A new method for automated reconstruction of pre-transformation microstructures

A new method, referred to as the cluster method, is proposed and tested for the reconstruction of pre-transformation microstructure in Ti–6Al–4V alloy. The cluster method begins by constructing clusters of daughter grains (room-temperature hcp α-phase) that have a high probability of being transformed from the same parent grain (high-temperature bcc β-phase). These clusters are then grown, and back-transformed to the parent grain orientations using the ‘summation of mutual misorientation angle method’ (SMMA method). The cluster method is validated by comparing the obtained results with the results of the SMMA and triplet methods.     

Children's creation of algorithms: simulations and gestures

Experiments showed that children are able to create algorithms, that is, sequences of operations that solve problems, and that their gestures help them to do so. The theory of mental models, which is implemented in a computer program, postulates that the creation of algorithms depends on mental simulations that unfold in time. Gestures are outward signs of moves and they help the process. We tested 10-year-old children, because they can plan, and because they gesture more than adults. They were able to rearrange the order of 6 cars in a train (using a siding), and the difficulty of the task depended on the number of moves in minimal solutions (Experiment 1). They were also able to devise informal algorithms to rearrange the order of cars when they were not allowed to move the cars, and the difficulty of the task depended on the complexity of the algorithms (Experiment 2). When children were prevented from gesturing as they formulated algorithms, the accuracy of their algorithms declined by13% (Experiment 3). We discuss the implications of these results.     

Does COVID-19 impact the frequency of threatening events in dreams? An exploration of pandemic dreaming in light of contemporary dream theories

Ninety-one dreams collected during the Covid-19 pandemic (the epidemic-situation sample) were compared with ninety-one dreams collected before the start of the epidemic (the non-epidemic-situation sample). The dreams were classified according to their content, using methods based on previous studies. The frequency of themes was compared to predictions that would be anticipated by three contemporary theories of dreaming: 1) threat simulation theory (TST); 2) incorporation continuity hypothesis (ICH); and 3) social simulation theory (SST). The epidemic-situation sample dreamed more of threatening events than the non-epidemic-situation sample (supporting the TST) and more of non-aggression threatening events, possibly due to the hyperassociation during sleep. However, the epidemic-situation sample did not show a greater prevalence of illness events in dreams (not supporting the ICH). Additionally, there was no significant difference in social neutral and positive events in dreams between the two samples as would have been predicted by the SST.     

An adaptive temporal-causal network model to analyse extinction of communication over time

The persistence of information communicated between humans is difficult to measure as it is affected by many features. This paper presents an approach to computationally model the cognitive processes of information sharing to describe persistence or extinction of communication in Twitter over time. The adaptive mental network model explains, for example, how an individual can experience information overflow on a topic, and how this affects the sharing of information. Parameter tuning by Simulated Annealing is used to identify characteristics of the network model that fit to empirical data from Twitter. The data collected is related to the independentism in Catalunya, Spain, which is considered a global issue with repercussion in Europe.     

Mapping drivers’ mental models of adaptive cruise control to performance

Driver support features (DSF) have the potential to improve safety, but they also change the driver-vehicle relationship —as well as their respective roles and responsibilities. To maximize safety, it is important to understand how drivers’ knowledge and understanding of these technologies—referred to as drivers’ mental models—impact performance and safety. This simulator study examined how drivers with different mental models of adaptive cruise control performed in edge cases. The study compared the responses of groups of drivers, with strong and weak mental models of ACC, established through a combination of screening, training, and exposure, in edge case situations in a high-fidelity driving simulator. In general, participants with strong mental models were faster than those with weak mental models to respond in edge-case situations—defined as cases where the ACC did not detect an approaching object, such as a slow-moving motorcycle. The performance deficits observed for drivers with weak mental models appear to reflect uncertainty surrounding how ACC will behave in edge cases.     

Influence of the median opening length on driving behaviors in the crossover work zone—A driving simulation study

Work zone safety has been a major concern for drivers and transportation agencies. Considering the severe consequences of crossover work zone crashes coupled with the limited literature on the median opening length, this paper aims to investigate the influence of the median opening length on driving behaviors from a behavioral perspective in crossover work zones. A driving simulation study with 32 participants was carried out to investigate the driving performance by metrics (speed, acceleration, maximum steering wheel speed, and lane-changing trajectory) with respect to five median opening lengths. The significant differences of each driving performance metric among various median opening lengths were found in the process of crossing through the median opening on the entrance by-pass and exit by-pass. The results indicated that drivers’ speeds were always higher than the speed limits for all median opening lengths. A narrower median opening may induce risky driving behaviors such as sharp deceleration and decelerating while lane-changing, which may increase speed variance. When crossing through the median opening with a larger length, high speed variance, sluggish lane-changing operations, and improper lane-using behaviors were observed because the excessive lane-changing space provided by the larger median opening length would decrease the situation awareness and increase the speed preference heterogeneity among drivers. Finally, some traffic management strategies were proposed, which can improve the safety and mobility of crossover work zones and further provide a basis for the formulation of related standards.     

