A First Step toward the Understanding of Implicit Learning of Hazard Anticipation in Inexperienced Road Users Through a Moped-Riding Simulator

Hazard perception is considered one of the most important abilities in road safety. Several efforts have been devoted to investigating how it improves with experience and can be trained. Recently, research has focused on the implicit aspects of hazard detection, reaction, and anticipation. In the present study, we attempted to understand how the ability to anticipate hazards develops during training with a moped-riding simulator: the Honda Riding Trainer (HRT). Several studies have already validated the HRT as a tool to enhance adolescents\u2019 hazard perception and riding abilities. In the present study, as an index of hazard anticipation, we used skin conductance response (SCR), which has been demonstrated to be linked to affective/implicit appraisal of risk. We administered to a group of inexperienced road users five road courses two times a week apart. In each course, participants had to deal with eight hazard scenes (except one course that included only seven hazard scenes). Participants had to ride along the HRT courses, facing the potentially hazardous situations, following traffic rules, and trying to avoid accidents. During the task, we measured SCR and monitored driving performance. The main results show that learning to ride the simulator leads to both a reduction in the number of accidents and anticipation of the somatic response related to hazard detection, as proven by the reduction of SCR onset recorded in the second session. The finding that the SCR signaling the impending hazard appears earlier when the already encountered hazard situations are faced anew suggests that training with the simulator acts on the somatic activation associated with the experience of risky situations, improving its effectiveness in detecting hazards in advance so as to avoid accidents. This represents the starting point for future investigations into the process of generalization of learning acquired in new virtual situations and in real-road situations

Empirical Scenarios of Fake Data Analysis: The Sample Generation by Replacement (SGR) Approach

Many self-report measures of attitudes, beliefs, personality, and pathology include items whose responses can be easily manipulated or distorted, as an example in order to give a positive impression to others, to obtain financial compensation, to avoid being charged with a crime, to get a job, or else. This fact confronts both researchers and practitioners with the crucial problem of biases yielded by the usage of standard statistical models. The current paper presents three empirical applications to the issue of faking of a recent probabilistic perturbation procedure called Sample Generation by Replacement (SGR; Lombardi and Pastore, 2012). With the intent to study the behavior of some statistics under fake perturbation and data reconstruction processes, ad-hoc faking scenarios were implemented and tested. Overall, results proved that SGR could be successfully applied both in the case of research designs traditionally proposed in order to deal with faking (e.g., use of fake-detecting scales, experimentally induced faking, or contrasting applicants vs. incumbents), and in the case of ecological research settings, where no information as regards faking could be collected by the researcher or the practitioner. Implications and limitations are presented and discussed

Integrated game-theory modelling for multi enterprise-wide coordination and collaboration under uncertain competitive environment

In this work, an integrated Game Theory (GT) approach is developed for the coordination of multi-enterprise Supply Chains (SCs) in a competitive uncertain environment. The conflicting goals of the different participants are solved through coordination contracts using a non-cooperative non-zero-sum Stackelberg game under the leadership of the manufacturer. The Stackelberg payoff matrix is built under the nominal conditions, and then evaluated under different probable uncertain scenarios using a Monte-Carlo simulation. The competition between the Stackelberg game players and the third parties is solved through a Nash Equilibrium game. A novel way to analyze the game outcome is proposed based on a win–win Stackelberg set of “Pareto-frontiers”. The benefits of the resulting MINLP tactical models are illustrated by a case study with different vendors around a client SC. The results show that the coordinated decisions lead to higher expected payoffs compared to the standalone case, while also leading to uncertainty reduction.Peer ReviewedPostprint (author's final draft

