High-power-density tokamaks offer a potential solution to design cost-effective fusion devices. One way to achieve high power density is to operate at a high $\beta$ value (the ratio of thermal to magnetic pressure), i.e. $\beta \sim 1$. However, a $\beta \sim 1$ state may be unstable to various pressure- and current-driven instabilities or have unfavourable microstability properties. To explore these possibilities, we generate $\beta \sim 1$ equilibria and investigate their stability. First, we demonstrate the generation of high-$\beta$ equilibria with the computer code VMEC. We then analyse these equilibria to determine their stability against the infinite-$n$ ideal-ballooning mode. We follow that by engaging in a detailed microstability study using the GS2 code, beginning with assessments of electrostatic ion-temperature-gradient and trapped election mode instabilities. We observe interesting behaviour for the high-$\beta$ equilibria – stabilization of these modes through two distinct mechanisms – large negative local shear and reversal of electron precession drift. Finally, we perform electromagnetic gyrokinetic simulations and observe enhanced stability in the outer core of high-$\beta$ equilibria and absence of kinetic ballooning modes in the negative-triangularity, high-$\beta$ equilibria. The enhanced outer-core stability of high-$\beta$ equilibria is different from their lower-$\beta$ counterparts and offers an alternative, potentially favourable regime of tokamak operation.

This paper examines the experience of the Hyundai Motor Company (HMC) managing its employees in a new plant established in Chennai, India. The establishment of the Indian plant in 1998 marked an important attempt by HMC to ‘relaunch’ its globalisation strategy after an earlier failure to manufacture in Canada. The ability of HMC to adopt an appropriate employment relations strategy in India will be an important factor determining its success as a global manufacturer. A key issue facing the Hyundai Motor Company of India (HMI) is the role to be accorded to unions and employee representation in the plant.

Recent research has highlighted a tendency for more rational and deliberative decision-making in individuals with autism. We tested this hypothesis by using eye-tracking to investigate the information processing strategies that underpin multi-attribute choice in a sample of adults diagnosed with autism spectrum condition. We found that, as the number of attributes defining each option increased, autistic decision-makers were speedier, examined less of the available information, and spent a greater proportion of their time examining the option they eventually chose. Rather than indicating a more deliberative style, our results are consistent with a tendency for individuals with autism to narrow down the decision-space more quickly than does the neurotypical population.

Recent research has shown that risk and reward are positively correlated in many environments, and that people have internalized this association as a “risk-reward heuristic”: when making choices based on incomplete information, people infer probabilities from payoffs and vice-versa, and these inferences shape their decisions. We extend this work by examining people’s expectations about another fundamental trade-off — that between monetary reward and delay. In 2 experiments (total N = 670), we adapted a paradigm previously used to demonstrate the risk-reward heuristic. We presented participants with intertemporal choice tasks in which either the delayed reward or the length of the delay was obscured. Participants inferred larger rewards for longer stated delays, and longer delays for larger stated rewards; these inferences also predicted people’s willingness to take the delayed option. In exploratory analyses, we found that older participants inferred longer delays and smaller rewards than did younger ones. All of these results replicated in 2 large-scale pre-registered studies with participants from a different population (total N = 2138). Our results suggest that people expect intertemporal choice tasks to offer a trade-off between delay and reward, and differ in their expectations about this trade-off. This “delay-reward heuristic” offers a new perspective on existing models of intertemporal choice and provides new insights into unexplained and systematic individual differences in the willingness to delay gratification.

When choosing between immediate and future rewards, how do people deal with uncertainty about the value of the future outcome or the delay until its occurrence? Skylark et al. (2020) suggested that people employ a delay-reward heuristic: the inferred value of an ambiguous future reward is a function of the stated delay, and vice-versa. The present paper investigates the role of this heuristic in choice behaviour. In Studies 1a–2b, participants inferred the value of an ambiguous future reward or delay before the true value was revealed and a choice made. Preference for the future option was predicted by the discrepancy between the estimated and true values: the more pleasantly surprising the delayed option, the greater the willingness to choose it. Studies 3a–3c examined the association between inference and preference when the ambiguous values remained unknown. As predicted by the use of a delay-reward heuristic, inferred delays and rewards were positively related to stated rewards and delays, respectively. More importantly, choices were associated with inferred rewards and, in some circumstances, delays. Critically, estimates and choices were both order-dependent: when estimates preceded choices, estimates were more optimistic (people inferred smaller delays and larger rewards) and were subsequently more likely to choose the delayed option than when choices were made before estimates. These order effects argue against a simple model in which people deal with ambiguity by first estimating the unknown value and then using their estimate as the basis for decision. Rather, it seems that inferences are partly constructed from choices, and the role of inference in choice depends on whether an explicit estimate is made prior to choosing. Finally, we also find that inferences about ambiguous delays depend on whether the estimate has to be made in “days” or in a self-selected temporal unit, and replicate previous findings that older participants make more pessimistic inferences than younger ones. We discuss the implications and possible mechanisms for these findings.

When one or several classes are much less prevalent than another class (unbalanced data), class error rates and variable importances of the machine learning algorithm random forest can be biased, particularly when sample sizes are smaller, imbalance levels higher, and effect sizes of important variables smaller. Using simulated data varying in size, imbalance level, number of true variables, their effect sizes, and the strength of multicollinearity between covariates, we evaluated how eight versions of random forest ranked and selected true variables out of a large number of covariates despite class imbalance. The version that calculated variable importance based on the area under the curve (AUC) was least adversely affected by class imbalance. For the same number of true variables, effect sizes, and multicollinearity between covariates, the AUC variable importance ranked true variables still highly at the lower sample sizes and higher imbalance levels at which the other seven versions no longer achieved high ranks for true variables. Conversely, using the Hellinger distance to split trees or downsampling the majority class already ranked true variables lower and more variably at the larger sample sizes and lower imbalance levels at which the other algorithms still ranked true variables highly. In variable selection, a higher proportion of true variables were identified when covariates were ranked by AUC importances and the proportion increased further when the AUC was used as the criterion in forward variable selection. In three case studies, known species–habitat relationships and their spatial scales were identified despite unbalanced data.

In a stably stratified shear layer, multiple competing instabilities produce sensitivity to small changes in initial conditions, popularly called the butterfly effect (as a flapping wing may alter the weather). Three ensembles of 15 simulated mixing events, identical but for small perturbations to the initial state, are used to explore differences in the route to turbulence, the maximum turbulence level and the total amount and efficiency of mixing accomplished by each event. Comparisons show that a small change in the initial state alters the strength and timing of the primary Kelvin–Helmholtz instability, the subharmonic pairing instability and the various three-dimensional secondary instabilities that lead to turbulence. The effect is greatest in, but not limited to, the parameter regime where pairing and the three-dimensional secondary instabilities are in strong competition. Pairing may be accelerated or prevented; maximum turbulence kinetic energy may vary by up to a factor of 4.6, flux Richardson number by 12 %–15 % and net mixing by a factor of 2.