 million times per second to provide 14 TeV proton-proton collisions at a design
\nluminosity of 1034 cm􀀀2s􀀀1. The LHC will also collide heavy ions (A), in particular lead nuclei, at
\n5.5 TeV per nucleon pair, at a design luminosity of 1027 cm􀀀2s􀀀1.
\nThe high interaction rates, radiation doses, particle multiplicities and energies, as well as the
\nrequirements for precision measurements have set new standards for the design of particle detectors.
\nTwo general purpose detectors, ATLAS (A Toroidal LHC ApparatuS) and CMS (Compact
\nMuon Solenoid) have been built for probing p-p and A-A collisions.
\nThis paper presents a comprehensive overview of the ATLAS detector prior to the first LHC
\ncollisions, written as the installation of the ATLAS detector is nearing completion. This detector
\nrepresents the work of a large collaboration of several thousand physicists, engineers, technicians,
\nand students over a period of fifteen years of dedicated design, development, fabrication, and installation.

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Across two studies with more than 1,700 U.S. adults recruited online, we present evidence that people share false claims about COVID-19 partly because they simply fail to think sufficiently about whether or not the content is accurate when deciding what to share. In Study 1, participants were far worse at discerning between true and false content when deciding what they would share on social media relative to when they were asked directly about accuracy. Furthermore, greater cognitive reflection and science knowledge were associated with stronger discernment. In Study 2, we found that a simple accuracy reminder at the beginning of the study (i.e., judging the accuracy of a non-COVID-19-related headline) nearly tripled the level of truth discernment in participants' subsequent sharing intentions. Our results, which mirror those found previously for political fake news, suggest that nudging people to think about accuracy is a simple way to improve choices about what to share on social media.

The transnational perspectives offered in Indigenous Research Methodologies by Bagele Chilisa make this book not only a valuable resource for university- and community-based research and engagement, but also one with practical and wide-reaching appeal for scholars, community researchers, and graduate students. Bagele Chilisa, a Botswanabased scholar, undertakes an extensive examination of Indigenous methodologies that draws on theories and practices from a variety of cultural and academic contexts. Her examples range across African proverbs and songs, Indigenous story-telling and mixed research methods including Indigenized Euro-Western approaches.

The T2K experiment observes indications of ν(μ) → ν(e) appearance in data accumulated with 1.43×10(20) protons on target. Six events pass all selection criteria at the far detector. In a three-flavor neutrino oscillation scenario with |Δm(23)(2)| = 2.4×10(-3) eV(2), sin(2)2θ(23) = 1 and sin(2)2θ(13) = 0, the expected number of such events is 1.5±0.3(syst). Under this hypothesis, the probability to observe six or more candidate events is 7×10(-3), equivalent to 2.5σ significance. At 90% C.L., the data are consistent with 0.03(0.04) < sin(2)2θ(13) < 0.28(0.34) for δ(CP) = 0 and a normal (inverted) hierarchy.

Robert H. Whittaker defined beta diversity as the variation in species composition among sites in a geographic area. Beta diversity is a key concept for understanding the functioning of ecosystems, for the conservation of biodiversity, and for ecosystem management. This paper explains how hypotheses about the origin of beta diversity can be tested by partitioning the spatial variation of community composition data (presence– absence or abundance data) with respect to environmental variables and spatial base functions. We compare two statistical methods to accomplish that. The sum‐of‐squares of a community composition data table, which is one possible measure of beta diversity, is correctly partitioned by canonical ordination; hence, canonical partitioning produces correct estimates of the different portions of community composition variation. In recent years, several authors interested in the variation in community composition among sites (beta diversity) have used another method, variation partitioning on distance matrices (Mantel approach). Their results led us to compare the two partitioning approaches, using simulated data generated under hypotheses about the variation of community composition among sites. The theoretical developments and simulation results led to the following observations: (1) the variance of a community composition table is a measure of beta diversity. (2) The variance of a dissimilarity matrix among sites is not the variance of the community composition table nor a measure of beta diversity; hence, partitioning on distance matrices should not be used to study the variation in community composition among sites. (3) In all of our simulations, partitioning on distance matrices underestimated the amount of variation in community composition explained by the raw‐data approach, and (4) the tests of significance had less power than the tests of canonical ordination. Hence, the proper statistical procedure for partitioning the spatial variation of community composition data among environmental and spatial components, and for testing hypotheses about the origin and maintenance of variation in community composition among sites, is canonical partitioning. The Mantel approach is appropriate for testing other hypotheses, such as the variation in beta diversity among groups of sites. Regression on distance matrices is also appropriate for fitting models to similarity decay plots.

Critical realism (CR) is a useful philosophical framework for social science; however, little guidance is available on which precise methods – including methods of data collection, coding, and analysis – are best suited to applied CR research. This article provides a concrete example of applied qualitative research using CR as a philosophical and methodological framework. Drawing examples from a study of Canadian farm women’s experiences with agricultural policy, I suggest a flexible deductive process of coding and data analysis that is consistent with CR ontology and epistemology. The paper follows the typical stages of qualitative research while demonstrating the application of methods informed by CR at each stage. Important considerations CR ontology and epistemology raise, such as the use of existing theory and critical engagement with participants’ knowledge and experience, are discussed throughout. Ultimately, I identify two key causal mechanisms shaping the lives of farm women and suggest a future direction for feminist political economy theory to more effectively analyze women’s work in agricultural contexts.

