Chagas, Guilherme OliveiraNogueira Lorena, Luiz AntonioCoelho dos Santos, Rafael Duarte
14页查看更多>>摘要：Community structures, which are sets of elements that share some relationship between themselves, can be found in several real-world networks. Many of these communities, also known as clusters, can share elements, i.e., they may overlap. Identifying such overlapping clusters is usually a harder task than finding non-overlapping ones and, therefore, it needs more sophisticated methods. In this work we proposed a hybrid heuristic for detecting overlapping clusters in networks. An overlapping clustering is generated through the solving of a mixed-integer linear program using, as input, a heterogeneous set of good-quality clusters. This set is produced by two state-of-the-art overlapping community detection algorithms. In addition, some local search methods for conductance minimization are used to improve the quality of the clustering generate by our hybrid heuristic. Test results in artificial and real-world graphs show that our approach is able to detect overlapping clusters with better overall conductance than methods in the state of the art.(C) 2022 Elsevier B.V. All rights reserved.
原文链接:
NSTL
Elsevier
Mahata, AjitNurujjaman, MdMajhi, SushovanDebnath, Kanish...
14页查看更多>>摘要：In the aftermath of stock market crash due to COVID-19, not all sectors recovered in the same way. Recently, a stock price model is proposed by Mahata et al. (2021) that describes V- and L-shaped recovery of the stocks and indices, but fails to simulate the U- and Swoosh-shaped recovery that arises due to sharp fall, continuation at the low price and followed by quick recovery, slow recovery for longer period, respectively. We propose a modified model by introducing a new parameter theta = +1, 0, - 1 to quantify investors' positive, neutral and negative sentiments, respectively. The model explains movement of sectoral indices with positive financial anti-fragility (phi) showing U- and Swoosh-shaped recovery. Simulation using synthetic fund-flow with different shock lengths, phi, negative sentiment period and portion of fund-flow during recovery period show U- and Swoosh-shaped recovery. It shows that recovery of indices with positive phi becomes very weak with extended shock and negative sentiment period. Stocks with higher phi and fund-flow show quick recovery. Simulation of Nifty Bank, Nifty Financial and Nifty Realty show U-shaped recovery and Nifty IT shows Swoosh-shaped recovery. Simulation results are consistent with stock price movement. The estimated time-scale of shock and recovery of these indices are also consistent with the time duration of change of negative sentiment from the onset of COVID-19. We conclude that investors need to evaluate sentiment along with phi before investing in stock markets because negative sentiment can dampen the recovery even in financially anti-fragile stocks. (c) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Xiao, TianyiMu, TongShen, SunleSong, Yiming...
15页查看更多>>摘要：Motivated by the global pandemic of COVID-19, this study investigates the spatial factors influencing physical distancing, and how these affect the transmission of the SARS-CoV-2 virus, by integrating pedestrian dynamics with a modified susceptible- exposed-infectious model. Contacts between infected and susceptible pedestrians are examined by determining physical-distancing pedestrian dynamics in three types of spaces, and used to estimate the proportion of newly infected pedestrians in these spaces. Desired behaviour for physical distancing can be observed from simulation results, and aggregated simulation findings reveal that certain layouts enable physical distancing to reduce the transmission of SARS-CoV-2. We also provide policymakers with several design guidelines on how to proactively design more effective and resilient space layouts in the context of pandemics to keep low transmission risks while maintaining a high pedestrian volume. This approach has enormous application potential for other infectious-disease transmission and space assessments. (C) 2021 The Authors. Published by Elsevier B.V.
原文链接:- NSTL
- Elsevier
Zheng, HongweiWang, JiannanWei, WeiZheng, Zhiming...
8页查看更多>>摘要：Non-periodic phenomena are common in a wide range of real-world recurrent dynamics, such as the occasional pandemic of seasonal influenza and the abrupt collapse of stock markets. In this paper we propose a biased excitable network model and illustrate the non-periodic phenomena as the collective response of a large amount of excitable individuals. In contrast with classic excitable networks, we introduce the bias of external stimuli that affects the exact behavior of each individual rather than its own inherent property. Based on the locally tree-like topology, we make a second order approximation on the network activity with diminishing stimuli. Result shows that the self-sustainment of network dynamics is determined by the largest eigenvalue lambda of the weighted adjacent matrix. For lambda > 1 the network is self-sustained even if the stimuli intensity approaches zero. For lambda < 1, the system tends to end up in quiescent state. At critical condition lambda = 1, the dynamic range of the system, which is the range of stimuli intensity that is distinguishable according to network activity, reaches maximum value. We also find that the dynamic range can be further enhanced when nodes are more inclined to inhibitory state as a result of smaller stimuli bias. These results are well supported by numerical simulations on both synthetic and real-world networks. Based on the proposed model, we manage to reproduce similar non-periodic phenomena to those in real-world recurrent dynamics, even when the intensity of external stimuli remains constant. Our research shed light on the mechanism of non-periodic phenomena in recurrent dynamics, which can be applied to the prevention of epidemic outbreaks as well as financial crisis. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
da Silva, S. T.Batista, C. A. S.Viana, R. L.
