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
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
Landauro, C., VNowak, H.Haeussler, P.
11页查看更多>>摘要：We present a new context independent complexity measure, the information capacitance, for discrete probability distributions, which is based on entropy-and energy measures and describes the ability of a system to absorb or emit information. We compare the new complexity measure with the statistical definition of complexity given by Lopez-Ruiz, Mancini and Calbet. We apply both definitions in several systems which are described by discrete probability distributions. Namely, two systems beyond the thermodynamic equilibrium, i.e. a DNA-two state system and the logistic map, and also for magnetic systems in thermodynamic equilibrium. It is shown that the information capacitance takes into account spin fluctuations near phase transitions in magnetic systems. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Baybusinov, Izat B.Fenoaltea, Enrico MariaZhang, Yi-Cheng
7页查看更多>>摘要：We propose and solve a negotiation model of multiple players facing many alternative solutions. The model can be generalized to many relevant circumstances where stakeholders' interests partially overlap and partially oppose. We also show that the model can be mapped into the well-known directed percolation and directed polymers problems. Moreover, many statistical mechanics tools, such as the Replica method, can be fruitfully employed. Studying our negotiation model can enlighten the links between social-economic phenomena and traditional statistical mechanics and help to develop new perspectives and tools in the fertile interdisciplinary field. (C) 2021 The Author(s). Published by Elsevier B.V.
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
Bouyer, AsgaraliBeni, Hamid Ahmadi
19页查看更多>>摘要：The influence maximization problem has gained particular importance in viral marketing for large-scale spreading in social networks. Developing a fast and appropriate algorithm to identify an optimized seed set for the diffusion process on social networks is crucial due to the fast growth of networks. Most fast methods only focus on the degree of nodes while ignoring the strategic position of nodes in the networks. These methods do not have the required quality in finding a seed set in most networks. On the other hand, many other methods have acceptable quality, but their computational overhead is significant. To address these issues, the main concentration of this paper is to propose a fast and accurate method for the influence maximization problem, which uses a local traveling for labeling of nodes based on the influence power, called the LMP algorithm. In the proposed LMP algorithm, first, a travel starts from a node with the lowest influence power to assign a ranking-label for this node and its neighbor nodes in each step based on their diffusion capability and strategic position. The LMP algorithm uses node labeling steps to reduce search space significantly. Three ranking-labels are used in the proposed algorithm, and nodes with the highest ranking-label are selected as candidate nodes. This local and fast step strictly reduces the search space. Finally, the LMP algorithm selects seed nodes based on the topology features and the strategic position of the candidate and connector. The performance of the proposed algorithm is benchmarked with the well-known and recently proposed seed selection algorithms. The experimental results are performed on real-world and synthetic networks to validate the efficiency and effectiveness. The experiments exhibit that the proposed algorithm is the fastest in comparison with other state-of-the-art algorithms, and it has linear time complexity. In addition, it can achieve a good tradeoff between the efficiency and time complexity in the influence maximization problem.(C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Li, JianhuiLi, QiaozhiWang, FangLiu, Fan...
11页查看更多>>摘要：Hyperspectral reflectance means a curve in a range of certain wavelength, the complex dynamic structure of which reflects the rich information of an object at different bands, which is often used as various modeling inputs. However, the potential redundancy associating with the information mentioned above will have serious impacts for the accurate extraction of spectral features. Thus, detecting information redundancy is a critical processing for the spectral analysis. By using the local detrended fluctuation analysis, we propose a new method detecting the redundant bands, which focuses on the spectral auto-correlation represented by local Hurst exponent in the moving windows, and the redundant bands can be defined through comparing the auto-correlation between two adjacent windows. Finally, with the fractal feature of the removing redundant bands as the augment, the rapeseed oleic acid prediction model based on the random decision forest is constructed to test our method. For the purpose of comparing, the same feature as the original spectrum is also employed as the augment for the model. The testing result shows that the feature obtained by removing the redundant bands has better performance over the feature of the original spectrum.(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
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
