Computational Models for Workload Analysis of Driving Tasks

2021 ◽  
Vol 65 (1) ◽  
pp. 690-694
Author(s):  
Holly Handley ◽  
Deborah Thompson
Keyword(s):  
Pilot Study ◽  
Computational Model ◽  
Secondary Task ◽  
Computational Models ◽  
Workload Analysis ◽  
Trade Offs ◽  
Driving Scenario ◽  
Experimental Findings

This paper describes a methodology to design computational models to evaluate the workload for driving tasks. A computational model was configured for a driving scenario used in a pilot study that included a secondary task at varying levels of difficulty to increase the driver’s workload. The computational model results provided a workload analysis of the concurrent driving tasks. This analysis can be used to explain the experimental findings from subject experiments and to evaluate the workload trade-offs between primary and secondary driving tasks.

scholarly journals The rebound response plays a role in the motion mechanisms and perception

2020 ◽  
Author(s):  
Hadar Cohen-Duwek ◽  
Hedva Spitzer
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
Human Subjects ◽  
Neuronal Response ◽  
Psychophysical Experiment ◽  
Electrophysiological Studies ◽  
Theoretical Predictions ◽  
Electrophysiological Findings ◽  
On And Off Pathways ◽  
Experimental Findings

abstractMotion estimation is an essential ability for sighted animals to survive in their natural environment. Many anatomical and electrophysiological studies on low visual levels have been based on the classic pioneering HRC (Hassenstein & Reichaedt Correlator) computational model. The accumulated experimental findings, which have given rise to a debate in the current computational models regarding the interaction between the On and Off pathways. The previous algorithms were challenged to correctly predict physiological experiment results and the two types of motion: a) Phi motion, also termed apparent motion. b) Reverse-phi motion that is perceived when the image contrast flips during the rapid succession. We have developed a computational model supported by simulations, which for the first time leads to correct predictions of the behavioral motions (phi and reverse-phi), while considering separated On and Off pathways and is also in agreement with the relevant electrophysiological findings. This has been achieved through the well-known neuronal response: the rebound response or “Off response”. We suggest that the rebound response, which has not been taken into account in the previous models, is a key player in the motion mechanism, and its existence requires separation between the On and the Off pathways for correct motion interpretation. We furthermore suggest that the criterial reverse-phi effect is only an epiphenomenon of the rebound response for the visual system. The theoretical predictions are confirmed by a psychophysical experiment on human subjects. Our findings shed new light on the comprehensive role of the rebound response as a parsimonious spatiotemporal detector for motion and additional memory tasks, such as for stabilization and navigation.

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Lichao Zhang ◽  
Zihong Huang ◽  
Liang Kong
Keyword(s):  
Computational Model ◽  
Binding Sites ◽  
Noncoding Rna ◽  
Computational Models ◽  
Information Sources ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gradient Boosting ◽  
Position Information ◽  
Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

scholarly journals Numerical and Experimental Investigations on Vocal Fold Approximation in Healthy and Simulated Unilateral Vocal Fold Paralysis

2021 ◽  
Vol 11 (4) ◽  
pp. 1817
Author(s):  
Zheng Li ◽  
Azure Wilson ◽  
Lea Sayce ◽  
Amit Avhad ◽  
Bernard Rousseau ◽  
...  
Keyword(s):  
Computational Model ◽  
Vocal Fold ◽  
High Speed ◽  
Computational Models ◽  
Ex Vivo ◽  
Vocal Fold Paralysis ◽  
Future Application ◽  
Experimental Investigations ◽  
Type I ◽  
Mri Scans

We have developed a novel surgical/computational model for the investigation of unilat-eral vocal fold paralysis (UVFP) which will be used to inform future in silico approaches to improve surgical outcomes in type I thyroplasty. Healthy phonation (HP) was achieved using cricothyroid suture approximation on both sides of the larynx to generate symmetrical vocal fold closure. Following high-speed videoendoscopy (HSV) capture, sutures on the right side of the larynx were removed, partially releasing tension unilaterally and generating asymmetric vocal fold closure characteristic of UVFP (sUVFP condition). HSV revealed symmetric vibration in HP, while in sUVFP the sutured side demonstrated a higher frequency (10–11%). For the computational model, ex vivo magnetic resonance imaging (MRI) scans were captured at three configurations: non-approximated (NA), HP, and sUVFP. A finite-element method (FEM) model was built, in which cartilage displacements from the MRI images were used to prescribe the adduction, and the vocal fold deformation was simulated before the eigenmode calculation. The results showed that the frequency comparison between the two sides was consistent with observations from HSV. This alignment between the surgical and computational models supports the future application of these methods for the investigation of treatment for UVFP.

scholarly journals Optimal Flow—A Pilot Study Balancing Sheep Movement and Welfare in Abattoirs

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 344
Author(s):  
Melissa J. Starling ◽  
Elyssa Payne ◽  
Paul McGreevy
Keyword(s):  
Pilot Study ◽  
Additional Source ◽  
Movement Rate ◽  
Optimal Outcomes ◽  
Optimal Flow ◽  
Livestock Movement ◽  
Trade Offs ◽  
Behavioural Interactions ◽  
Handling Practices ◽  
Rate Of Movement

Abattoirs are faced with the challenge of moving livestock efficiently through the plant, while also engaging in handling practices that assure good animal welfare. Achieving optimal outcomes for both of these goals can bring them into conflict. An additional source of conflict can arise from the design of the abattoir. These problems are compounded by the dearth of research available to inform how livestock should be handled to achieve all of these goals. We applied the concept of ‘Optimal Flow’ to describe conditions under which rate of movement is maximised while overt signs of distress in sheep are minimised. Effectively, this represents the point at which trade-offs between speed and welfare converge. The current pilot study examined the behavioural interactions between humans (n = 5), livestock herding dogs (n = 7), and sheep (n = 3235) in a large Australian abattoir to describe the factors associated with an increase or decrease in rate of sheep movement per minute. It revealed that distress behaviours in sheep were associated with dog presence and with a decrease in livestock movement rate. However, we found that as sheep density increased, there was increased livestock movement rate as well as an elevated incidence of distress behaviours. Optimal Flow at this abattoir was achieved by maintaining sheep at lower densities. Our report discusses the possible confounds in this interpretation.

scholarly journals Interacting roles of lateral and medial Orbitofrontal cortex in decision-making and learning : A system-level computational model

2019 ◽  
Author(s):  
Bhargav Teja Nallapu ◽  
Frédéric Alexandre
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Computational Model ◽  
Orbitofrontal Cortex ◽  
Temporal Dynamics ◽  
System Level ◽  
Interacting Networks ◽  
Cortical Regions ◽  
Experimental Findings

AbstractIn the context of flexible and adaptive animal behavior, the orbitofrontal cortex (OFC) is found to be one of the crucial regions in the prefrontal cortex (PFC) influencing the downstream processes of decision-making and learning in the sub-cortical regions. Although OFC has been implicated to be important in a variety of related behavioral processes, the exact mechanisms are unclear, through which the OFC encodes or processes information related to decision-making and learning. Here, we propose a systems-level view of the OFC, positioning it at the nexus of sub-cortical systems and other prefrontal regions. Particularly we focus on one of the most recent implications of neuroscientific evidences regarding the OFC - possible functional dissociation between two of its sub-regions : lateral and medial. We present a system-level computational model of decision-making and learning involving the two sub-regions taking into account their individual roles as commonly implicated in neuroscientific studies. We emphasize on the role of the interactions between the sub-regions within the OFC as well as the role of other sub-cortical structures which form a network with them. We leverage well-known computational architecture of thalamo-cortical basal ganglia loops, accounting for recent experimental findings on monkeys with lateral and medial OFC lesions, performing a 3-arm bandit task. First we replicate the seemingly dissociate effects of lesions to lateral and medial OFC during decision-making as a function of value-difference of the presented options. Further we demonstrate and argue that such an effect is not necessarily due to the dissociate roles of both the subregions, but rather a result of complex temporal dynamics between the interacting networks in which they are involved.Author summaryWe first highlight the role of the Orbitofrontal Cortex (OFC) in value-based decision making and goal-directed behavior in primates. We establish the position of OFC at the intersection of cortical mechanisms and thalamo-basal ganglial circuits. In order to understand possible mechanisms through which the OFC exerts emotional control over behavior, among several other possibilities, we consider the case of dissociate roles of two of its topographical subregions - lateral and medial parts of OFC. We gather predominant roles of each of these sub-regions as suggested by numerous experimental evidences in the form of a system-level computational model that is based on existing neuronal architectures. We argue that besides possible dissociation, there could be possible interaction of these sub-regions within themselves and through other sub-cortical structures, in distinct mechanisms of choice and learning. The computational framework described accounts for experimental data and can be extended to more comprehensive detail of representations required to understand the processes of decision-making, learning and the role of OFC and subsequently the regions of prefrontal cortex in general.

scholarly journals The Exponential-Weight Mean-Variance Model: A novel computational model for the Balloon Analogue Risk Task

2019 ◽  
Author(s):  
Harhim Park ◽  
Jaeyeong Yang ◽  
Jasmin Vassileva ◽  
Woo-Young Ahn
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
Model Comparison ◽  
Exponential Weight ◽  
Multiple Model ◽  
Variance Model ◽  
Balloon Analogue Risk Task ◽  
Parameter Recovery ◽  
Post Hoc ◽  
Mean Variance

The Balloon Analogue Risk Task (BART) is a popular task used to measure risk-taking behavior. To identify cognitive processes associated with choice behavior on the BART, a few computational models have been proposed. However, the extant models are either too simplistic or fail to show good parameter recovery performance. Here, we propose a novel computational model, the exponential-weight mean-variance (EWMV) model, which addresses the limitations of existing models. By using multiple model comparison methods, including post hoc model fits criterion and parameter recovery, we showed that the EWMV model outperforms the existing models. In addition, we applied the EWMV model to BART data from healthy controls and substance-using populations (patients with past opiate and stimulant dependence). The results suggest that (1) the EWMV model addresses the limitations of existing models and (2) heroin-dependent individuals show reduced risk preference than other groups in the BART.

A Mechanistic Motor-Clutch Model That Explains Cell Shape Dynamics to Cyclic Stretch

2022 ◽  
Author(s):  
Benjamin W. Scandling ◽  
Jia Gou ◽  
Jessica Thomas ◽  
Jacqueline Xuan ◽  
Chuan Xue ◽  
...  
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
The Body ◽  
Cyclic Stretch ◽  
Substrate Stiffness ◽  
Cell Alignment ◽  
Cellular Mechanisms ◽  
Actin Bundles ◽  
The Impact

Many cells in the body experience cyclic mechanical loading, which can impact cellular processes and morphology. In vitro studies often report that cells reorient in response to cyclic stretch of their substrate. To explore cellular mechanisms involved in this reorientation, a computational model was developed by utilizing the previous computational models of the actin-myosin-integrin motor-clutch system developed by others. The computational model predicts that under most conditions, actin bundles align perpendicular to the direction of applied cyclic stretch, but under specific conditions, such as low substrate stiffness, actin bundles align parallel to the direction of stretch. The model also predicts that stretch frequency impacts the rate of reorientation, and that proper myosin function is critical in the reorientation response. These computational predictions are consistent with reports from the literature and new experimental results presented here. The model suggests that the impact of different stretching conditions (stretch type, amplitude, frequency, substrate stiffness, etc.) on the direction of cell alignment can largely be understood by considering their impact on cell-substrate detachment events, specifically whether detachment occurs during stretching or relaxing of the substrate.

scholarly journals SUBLINEARLY SPACE BOUNDED ITERATIVE ARRAYS

2010 ◽  
Vol 21 (05) ◽  
pp. 843-858 ◽  
Author(s):  
ANDREAS MALCHER ◽  
CARLO MEREGHETTI ◽  
BEATRICE PALANO
Keyword(s):  
Lower Bound ◽  
Computational Model ◽  
Regular Language ◽  
Finite Automata ◽  
Complexity Classes ◽  
Language Recognition ◽  
Deterministic Finite Automata ◽  
One Dimensional ◽  
Sequential Processing ◽  
Trade Offs

Iterative arrays (IAs) are a parallel computational model with a sequential processing of the input. They are one-dimensional arrays of interacting identical deterministic finite automata. In this paper, realtime-IAs with sublinear space bounds are used to recognize formal languages. The existence of an infinite proper hierarchy of space complexity classes between logarithmic and linear space bounds is proved. Some decidability questions on logarithmically space bounded realtime-IAs are investigated, and an optimal logarithmic space lower bound for non-regular language recognition on realtime-IAs is shown. Finally, some non-recursive trade-offs between space bounded realtime-IAs are emphasized.

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