15页查看更多>>摘要：This new tool, in the form of Machine Learning (ML), has proven to be very useful in several areas of physics, due to its strong versatility, its ability to obtain patterns in very complex systems. In this work we explore techniques from Machine Learning (ML) to characterize spatio-temporal patterns in complex dynamical systems. These techniques are applied in coupled map lattices, for which the relevant parameters are the nonlinearity and coupling strength. As a training phase of our ML, we show several samples with the dynamic characteristics of each known space-time profile, such as frozen random pattern, pattern selection, chaotic defects, intermittency and fully developed space-time chaos, for example. After the training phase, we apply our algorithm to different values of non-linearity and coupling, where given the dynamic characteristics, for each pair of parameters, we can accurately identify the regions where each of these profiles is formed. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Buechel, BedaCorman, Francesco
20页查看更多>>摘要：In the age of Intelligent Transportation Systems it is essential to provide operators and passengers with reliable information. The estimation of probability distributions of public transport travel times is crucial as it directly informs about the reliability of travel times. Thus, the probability distributions of travel times are useful for timetabling and route choice. This work estimates probability distributions of segment (multi-section) bus running and dwell times. We propose a hidden Markov chain framework, which captures the dependency structure of consecutive section running times and includes conditional correlations. The dependency structure of consecutive segment dwell times is modeled as a combination of correlation and operation-specific dependencies. Such a model allows describing the relationship between section-level running/station-level dwell time distributions and segment level distributions. The model is interpretable, as the dependency structure is explicitly modeled. Finally, the proposed model is evaluated on the operation of the trolley bus network of Zurich, Switzerland, and shows an average increase in fitting quality (measured by Wasserstein distance) of 26% for running times and 29% for dwell times compared to an approach not including conditional dependencies, i.e., convolution of link running and dwell times. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
原文链接:- NSTL
- Elsevier
Li, MengChen, TaoDu, HaoMa, Na...
13页查看更多>>摘要：Group behaviour is common among bicycle users and can have an impact on traffic dynamics in natural or emergency situations. As our observation shows, 35% of cyclists in traffic ride in pairs or groups. This paper aims to explore how cyclist group members interact and organize in bicycle flow. A field study was conducted, 166 groups of cyclists were selected as the observation objects, and their tracks were obtained through the multi-object tracking algorithm (MOT). The average speed, spatial arrangement and similarity between group members were calculated and analysed. The results show that group size has a significant negative effect on average group speed as cyclist members of the same group tend to maintain similar speeds as they ride forward. The 2-person and 3-person riding groups have relatively "time stable "geometrical structures (4 patterns for pairs and 5 patterns for triads). They are generated not only from traffic rules but also from the local interaction between group members. The dissimilarity value of movement among them tends to increase with increasing group size due to weaker coordination in larger groups. These findings have implications for bicycle traffic modelling and safe bicycle facility design. (C) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Song, JieZhang, LiyeQin, ZhengRamli, Muhamad Azfar...
21页查看更多>>摘要：Recent months have seen ever-increasing levels of confirmed COVID-19 cases despite the accelerated adoption of vaccines. In the wake of the pandemic, travel patterns of individuals change as well. Understanding the changes in biking behaviors during evolving COVID-19 situations is a primary goal of this paper. It investigated usage patterns of the bike-share system in Singapore before, during, and after local authorities imposed lockdown measures. It also correlated the centrality attributes of biking mobility networks of different timestamps with land-use conditions. The results show that total ridership surprisingly climbed by 150% during the lockdown, compared with the pre-pandemic level. Biking mobility graphs became more locally clustered and polycentric as the epidemic develop. There existed a positive and sustained spatial autocorrelation between centrality measures and regions with high residential densities or levels of the land-use mixture. This study suggests that bike-share systems may serve as an alternative mode to fulfill mobility needs when public transit services are restricted due to lockdown policies. Shared-micromobility services have the potential to facilitate a disease-resilient transport system as societies may have to coexist with COVID in the future.(C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
