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Electrostatic gyrokinetic instabilities and turbulence in the Wendelstein 7-X stellarator are studied. Particular attention is paid to the ion-temperature-gradient (ITG) instability and its character close to marginal stability [Floquet-type turbulence (Zocco et al., Phys. Rev. E, vol. 106, 2022, p. L013202) with no electron temperature gradient]. The flux tube version of the $\delta f$ code stella (Barnes et al., J. Comput. Phys., vol. 391, 2019, pp. 365–380) is used to run linear and nonlinear gyrokinetic simulations with kinetic electrons. The nature of the dominant instability depends on the wavelength perpendicular to the magnetic field, and the results are conveniently displayed in stability diagrams that take this dependence into account. This approach highlights the presence of universal instabilities, which are less unstable but have longer wavelengths than other modes. A quasi-linear estimate of the heat flux suggests they are relevant for transport. Close to the stability threshold, the linear eigenmodes and turbulence form highly extended structures along the computational domain if the magnetic shear is small. Numerical experiments and diagnostics are undertaken to assess the resulting radial localisation of the turbulence, which affects the interaction of the latter with zonal flows. Increasing the amplitude of the magnetic shear (e.g.through current drive) has a stabilising effect on the turbulence and, thus, reduces the nonlinear energy transport.
Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.
Reading is an important skill, and becomes even more so beyond elementary years, when the focus shifts to comprehension as a means of learning and understanding academic material across subjects (Kamil et al., 2008; Shanahan et al., 2010; Snow, 2002). One construct receiving much recent interest in research, especially that related to academic achievement, is mind wandering (MW). MW has been defined as "a shift away from a primary task toward internal information" (Smallwood & Schooler, 2006). Though it is known to be ubiquitous among people (McVay & Kane, 2012), there are numerous theories about why MW occurs, in different contexts, and in relation to various other factors, and no one theory is currently dominant. MW and other factors such as working memory (WM) and decoding are all known to influence functional outcomes such as reading comprehension (RC), but there is little information on how all of these factors interact with one another with regard to RC. Most prior work focuses on adults and thus generalization to children is still needed. Therefore, the goals of this project were to examine the roles of WM, MW, decoding, and their interactions in relation to RC. It was hypothesized that each would demonstrate a significant relationship with the outcome of RC and that they would interact with one another beyond their individual main effects.
Participants and Methods:
The sample included 214 6th and 7th grade students with a larger proportion of struggling readers. Participants were each administered the Kaufman Test of Educational Achievement -Third Edition (KTEA-3; Kaufman & Kaufman, 2014) Letter Word Recognition subtest (decoding), the Weschler Intelligence Scale for Children - Fifth Edition (WISC-5; Wechsler, 2014) Digit Span and Picture Span subtests(WM), and the Gates-MacGinitie Reading Tests - Fourth Edition (GMRT-4; MacGinitie, 1978) Comprehension subtest (RC). Four measures of MW were administered: the trait-based Mind Wandering Questionnaire (MWQ; Mrazek et al., 2013); two task-based (or state-dependent) retrospective reporting (TBRR) questionnaires (Matthews et al., 2002), and a researcher-generated single-item task-based retrospective report administered after four tasks. Correlations and regression were utilized to evaluate the relationships among predictor variables, and with regard to RC, including how predictors moderate one another.
Results:
All three key predictors demonstrated a significant relationship with RC both via zero-order correlations and main effects in the context of interactive relationships. WM and decoding demonstrated positive relationships with RC and MW demonstrated a negative relationship with RC, though only when one (MWQ) measure of MW was used, rather than the TBRR measure. There was a significant interaction of decoding and MW as measured by the TBRR questionnaires on the outcome of RC. Other interactions were not significant.
Conclusions:
These results clarify the interactive relationships of these three key predictors on the important academic achievement outcome of RC, ultimately suggesting that intervention strategies for achievement problems in areas such as RC should consider MW in conjunction with decoding abilities in order to implement effective strategies that capitalize on individual children's strengths and build on their particular weaknesses.
Chapter 3 empirically addresses the question: Do citizens want to be represented by members of the working class? We demonstrate, using novel survey data from Argentina and Mexico and publicly available cross-national data from LAPOP, that citizens do prefer to be represented by legislators from the working class. To do this, we first examined patterns of support for working-class representation using a series of original survey questions in Argentina and Mexico. We asked citizens about their preferences for working-class representation and show that the average citizen in Argentina and Mexico both want more working-class deputies to occupy seats in congress. Then we introduce data on the class background of legislators obtained from elite survey data, and present descriptive information about the occupations, gender, and race/ethnicity of working-class deputies. Finally, using cross-national survey data and these data on legislators’ class background, we demonstrate that citizens have better evaluations of representative institutions when working-class deputies hold a higher share of seas in the national assembly.
Chapter 7 examines how the relationship between working-class representation and positive evaluations of representative institutions varies among citizens who are more or less likely to be aware of working-class representation. Even though voters can learn about working-class representation through political campaigns, news, and paying attention to politics, we show that levels of political interest and news consumption vary dramatically among citizens within the same country – implying that not all voters are equally likely to be aware of working-class representation. Then, using survey data from across Latin America, we demonstrate that the positive relationship between working-class representation and better evaluations of representative institutions is strongest among citizens with high levels of political interest and those who are avid news followers.
Chapter 6 addresses our final question: How do voters know workers are in office? Our theory argues that even though citizens are unlikely to know the exact share of seats workers occupy in office, they are generally aware of working-class representation. Drawing on campaign material, candidate websites, and social media websites, we show that both parties and individual politicians have an incentive to showcase politicians’ class status. Then we present qualitative evidence from publicly available data, coupled with an inventory of government websites, to show that even absent these political incentives, information on candidates’ class background is publicly available and – at least some of this information – makes it into the hands of citizens, thanks to popular press. Then we turn to evidence from two survey experiments from Argentina and Mexico that were designed to evaluate whether citizens can glean information about deputies’ class status from facial images alone. We demonstrate that participants can correctly identify the class background of the national deputies depicted in photographs at a rate significantly better than chance.
This chapter develops our theory of working-class inclusion. The chapter is structured around the three central questions that we tackle in this book: (1) Do citizens – and particularly working-class citizens – want to be represented by members of the working class? (2) Will any worker do? Or, how do citizens evaluate workers who do not represent working-class policy interests? (3) How do voters know workers are in office? In answering these questions, we develop new expectations that we evaluate in the following chapters.
Drawing on examples from across Latin America, Chapter 1 introduces the political exclusion of the working class and the puzzles that motivate the book: (1) Do citizens – and particularly working-class citizens – want to be represented by members of the working class? (2) Do citizens know workers are in office? (3) How do citizens evaluate workers who do not represent working-class policy interests? The chapter previews our theory in general terms and provides an overview of the data and cases we use to tackle these important questions. The chapter concludes by introducing the major implications of our findings.