Exploring the Effect of Crowd Management Measures on Passengers’ Behaviour at Metro Stations

To reduce problems of interaction at the platform train interface (PTI) platform edge doors (PEDs) and markings on the platform are used as door positions indicators. The common methods to study the effect of these measures are based on average values of density using Fruin’s Level of Service (LOS), however identification cannot be made of which part of the PTI is more congested. To solve this problem, a new method is proposed. The method included a conceptual model in which the PTI was discretised into 40 cm square cells to identify which part of the platform is more congested. Passengers’ behaviour was recorded considering two situations before the train arrives: i) passengers waiting in front of the doors; ii) passengers waiting beside the doors. Observation was done at existing stations at Metro de Santiago and London Underground. Results show that PEDs changed the behaviour of passengers as they were located beside the doors rather than in front of them. In addition, when markings were used on the platform, then this behaviour was reinforced. Therefore, it is recommended to use this method to better design the PTI rather than the LOS which is used to design the whole platform. Further research is needed to study the effect of PEDs on passengers with reduced mobility

Harmonic Analysis of Boolean Networks: Determinative Power and Perturbations

Consider a large Boolean network with a feed forward structure. Given a probability distribution on the inputs, can one find, possibly small, collections of input nodes that determine the states of most other nodes in the network? To answer this question, a notion that quantifies the determinative power of an input over the states of the nodes in the network is needed. We argue that the mutual information (MI) between a given subset of the inputs X = {X_1, ..., X_n} of some node i and its associated function f_i(X) quantifies the determinative power of this set of inputs over node i. We compare the determinative power of a set of inputs to the sensitivity to perturbations to these inputs, and find that, maybe surprisingly, an input that has large sensitivity to perturbations does not necessarily have large determinative power. However, for unate functions, which play an important role in genetic regulatory networks, we find a direct relation between MI and sensitivity to perturbations. As an application of our results, we analyze the large-scale regulatory network of Escherichia coli. We identify the most determinative nodes and show that a small subset of those reduces the overall uncertainty of the network state significantly. Furthermore, the network is found to be tolerant to perturbations of its inputs

ОЦЕНКА КАЧЕСТВА ПЕРЕВОЗКИ ПАССАЖИРОВ ГОРОДСКИМ ТРАНСПОРТОМ ПРИ РАЗЛИЧНОМ КОЛИЧЕСТВЕ ТРАНСПОРТНЫХ СРЕДСТВ, РАБОТАЮЩИХ НА МАРШРУТЕ

The quality of services which are offered by urban passenger transport determines a living standard of country’s inhabitants. The executed analysis of methods for quality assessment of passenger transportation service by urban transport has shown that it is expedient to use complex indices for such assessment. The existing methods for quality assessment of urban passenger transport do not fully take into account subjective passengers’ evaluation of service quality criteria. The investigations conducted with the purpose to estimate significance of quality assessment criteria pertaining to urban pas-senger transport operation for passengers have made it possible to formalize importance of the given significance. A complex index of urban passenger transport quality has been prepared that includes such isolated quality indicators as pedestrian mo-tion component, waiting time, travel time, dynamic factor of capacity usage. It has been determined that it is possible to plan quality of passenger transportation while using transport process simulation. Influence of technological parameters on quality parameters can be determined with the help of the developed simulation model for quality assessment of transportation pro-cess on route of urban passenger transport. While using the simulation model regularities in changing a complex quality index of urban passenger transport have been analyzed according to number of transport facilities operating on route. Simulation results have shown that changes in number of transport facilities significantly exert an influence on the value of dynamic fac-tor of transport facility capacity usage, waiting time for transport facility and time travel. This leads to changes in quality of transportation process. Analysis of the obtained results has permitted to make a conclusion that every route with certain parameters has such rational number of transport facilities that ensures maximum efficiency of urban passenger transport with appropriate quality. It has been revealed that any change in number of transport facilities is described with a reasonable degree of accuracy by a nonlinear regression equation where parameters of transport facilities, passenger traffic flows and route are used as independent variables. Качество услуг, которые предоставляет городской пассажирский транспорт, определяет стандарты жизни жителей страны. Проведенный анализ методов оценки качества транспортного обслуживания пассажиров городским транспортом показал, что для этой оценки целесообразно использовать комплексные показатели. Существующие методы оценки качества городского пассажирского транспорта не полностью учитывают субъективную оценку пассажирами критериев качества обслуживания. Проведенные исследования значимости для пассажиров критериев оценки качества работы городского пассажирского транспорта позволили формализовать значение данной значимости. На этой основе был разработан комплексный показатель качества городского пассажирского транспорта, включающий в себя единичные показатели качества: пешеходной составляющей передвижений, времени ожидания, времени поездки, динамического коэффициента использования вместимости. Определено, что планирование качества перевозки пассажиров возможно с использованием моделирования транспортного процесса. Влияние технологических параметров на параметры качества можно определить с помощью разработанной имитационной модели оценки качества процесса перевозки на маршруте городского пассажирского транспорта. С применением имитационной модели проанализированы закономерности изменения комплексного показателя качества городского пассажирского транспорта в зависимости от количества транспортных средств, работающих на маршруте. Результаты моделирования показали, что изменение количества транспортных средств существенным образом влияет на величину динамического коэффициента использования вместимости транспортного средства, времени ожидания транспортных средств и времени поездки. Это приводит к изменению качества процесса перевозки. Анализ полученных результатов позволил сделать вывод, что для каждого маршрута с определенными параметрами существует такое рациональное количество транспортных средств, которое обеспечивает максимальную эффективность городского пассажирского транспорта при соответствующем качестве. Выявлено, что изменение данного количества транспортных средств с достаточной точностью описывается нелинейным регрессионным уравнением, в котором в качестве независимых переменных используются параметры транспортных средств, пассажиропотока и маршрута

