Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages.

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

Bridging the Micro-Macro Gap between Single-Molecular Behavior and Bulk Hydrolysis Properties of Cellulase.

The microscopic kinetics of enzymes at the single-molecule level often deviate considerably from those expected from bulk biochemical experiments. Here, we propose a coarse-grained-model approach to bridge this gap, focusing on the unexpectedly slow bulk hydrolysis of crystalline cellulose by cellulase, which constitutes a major obstacle to mass production of biofuels and biochemicals. Building on our previous success in tracking the movements of single molecules of cellulase on crystalline cellulose, we develop a mathematical description of the collective motion and function of enzyme molecules hydrolyzing the surface of cellulose. Model simulations robustly explained the experimental findings at both the microscopic and macroscopic levels and revealed a hitherto-unknown mechanism causing a considerable slowdown of the reaction, which we call the crowding-out effect. The size of the cellulase molecule impacted significantly on the collective dynamics, whereas the rate of molecular motion on the surface did not.

Critical bottleneck size for jamless particle flows in two dimensions.

We propose a simple microscopic model for arching phenomena at bottlenecks. The dynamics of particles in front of a bottleneck is described by a one-dimensional stochastic cellular automaton on a semicircular geometry. The model reproduces oscillation phenomena due to the formation and collapsing of arches. It predicts the existence of a critical bottleneck size for continuous particle flows. The dependence of the jamming probability on the system size is approximated by the Gompertz function. The analytical results are in good agreement with simulations.

Metastability due to a branching-merging structure in a simple network of an exclusion process.

We investigate a simple network, which has a branching-merging structure, using the totally asymmetric simple exclusion process, considering conflicts at the merging point. For both periodic and open boundary conditions, the system exhibits metastability. Specifically, for open boundary conditions, we observe two types of metastability: hysteresis and a nonergodic phase. We analytically determine the tipping points, that is, the critical conditions under which a small disturbance can lead to the collapse of metastability. Our findings provide insights into metastability induced by branching-merging structures, which exist in all network systems in various fields.

Two-lane totally asymmetric simple exclusion process with extended Langmuir kinetics.

Multilane totally asymmetric simple exclusion processes with interactions between the lanes have recently been investigated actively. This paper proposes a two-lane model with extended Langmuir kinetics on a periodic lattice. Both bidirectional and unidirectional flows are investigated. In our model, the hopping, attachment, and detachment rates vary depending on the state of the corresponding site in the other lane. We obtain a theoretical expression for the global density of the system in the steady state from three kinds of mean-field analyses [(1×1)-, (2×1)-, and (2×2)-cluster cases]. We verify that the (2×2)-cluster mean-field analysis reproduces the differences between the two directional flows and approximates well the results of computer simulations for some cases. We observe that (2×1)-cluster mean-field analyses are already good approximations of the simulation results for unidirectional flows; on the other hand, the accuracy of the approximations much improves by (2×2)-cluster one for bidirectional flows. We explain the phenomena in a qualitative manner by a simple analysis of correlations. We expect these findings to give informative suggestions for actual traffic systems.

Effect of congestion avoidance due to congestion information provision on optimizing agent dynamics on an endogenous star network topology.

This study elucidates the effect of congestion avoidance of agents given congestion information on optimizing traffic in a star topology in which the central node is connected to isolated secondary nodes with different preferences. Each agent at the central node stochastically selects a secondary node by referring to the declining preferences based on the congestion rate of the secondary nodes. We investigated two scenarios: (1) repeated visits and (2) a single visit for each node. For (1), we found that multivariate statistics describe well the nonlinear dependence of agent distribution on the number of secondaries, demonstrating the existence of the number of secondaries that makes the distribution the most uniform. For (2), we discovered that congestion avoidance linearizes the travel time for all agents visiting all nodes; in contrast, the travel time increases exponentially with secondaries when not referring to congestion information. Health examination epitomizes this finding; by allowing patients to be preferentially selected for examination in vacant examination sites, we can linearize the time it takes for everyone to complete their examination. We successfully described the optimization effect of congestion avoidance on the collective dynamics of agents in star topologies.

Model retraining and information sharing in a supply chain with long-term fluctuating demands.

Demand forecasting based on empirical data is a viable approach for optimizing a supply chain. However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data. In this study, we examine the effects of updating models in a supply chain using a minimal setting. We demonstrate that when each party in the supply chain has its own forecasting model, uncoordinated model retraining causes the bullwhip effect even if a very simple replenishment policy is applied. Our results also indicate that sharing the forecasting model among the parties involved significantly reduces the bullwhip effect.

Mutual anticipation can contribute to self-organization in human crowds.

Human crowds provide paradigmatic examples of collective behavior emerging through self-organization. Understanding their dynamics is crucial to help manage mass events and daily pedestrian transportation. Although recent findings emphasized that pedestrians' interactions are fundamentally anticipatory in nature, whether and how individual anticipation functionally benefits the group is not well understood. Here, we show the link between individual anticipation and emergent pattern formation through our experiments of lane formation, where unidirectional lanes are spontaneously formed in bidirectional pedestrian flows. Manipulating the anticipatory abilities of some of the pedestrians by distracting them visually delayed the collective pattern formation. Moreover, both the distracted pedestrians and the nondistracted ones had difficulties avoiding collisions while navigating. These results imply that avoidance maneuvers are normally a cooperative process and that mutual anticipation between pedestrians facilitates efficient pattern formation. Our findings may influence various fields, including traffic management, decision-making research, and swarm dynamics.

A system for efficient egress scheduling during mass events and small-scale experimental demonstration.

Improvements in the design of pedestrian facilities have reduced the frequency of crowd accidents, and safety is now generally ensured in well-planned crowd events. However, congestion and inefficient use of infrastructures still remain an issue. To guarantee comfort and reduce close contacts between people, there are circumstances when crowd density may have to be reduced well below safety limits. Although research has given a lot of attention to extreme scenarios, methods to improve non-critical conditions have been little explored. In addition, crowd sensing technology is still mostly used for data collection and direct use on crowd management is rare. In this work, we present a system aimed at computing optimal egress time for groups of people leaving a complex facility. We show that, if egress starting time is accurately computed for each group based on actual crowd conditions, density can be greatly reduced without having a large effect on the total egress time of the whole crowd. To show the efficacy of such a system, a small-scale experiment is conducted where all components are tested in a simple scenario. As a result, an increase in total egress time by only 5% allowed to reduce maximum density by 35%.

Systematic experimental investigation of the obstacle effect during non-competitive and extremely competitive evacuations.

Although some experimental evidence showed that an obstacle placed in front of a door allows making people's evacuations faster, the efficacy of such a solution has been debated for over 15 years. Researchers are split between those who found the obstacle beneficial and those who could not find a significant difference without it. One of the reasons for the several conclusions lies in the variety of the experiments performed so far, both in terms of competitiveness among participants, geometrical configuration and number of participants. In this work, two unique datasets relative to evacuations with/without obstacle and comprising low and high competitiveness are analyzed using state-of-the-art definitions for crowd dynamics. In particular, the so-called congestion level is employed to measure the smoothness of collective motion. Results for extreme conditions show that, on the overall, the obstacle does not reduce density and congestion level and it could rather slightly increase it. From this perspective, the obstacle was found simply shifting the dangerous spots from the area in front of the exit to the regions between the obstacle and the wall. On the other side, it was however confirmed, that the obstacle can stabilize longitudinal crowd waves, thus reducing the risk of trampling, which could be as important (in terms of safety) as improving the evacuation time. However, under urgent, competitive, but non-extreme conditions, the obstacle generally had a positive effect, helping channeling the flow of pedestrians through the exit while facilitating their interactions.

Diffusion enhancement in a levitated droplet via oscillatory deformation.

Recent experimental results indicate that mixing is enhanced by a reciprocal flow induced inside a levitated droplet with an oscillatory deformation [T. Watanabe et al., Sci. Rep. 8, 10221 (2018)2045-232210.1038/s41598-018-28451-5]. Generally, reciprocal flow cannot convect the solutes in time average, and agitation cannot take place. In the present paper, we focus on the diffusion process coupled with the reciprocal flow. We theoretically derive that the diffusion process can be enhanced by the reciprocal flow, and the results are confirmed via numerical calculation of the over-damped Langevin equation with a reciprocal flow.

Ultradiscrete optimal velocity model: a cellular-automaton model for traffic flow and linear instability of high-flux traffic.

In this paper, we propose the ultradiscrete optimal velocity model, a cellular-automaton model for traffic flow, by applying the ultradiscrete method for the optimal velocity model. The optimal velocity model, defined by a differential equation, is one of the most important models; in particular, it successfully reproduces the instability of high-flux traffic. It is often pointed out that there is a close relation between the optimal velocity model and the modified Korteweg-de Vries (mkdV) equation, a soliton equation. Meanwhile, the ultradiscrete method enables one to reduce soliton equations to cellular automata which inherit the solitonic nature, such as an infinite number of conservation laws, and soliton solutions. We find that the theory of soliton equations is available for generic differential equations and the simulation results reveal that the model obtained reproduces both absolutely unstable and convectively unstable flows as well as the optimal velocity model.

Achievement of alternative configurations of vehicles on multiple lanes.

Heavy traffic congestion occurs daily at merging sections on a highway. For relieving this congestion, possibility of alternative configuration of vehicles on multiple-lane road at a merging area is discussed in this paper. This is the configuration where no vehicles move aside on the other lane. It has merit in making a smooth merging at an intersection or a junction due to the so-called "zipper effect." We show, by developing a cellular automaton model for multiple lanes, that this configuration is achieved by simple local interactions between vehicles neighboring each other. The degree of the alternative configuration in terms of the spatial increase in parallel driving length is studied by using both numerical simulations and mean-field theory. We successfully construct a theoretical method for calculating this degree of the alternative configuration by using cluster approximation. It is shown that the theoretical results coincide with those of the simulations very well.

Dependence of the transportation time on the sequence in which particles with different hopping probabilities enter a lattice.

Smooth transportation has drawn the attention of many researchers and practitioners in several fields. In the present study, we propose a modified model of a totally asymmetric simple exclusion process (TASEP), which includes multispecies of particles and takes into account the sequence in which the particles enter a lattice. We investigate the dependence of the transportation time on this "entering sequence" and show that, for a given collection of particles, group sequence in some cases minimizes the transportation time better than a random sequence. We also introduce the "sorting cost" necessary to transform a random sequence into a group sequence and show that when this is included a random sequence can become advantageous in some conditions. We obtain these results not only from numerical simulations but also by theoretical analyses that generalize the simulation results for some special cases.

Lévy walk process in self-organization of pedestrian crowds.

Similar to other animal groups, human crowds exhibit various collective patterns that emerge from self-organization. Recent studies have emphasized that individuals anticipate their neighbours' motions to seek their paths in dynamical pedestrian flow. This path-seeking behaviour results in deviation of pedestrians from their desired directions (i.e. the direct path to their destination). However, the strategies that individuals adopt for the behaviour and how the deviation of individual movements impact the emergent organization are poorly understood. We here show that the path-seeking behaviour is performed through a scale-free movement strategy called a Lévy walk, which might facilitate transition to the group-level behaviour. In an experiment of lane formation, a striking example of self-organized patterning in human crowds, we observed how flows of oppositely moving pedestrians spontaneously separate into several unidirectional lanes. We found that before (but not after) lane formation, pedestrians deviate from the desired direction by Lévy walk process, which is considered optimal when searching unpredictably distributed resources. Pedestrians balance a trade-off between seeking their direct paths and reaching their goals as quickly as possible; they may achieve their optimal paths through Lévy walk process, facilitating the emergent lane formation.

Correction: A universal function for capacity of bidirectional pedestrian streams: Filling the gaps in the literature.

[This corrects the article DOI: 10.1371/journal.pone.0208496.].

Traffic flow in a crowd of pedestrians walking at different speeds.

This study investigates motion in a crowd of pedestrians walking at different speeds. Three pedestrian groups are considered (slow walkers, normal walkers, and fast walkers), and we design the experimental condition by mixing the normal walkers with either the slow or the fast walkers to create flows with different speed compositions. All the walkers in this experiment were instructed to walk along a circular course unidirectionally. Fundamental diagrams and multiple regression analysis show that the speed at which a particular pedestrian walks is determined by both the local density and the speed at which the surrounding pedestrians are walking. We also find that the spontaneous lane formation, that occurs in bidirectional flow, does not occur in flow in which the speed is heterogeneous, thereby resulting in a spatial density distribution with large variance. This corresponds to pedestrian clustering, which reduces both the mean speed and the flow rate.

Biologically motivated asymmetric exclusion process: Interplay of congestion in RNA polymerase traffic and slippage of nascent transcript.

We develop a theoretical framework, based on an exclusion process, that is motivated by a biological phenomenon called transcript slippage (TS). In this model a discrete lattice represents a DNA strand while each of the particles that hop on it unidirectionally, from site to site, represents a RNA polymerase (RNAP). While walking like a molecular motor along a DNA track in a step-by-step manner, a RNAP simultaneously synthesizes an RNA chain; in each forward step it elongates the nascent RNA molecule by one unit, using the DNA track also as the template. At some special "slippery" position on the DNA, which we represent as a defect on the lattice, a RNAP can lose its grip on the nascent RNA and the latter's consequent slippage results in a final product that is either longer or shorter than the corresponding DNA template. We develop an exclusion model for RNAP traffic where the kinetics of the system at the defect site captures key features of TS events. We demonstrate the interplay of the crowding of RNAPs and TS. A RNAP has to wait at the defect site for a longer period in more congested RNAP traffic, thereby increasing the likelihood of its suffering a larger number of TS events. The qualitative trends of some of our results for a simple special case of our model are consistent with experimental observations. The general theoretical framework presented here will be useful for guiding future experimental queries and for analysis of the experimental data with more detailed versions of the same model.

Publisher's Note: Empirical analysis of the lane formation process in bidirectional pedestrian flow [Phys. Rev. E 94, 032304 (2016)].

This corrects the article DOI: 10.1103/PhysRevE.94.032304.

Publisher's Note: Empirical analysis of the lane formation process in bidirectional pedestrian flow [Phys. Rev. E 94, 032304 (2016)].

This corrects the article DOI: 10.1103/PhysRevE.94.032304.