This paper describes the development and validation of the Emotional Labour Scale (ELS) as tested on samples of 296 and 238 respondents. The ELS is a 15‐item self‐report questionnaire that measures six facets of emotional display in the workplace, including the frequency, intensity and variety of emotional display, the duration of interaction, and surface and deep acting. Estimates of internal consistency for the subscales ranged from .74 to .91. Confirmatory factor analysis results provided support for the existence of six unidimensional subscales. Evidence was also provided for convergent and discriminant validity.

Research and clinical observations suggest that during times of pandemic many people exhibit stress- or anxiety-related responses that include fear of becoming infected, fear of coming into contact with possibly contaminated objects or surfaces, fear of foreigners who might be carrying infection (i.e., disease-related xenophobia), fear of the socio-economic consequences of the pandemic, compulsive checking and reassurance-seeking regarding possible pandemic-related threats, and traumatic stress symptoms about the pandemic (e.g., nightmares, intrusive thoughts). We developed the 36-item COVID Stress Scales (CSS) to measure these features, as they pertain to COVID-19. The CSS were developed to better understand and assess COVID-19-related distress. The scales were intentionally designed so they could be readily adapted for future pandemics. The CSS were developed and initially validated in population-representative samples from Canada (N = 3479) and the United States (N = 3375). A stable 5-factor solution was identified, corresponding to scales assessing COVID-related stress and anxiety symptoms: (1) Danger and contamination fears, (2) fears about economic consequences, (3) xenophobia, (4) compulsive checking and reassurance seeking, and (5) traumatic stress symptoms about COVID-19. The scales performed well on various indices of reliability and validity. The scales were intercorrelated, providing evidence of a COVID Stress Syndrome. The scales offer promise as tools for better understanding the distress associated with COVID-19 and for identifying people in need of mental health services.

We synthesize a burgeoning literature investigating why people believe and share false or highly misleading news online. Contrary to a common narrative whereby politics drives susceptibility to fake news, people are 'better' at discerning truth from falsehood (despite greater overall belief) when evaluating politically concordant news. Instead, poor truth discernment is associated with lack of careful reasoning and relevant knowledge, and the use of heuristics such as familiarity. Furthermore, there is a substantial disconnect between what people believe and what they share on social media. This dissociation is largely driven by inattention, more so than by purposeful sharing of misinformation. Thus, interventions can successfully nudge social media users to focus more on accuracy. Crowdsourced veracity ratings can also be leveraged to improve social media ranking algorithms.

Much of the existing research on disordered eating has centered on the drive for thinness, which is most commonly observed in girls and women. The male standard of bodily attractiveness, however, is bigger, bulkier, and more muscular. Are boys and men motivated to be big and muscular in the same way that girls and women are motivated to be thin? The authors constructed a 15-item survey and administered it to 197 adolescents. The findings showed that the drive for muscularity measure displayed good reliability; that individuals high in the drive were more likely to be boys who were trying to gain both weight and muscle mass; that the drive was related to poor self-esteem and higher levels of depression among boys, but not among girls; and that the drive for muscularity was relatively unrelated to the drive for thinness.

In this paper, we discuss an extension to two popular approaches to modeling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modeling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between HGAMs, HGLMs, and GAMs, explain how to model different assumptions about the degree of intergroup variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, the mgcv package in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data. All code and data used to generate this paper are available at: github.com/eric-pedersen/mixed-effect-gams .

Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross-selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socio-economic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR.

Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced. This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives strategies for selecting appropriate measures for applications, and identifies opportunities for future research in this area.

Recent controversy surrounds the use of the Trail Making Test as a measure of cognitive flexibility, given that the Trail Making Test, Part B (TMT-B) also differs from Part A (TMT-A) in factors of motor control and perceptual complexity. The present study compared performance in the TMT and a set-switching task in order to test the assumption that cognitive flexibility is captured in TMT-B performance. Set-switching tasks have low motor and perceptual selection demands, and therefore provide a clearer index of executive function. In this study, participants made category judgments for digits, letters, or symbols across a series of trials, and performance for consecutive same-task trials was compared with task-switch trials. Results of the set-switching task indicated significant switch cost, but only for the situation of task alternation (e.g., an ABA series), suggesting that task-set inhibition may play a role in this effect. Alternating-switch cost was significantly correlated with TMT-B performance, especially with the TMT-B to TMT-A ratio (B/A). Cost for alternating switches was especially large for participants with B/A ratio > 3. These results provide direct evidence that the B/A ratio of performance in the TMT provides an index of executive function.

The current review and synthesis serves to define and contextualize fear of the unknown relative to related constructs, such as intolerance of uncertainty, and contemporary models of emotion, attachment, and neuroticism. The contemporary models appear to share a common core in underscoring the importance of responses to unknowns. A recent surge in published research has explored the transdiagnostic impact of not knowing on anxiety and related pathologies; as such, there appears to be mounting evidence for fear of the unknown as an important core transdiagnostic construct. The result is a robust foundation for transdiagnostic theoretical and empirical explorations into fearing the unknown and intolerance of uncertainty.

This study uses S. E. Hobfoll's (1989) conservation of resources theory as a means of examining why emotional labor may or may not result in burnout. A model is developed that proposes that workers attempt to cope with role demands by performing surface or deep acting and that the effect of this expenditure of resources on worker burnout depends on the more immediate rewards of the service encounter and the application of internal and external resources specific to the needs at hand. This model is tested and confirmed using cross-sectional survey responses from 236 working adults. Research and practical implications are discussed.