Relationship between US Societal Fatality Risk per Vehicle Miles of Travel and Mass, for Individual Vehicle Models over Time (Model Year)

Trust is a key component of social interaction. Older adults, however, often exhibit excessive trust relative to younger adults. One explanation is that older adults may learn to trust differently than younger adults. Here, we examine how younger (N=33) and older adults (N=30) learn to trust over time. Participants completed a classic iterative trust game with three partners. Younger and older adults shared similar amounts but differed in how they shared money. Compared to younger adults, older adults invested more with untrustworthy partners and less with trustworthy partners. As a group, older adults displayed less learning than younger adults. However, computational modeling shows that this is because older adults are more likely to forget what they have learned over time. Model-based fMRI analyses revealed several age-related differences in neural processing. Younger adults showed prediction error signals in social processing areas while older adults showed over-recruitment of several cortical areas. Collectively, these findings suggest that older adults attend to and learn from social cues differently from younger adults.

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Effect of Using Different Vehicle Weight Groups on the Estimated Relationship Between Mass Reduction and U.S. Societal Fatality Risk per Vehicle Miles of Travel

10.2172/1345199 ◽

2016 ◽

Author(s):

Tom P. Wenzel

Keyword(s):

Mass Reduction ◽

Fatality Risk ◽

Vehicle Weight ◽

Weight Groups ◽

Vehicle Miles Of Travel

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Assessment of NHTSA’s Report “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2004-2011 Passenger Cars and LTVs” (LBNL Phase 1)

10.2172/1430692 ◽

2018 ◽

Author(s):

Tom P. Wenzel

Keyword(s):

Phase 1 ◽

Passenger Cars ◽

Fatality Risk ◽

Model Year

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An Assessment of the Effects of Vehicle Weight and Size on Fatality Risk in 1985 to 1998 Model Year Passenger Cars and 1985 to 1997 Model Year Light Trucks and Vans

10.4271/2005-01-1354 ◽

2005 ◽

Cited By ~ 6

Author(s):

R. Michael Van Auken ◽

J. W. Zellner

Keyword(s):

Passenger Cars ◽

Fatality Risk ◽

Vehicle Weight ◽

Light Trucks ◽

Model Year

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The diffusion of language change in real time: Progressive and conservative individuals and the time depth of change

Language Variation and Change ◽

10.1017/s0954394510000207 ◽

2011 ◽

Vol 23 (1) ◽

pp. 1-43 ◽

Cited By ~ 37

Author(s):

Terttu Nevalainen ◽

Helena Raumolin-Brunberg ◽

Heikki Mannila

Keyword(s):

Computational Model ◽

Real Time ◽

Language Change ◽

Time Model ◽

Linguistic Change ◽

Structural Patterns ◽

Stage Of Development ◽

Stuart England ◽

Early English ◽

Over Time

AbstractA major issue in the study of language change is the degree to which individual speakers participate in ongoing linguistic changes as these progress over time. In this study, we examine the hypothesis, suggested by research based on the apparent-time model, that in any given period most people are neither progressive nor conservative with regard to ongoing changes, but rather fall between these polarities. Our data come from the Corpus of Early English Correspondence, which spans over 270 years. A computational model was developed to establish which language users were progressive and which conservative with respect to several ongoing changes that progressed in real time between the early 15th and late 17th centuries. The changes studied ranged from morpheme replacements to more abstract structural patterns. Our results indicate that the degree to which language users participated in changes in progress depended on the type of language change analyzed, the stage of development of the change, and the rate of diffusion of the process over time. The model also enabled the identification of groups of leaders of linguistic change in Tudor and Stuart England.