A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models

Best Performance Frontiers for Buy-Online-Pickup-in-Store order fulfilment

With the proliferation of omni-channel retailing, Buy-Online-Pickup-in-Store (BOPS) retail services have gained increasing popularity as they have benefits for both customers and retailers. However, using conventional retail stores to fulfil orders received online whilst also serving walk-in customers is challenging for retailers, particularly when a high customer service level is promised to online customers (e.g., order by a certain time and pick up in store after a specific time later the same day). This paper examines store picking operations for same day BOPS services. Specifically, we derive Best Performance Frontiers (BPFs) for single wave and multi-wave order picking. New relationships, propositions, and results are presented to determine the minimum picking rate needed in stores to guarantee a target service level, the number of picking waves a retailer should launch in an ordering cycle, and the timing of picking waves. We also examine demand surge scenarios with different order arrival rates in an ordering cycle. Insights and implications of the results are discussed for retailers that seek to benchmark their current BOPS performances and understand how to schedule and improve the picking of online orders in conventional retail stores and the picking rates needed to guarantee a desired service level for online customers

Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data

Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients – basic parameters required for implementing the MIIA – are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite

Situating emotional experience

Psychological construction approaches to emotion suggest that emotional experience is situated and dynamic. Fear, for example, is typically studied in a physical danger context (e.g., threatening snake), but in the real world, it often occurs in social contexts, especially those involving social evaluation (e.g., public speaking). Understanding situated emotional experience is critical because adaptive responding is guided by situational context (e.g., inferring the intention of another in a social evaluation situation vs. monitoring the environment in a physical danger situation). In an fMRI study, we assessed situated emotional experience using a newly developed paradigm in which participants vividly imagine different scenarios from a first-person perspective, in this case scenarios involving either social evaluation or physical danger. We hypothesized that distributed neural patterns would underlie immersion in social evaluation and physical danger situations, with shared activity patterns across both situations in multiple sensory modalities and in circuitry involved in integrating salient sensory information, and with unique activity patterns for each situation type in coordinated large-scale networks that reflect situated responding. More specifically, we predicted that networks underlying the social inference and mentalizing involved in responding to a social threat (in regions that make up the “default mode” network) would be reliably more active during social evaluation situations. In contrast, networks underlying the visuospatial attention and action planning involved in responding to a physical threat would be reliably more active during physical danger situations. The results supported these hypotheses. In line with emerging psychological construction approaches, the findings suggest that coordinated brain networks offer a systematic way to interpret the distributed patterns that underlie the diverse situational contexts characterizing emotional life

Language Practice and Study Abroad

The present study measures the impact that pragmatic intervention has when students are exposed to targeted language practice during a six-week study abroad programme in Buenos Aires, Argentina. The intervention had three goals. First, the researcher drew learners’ attention to language use and context through discussion about pragmatics and exposure to authentic input. Second, the intervention aimed at making students aware of the pragmatic norms of the target culture, including the appropriate use of communication strategies. The third and final goal was to afford the participants opportunities to engage in what DeKeyser (2007) and others argue are five critical aspects of language practice during study abroad: input, output, interaction, guided reflection and targeted feedback. Results indicated that over time all six students increased their use of targetlike request strategies. Journal entries and interviews with the researcher also revealed that the students became more aware of appropriate target-like request behaviour as a result of the language practice. In their journals and interviews, the students also attributed their pragmatic development to three additional sources: interactions with host families and other native speakers, their participation in service encounter exchanges and the targeted feedback given to them by the researcher. The results suggest that exposure to targeted language practice prior to and during study abroad can facilitate pragmatic learning and, in turn, contribute to a more successful study abroad experience

A learning tool to develop sustainable projects

This paper presents a tool developed to help engineers to design and develop sustainable projects. The tool has been designed to introduce and evaluate the sustainability of engineering projects in general, but here we show its application to assess the final project of an engineering degree. This tool is a guide for students to introduce and estimate the sustainability of their projects, but it also helps teachers to assess them. The tool is based on the Socratic Methodology and consists of a matrix where each cell contains several questions that students must consider during the project development and which they must answer in their project report. A positive or negative mark is assigned to every cell, and the sum of all marks states the project sustainability. However, the result is not as simplistic as a final number, but a descriptive sustainability analysis where questions are answered and every mark justified. A pilot test with some students has obtained good results, but the first Final Degree Project using this methodology will be read in July 2016.Peer ReviewedPostprint (author's final draft