Parameter estimation for Boolean models of biological networks

Boolean networks have long been used as models of molecular networks and play an increasingly important role in systems biology. This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean network models based on experimental data and biological input. The key feature is a discrete analog of parameter estimation for continuous models. With only experimental data as input, the software can be used as a tool for reverse-engineering of Boolean network models from experimental time course data.Comment: Web interface of the software is available at http://polymath.vbi.vt.edu/polynome

Prediction of lethal and synthetically lethal knock-outs in regulatory networks

The complex interactions involved in regulation of a cell's function are captured by its interaction graph. More often than not, detailed knowledge about enhancing or suppressive regulatory influences and cooperative effects is lacking and merely the presence or absence of directed interactions is known. Here we investigate to which extent such reduced information allows to forecast the effect of a knock-out or a combination of knock-outs. Specifically we ask in how far the lethality of eliminating nodes may be predicted by their network centrality, such as degree and betweenness, without knowing the function of the system. The function is taken as the ability to reproduce a fixed point under a discrete Boolean dynamics. We investigate two types of stochastically generated networks: fully random networks and structures grown with a mechanism of node duplication and subsequent divergence of interactions. On all networks we find that the out-degree is a good predictor of the lethality of a single node knock-out. For knock-outs of node pairs, the fraction of successors shared between the two knocked-out nodes (out-overlap) is a good predictor of synthetic lethality. Out-degree and out-overlap are locally defined and computationally simple centrality measures that provide a predictive power close to the optimal predictor.Comment: published version, 10 pages, 6 figures, 2 tables; supplement at http://www.bioinf.uni-leipzig.de/publications/supplements/11-01

Boolean network model predicts cell cycle sequence of fission yeast

A Boolean network model of the cell-cycle regulatory network of fission yeast (Schizosaccharomyces Pombe) is constructed solely on the basis of the known biochemical interaction topology. Simulating the model in the computer, faithfully reproduces the known sequence of regulatory activity patterns along the cell cycle of the living cell. Contrary to existing differential equation models, no parameters enter the model except the structure of the regulatory circuitry. The dynamical properties of the model indicate that the biological dynamical sequence is robustly implemented in the regulatory network, with the biological stationary state G1 corresponding to the dominant attractor in state space, and with the biological regulatory sequence being a strongly attractive trajectory. Comparing the fission yeast cell-cycle model to a similar model of the corresponding network in S. cerevisiae, a remarkable difference in circuitry, as well as dynamics is observed. While the latter operates in a strongly damped mode, driven by external excitation, the S. pombe network represents an auto-excited system with external damping.Comment: 10 pages, 3 figure

Identification of a Topological Characteristic Responsible for the Biological Robustness of Regulatory Networks

Attribution of biological robustness to the specific structural properties of a regulatory network is an important yet unsolved problem in systems biology. It is widely believed that the topological characteristics of a biological control network largely determine its dynamic behavior, yet the actual mechanism is still poorly understood. Here, we define a novel structural feature of biological networks, termed ‘regulation entropy’, to quantitatively assess the influence of network topology on the robustness of the systems. Using the cell-cycle control networks of the budding yeast (Saccharomyces cerevisiae) and the fission yeast (Schizosaccharomyces pombe) as examples, we first demonstrate the correlation of this quantity with the dynamic stability of biological control networks, and then we establish a significant association between this quantity and the structural stability of the networks. And we further substantiate the generality of this approach with a broad spectrum of biological and random networks. We conclude that the regulation entropy is an effective order parameter in evaluating the robustness of biological control networks. Our work suggests a novel connection between the topological feature and the dynamic property of biological regulatory networks

Detecting controlling nodes of boolean regulatory networks

Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 23k, where k is the number of controlling variables for a function. Further, we consider the class of 1-low networks which include unate networks, linear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied

Evolving Sensitivity Balances Boolean Networks

We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman’s Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. Insilico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.Fundacao de Amparo e Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Coordenacao de Aperfeicofamento de Pessoal de Nivel Superior (CAPES)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq