Mean (thicker solid lines) and mean plus/minus one standard deviation (thinner dashed lines) of cumulative meta-analytic effect as a function of the number of publications.

<p>The black lines represent the situation where <i>E_obs</i> is tested with respect to 0. The red lines represent the situation when ignoring the 3 latest publications for determining <i>E_meta</i>. The means and standard deviations are calculated across the 5,000 repetitions of the simulation.</p

Mean (thicker solid lines) and mean plus/minus one standard deviation (thinner dashed lines) of the cumulative meta-analytic effect (<i>E_meta</i>) as a function of the number of publications.

<p>The means and standard deviations are calculated across the 5,000 repetitions of the simulation.</p

Mean (thicker solid lines) and mean plus/minus one standard deviation (thinner dashed lines) of the cumulative meta-analytic effect as a function of the number of studies.

<p>Note that the number of studies can vary per repetition because the simulation was terminated when 40 publications were done under the Selective Publication Approach. Only studies having more than 4,500 out of 5,000 <i>E_meta</i> values available are shown (i.e., study numbers 3–585). The means and standard deviations are calculated across the repetitions of the simulation.</p

Mean number of studies until publication as a function of the number of publications.

<p>The means are calculated across the 5,000 repetitions of the simulation.</p

Illustration of Proteus phenemonen from Ioannidis and Trikalinos [27].

<p>(Reprinted from Journal of Clinical Epidemiology, Vol. 58, J. P. Ioannidis and T. A. Trikalinos, Early extreme contradictory estimates may appear in published research: The Proteus phenomenon in molecular genetics research and randomized trials, pp. 543–549, 2005, with permission from Elsevier.) The figure shows odds ratios and 95% confidence intervals of “the relationship between the methylenetetrahydrofolate reductase (MTHFR) TT genotype in the mother and the risk of neural tube defects in the child”. The study with the strongest effect is shown by a square symbol and the study with the smallest effect is shown by a triangular symbol. The white line represents the summary odds ratio. The shaded area represents the 95% confidence interval of the summary odds ratio.</p

Mean (thicker solid lines) and mean plus/minus one standard deviation (thinner dashed lines) of observed effects of individual studies (<i>E_obs,pub</i>) as a function of the number of publications.

<p>The means and standard deviations are calculated across the 5,000 repetitions of the simulation.</p

Data_Sheet_1_Neck stabilization through sensory integration of vestibular and visual motion cues.pdf

BackgroundTo counteract gravity, trunk motion, and other perturbations, the human head–neck system requires continuous muscular stabilization. In this study, we combine a musculoskeletal neck model with models of sensory integration (SI) to unravel the role of vestibular, visual, and muscle sensory cues in head–neck stabilization and relate SI conflicts and postural instability to motion sickness.MethodA 3D multisegment neck model with 258 Hill-type muscle elements was extended with postural stabilization using SI of vestibular (semicircular and otolith) and visual (rotation rate, verticality, and yaw) cues using the multisensory observer model (MSOM) and the subjective vertical conflict model (SVC). Dynamic head–neck stabilization was studied using empirical datasets, including 6D trunk perturbations and a 4 m/s2 slalom drive inducing motion sickness.ResultsRecorded head translation and rotation are well matched when using all feedback loops with MSOM or SVC or assuming perfect perception. A basic version of the model, including muscle, but omitting vestibular and visual perception, shows that muscular feedback can stabilize the neck in all conditions. However, this model predicts excessive head rotations in conditions with trunk rotation and in the slalom. Adding feedback of head rotational velocity sensed by the semicircular canals effectively reduces head rotations at mid-frequencies. Realistic head rotations at low frequencies are obtained by adding vestibular and visual feedback of head rotation based on the MSOM or SVC model or assuming perfect perception. The MSOM with full vision well captures all conditions, whereas the MSOM excluding vision well captures all conditions without vision. The SVC provides two estimates of verticality, with a vestibular estimate SVCvest, which is highly effective in controlling head verticality, and an integrated vestibular/visual estimate SVCint which can complement SVCvest in conditions with vision. As expected, in the sickening drive, SI models imprecisely estimate verticality, resulting in sensory conflict and postural instability.ConclusionThe results support the validity of SI models in postural stabilization, where both MSOM and SVC provide credible results. The results in the sickening drive show imprecise sensory integration to enlarge head motion. This uniquely links the sensory conflict theory and the postural instability theory in motion sickness causation.</p

The effect of haptic feedback in the balance of a bicycle

Roll_data_perturbation folder includes the data of all 20 participants.  The signals are organized in structures and contain both pure measurement data (labeled data) and the outputs of the Finite Impulse Response Model (labeled black_box).</p

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Automated vehicle acceptance (AVA) is a necessary condition for the realisation of higher-level objectives such as improvements in road safety, reductions in traffic congestion and environmental pollution. On the basis of a systematic literature review of 124 empirical studies, the present study proposes MAVA, a multi-level model to predict AVA. It incorporates a process-oriented view on AVA, considering acceptance as the result of a four-stage decision-making process that ranges from the exposure of the individual to automated vehicles (AVs) in Stage 1, the formation of favourable or unfavourable attitudes towards AVs in Stage 2, making the decision to adopt or reject AVs in Stage 3, to the implementation of AVs into practice in Stage 4. MAVA incorporates 28 acceptance factors that represent seven main acceptance classes. The acceptance factors are located at two levels, i.e., micro and meso. Factors at the micro-level constitute individual difference factors (i.e., socio-demographics, personality and travel behaviour). The meso-level captures the exposure of individuals to AVs, instrumental domain-specific, symbolic-affective and moral-normative factors of AVA. The literature review revealed that 6% of the studies investigated the exposure of individuals to AVs (i.e., knowledge and experience). 22% of the studies investigated domain-specific factors (i.e., performance and effort ­expectancy, safety, facilitating conditions, and service and vehicle ­characteristics), 4% symbolic-affective factors (i.e., hedonic motivation and social influence), and 12% moral-normative factors (i.e., perceived benefits and risks). Factors related to a person’s socio-demographic profile, travel behaviour and personality were investigated by 28%, 15% and 14% of the studies, respectively. We recommend that future studies empirically verify MAVA using longitudinal or experimental studies.</p

Structural model a and b.

Structural model a and b.</p