Egocentric metric representations in peripersonal space: A bridge between motor resources and spatial memory

Research on visuospatial memory has shown that egocentric (subject-to-object) and allocentric (object-to-object) reference frames are connected to categorical (non-metric) and coordinate (metric) spatial relations, and that motor resources are recruited especially when processing spatial information in peripersonal (within arm reaching) than extrapersonal (outside arm reaching) space. In order to perform our daily-life activities, these spatial components cooperate along a continuum from recognition-related (e.g., recognizing stimuli) to action-related (e.g., reaching stimuli) purposes. Therefore, it is possible that some types of spatial representations rely more on action/motor processes than others. Here, we explored the role of motor resources in the combinations of these visuospatial memory components. A motor interference paradigm was adopted in which participants had their arms bent behind their back or free during a spatial memory task. This task consisted in memorizing triads of objects and then verbally judging what was the object: (1) closest to/farthest from the participant (egocentric coordinate); (2) to the right/left of the participant (egocentric categorical); (3) closest to/farthest from a target object (allocentric coordinate); and (4) on the right/left of a target object (allocentric categorical). The triads appeared in participants' peripersonal (Experiment 1) or extrapersonal (Experiment 2) space. The results of Experiment 1 showed that motor interference selectively damaged egocentric-coordinate judgements but not the other spatial combinations. The results of Experiment 2 showed that the interference effect disappeared when the objects were in the extrapersonal space. A third follow-up study using a within-subject design confirmed the overall pattern of results. Our findings provide evidence that motor resources play an important role in the combination of coordinate spatial relations and egocentric representations in peripersonal space.     

From segregation to intergroup contact and back: Using experiments and simulation to understand the bidirectional link

Research on intergroup contact has mostly viewed desegregation as a necessary condition for contact to unfold its power to reduce prejudice. Through residential and school choices, however, prejudice also contributes to segregation. To shed light on this bidirectional link, we conducted two survey‐based experiments with stratified quota samples of German adults. In Study 1, respondents with less contact and more prejudice indicated a lower likelihood of renting an apartment in a neighborhood with a larger proportion of minority members, although housing quality and crime rate were held constant. In Study 2, similar results were obtained for the likelihood of enrolling their child at a school with a larger proportion of minority students. Building on these results in a computer simulation, we find that because contact only reduces prejudice, but does not produce pro‐minority preferences, spontaneous desegregation is unlikely to occur even under the most favorable structural and economic conditions.     

Computing the real solutions of Fleishman's equations for simulating non-normal data

Fleishman's power method is frequently used to simulate non-normal data with a desired skewness and kurtosis. Fleishman's method requires solving a system of nonlinear equations to find the third-order polynomial weights that transform a standard normal variable into a non-normal variable with desired moments. Most users of the power method seem unaware that Fleishman's equations have multiple solutions for typical combinations of skewness and kurtosis. Furthermore, researchers lack a simple method for exploring the multiple solutions of Fleishman's equations, so most applications only consider a single solution. In this paper, we propose novel methods for finding all real-valued solutions of Fleishman's equations. Additionally, we characterize the solutions in terms of differences in higher order moments. Our theoretical analysis of the power method reveals that there typically exists two solutions of Fleishman's equations that have noteworthy differences in higher order moments. Using simulated examples, we demonstrate that these differences can have remarkable effects on the shape of the non-normal distribution, as well as the sampling distributions of statistics calculated from the data. Some considerations for choosing a solution are discussed, and some recommendations for improved reporting standards are provided.     

Bio-inspired computational object classification model for object recognition

Human beings can effortlessly perceive stimuli through their sensory systems to learn, understand, recognize and act on our environment or context. Over the years, efforts have been made to enable cybernetic entities to be close to performing human perception tasks; and in general, to bring artificial intelligence closer to human intelligence.Neuroscience and other cognitive sciences provide evidence and explanations of the functioning of certain aspects of visual perception in the human brain. Visual perception is a complex process, and its has been divided into several parts. Object classification is one of those parts; it is necessary for carrying out the declarative interpretation of the environment. This article deals with the object classification problem.In this article, we propose a computational model of visual classification of objects based on neuroscience, it consists of two modular systems: a visual processing system, in charge of the extraction of characteristics; and a perception sub-system, which performs the classification of objects based on the features extracted by the visual processing system.With the results obtained, a set of aspects are analyzed using similarity and dissimilarity matrices. Also based on the neuroscientific evidence and the results obtained from this research, some aspects are suggested for consideration to improve the work in the future and bring us closer to performing the task of visual classification as humans do.