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A dynamic power-law sexual network model of gonorrhoea outbreaks

10.1101/322875 ◽

2018 ◽

Author(s):

Lilith K Whittles ◽

Peter J White ◽

Xavier Didelot

Keyword(s):

Network Model ◽

Power Law ◽

Dynamic Network ◽

Great Promise ◽

Model Parameters ◽

Power Law Distribution ◽

Time Model ◽

Dynamic Network Model ◽

The Impact ◽

Over Time

AbstractHuman networks of sexual contacts are dynamic by nature, with partnerships forming and breaking continuously over time. Sexual behaviours are also highly heterogeneous, so that the number of partners reported by individuals over a given period of time is typically distributed as a power-law. Both the dynamism and heterogeneity of sexual partnerships are likely to have an effect in the patterns of spread of sexually transmitted diseases. To represent these two fundamental properties of sexual networks, we developed a stochastic process of dynamic partnership formation and dissolution, which results in power-law numbers of partners over time. Model parameters can be set to produce realistic conditions in terms of the exponent of the power-law distribution, of the number of individuals without relationships and of the average duration of relationships. Using an outbreak of antibiotic resistant gonorrhoea amongst men have sex with men as a case study, we show that our realistic dynamic network exhibits different properties compared to the frequently used static networks or homogeneous mixing models. We also consider an approximation to our dynamic network model in terms of a much simpler branching process. We estimate the parameters of the generation time distribution and offspring distribution which can be used for example in the context of outbreak reconstruction based on genomic data. Finally, we investigate the impact of a range of interventions against gonorrhoea, including increased condom use, more frequent screening and immunisation, concluding that the latter shows great promise to reduce the burden of gonorrhoea, even if the vaccine was only partially effective or applied to only a random subset of the population.

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Assessment of NHTSA’s Report “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2003-2010 Passenger Cars and LTVs”

10.2172/1341729 ◽

2016 ◽

Author(s):

Tom Wenzel

Keyword(s):

Passenger Cars ◽

Fatality Risk ◽

Model Year

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An analytical framework for the study of epidemic models on activity driven networks

Journal of Complex Networks ◽

10.1093/comnet/cnx056 ◽

2017 ◽

Vol 5 (6) ◽

pp. 924-952 ◽

Cited By ~ 16

Author(s):

Lorenzo Zino ◽

Alessandro Rizzo ◽

Maurizio Porfiri

Keyword(s):

Jump Process ◽

Analytical Framework ◽

Epidemic Models ◽

Time Varying ◽

Epidemic Spreading ◽

Markov Jump ◽

Real World Data ◽

Time Model ◽

Formal Framework ◽

Over Time

Abstract Network theory has greatly contributed to an improved understanding of epidemic processes, offering an empowering framework for the analysis of real-world data, prediction of disease outbreaks, and formulation of containment strategies. However, the current state of knowledge largely relies on time-invariant networks, which are not adequate to capture several key features of a number of infectious diseases. Activity driven networks (ADNs) constitute a promising modelling framework to describe epidemic spreading over time varying networks, but a number of technical and theoretical gaps remain open. Here, we lay the foundations for a novel theory to model general epidemic spreading processes over time-varying, ADNs. Our theory derives a continuous-time model, based on ordinary differential equations (ODEs), which can reproduce the dynamics of any discrete-time epidemic model evolving over an ADN. A rigorous, formal framework is developed, so that a general epidemic process can be systematically mapped, at first, on a Markov jump process, and then, in the thermodynamic limit, on a system of ODEs. The obtained ODEs can be integrated to simulate the system dynamics, instead of using computationally intensive Monte Carlo simulations. An array of mathematical tools for the analysis of the proposed model is offered, together with techniques to approximate and predict the dynamics of the epidemic spreading, from its inception to the endemic equilibrium. The theoretical framework is illustrated step-by-step through the analysis of a susceptible–infected–susceptible process. Once the framework is established, applications to more complex epidemic models are presented, along with numerical results that corroborate the validity of our approach. Our framework is expected to find application in the study of a number of critical phenomena, including behavioural changes due to the infection, unconscious spread of the disease by exposed individuals, or the removal of nodes from the network of contacts.

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