Crowding effects in vehicular traffic

While the impact of crowding on the diffusive transport of molecules within a cell is widely studied in biology, it has thus far been neglected in traffic systems where bulk behavior is the main concern. Here, we study the effects of crowding due to car density and driving fluctuations on the transport of vehicles. Using a microscopic model for traffic, we found that crowding can push car movement from a superballistic down to a subdiffusive state. The transition is also associated with a change in the shape of the probability distribution of positions from negatively-skewed normal to an exponential distribution. Moreover, crowding broadens the distribution of cars' trap times and cluster sizes. At steady state, the subdiffusive state persists only when there is a large variability in car speeds. We further relate our work to prior findings from random walk models of transport in cellular systems.Comment: 23 pages, 11 figures, accepted for publication in PLoS ON

Immediate Effects of Cervical Spine Manipulation on Gait Parameters in Individuals with and without Mechanical Neck Pain

Purpose: The purpose of this study was to determine 1) if there were any differences in gait parameters between participants with mechanical neck pain and those without and 2) if cervical spine manipulation has an immediate effect on these gait parameters. Methods: Twenty participants with mechanical neck pain and twenty participants without neck pain were randomly assigned into either the sham or manipulation group. The two intervention groups participated in walking across a GAITRite Walkway that recorded gait parameters such as stride length, cadence and step width before and after cervical spine manipulation. The participants walked at their own cadence with 1) head forward, 2) head turning up and down and 3) head turning side-to-side. T-tests were used to assess 8 different gait parameters between groups before and after intervention and to assess cervical range of motion differences between groups and before and after intervention in the sagittal, transverse and coronal plane. Repeated measures two-way ANOVA was used to assess pre and post intervention differences between groups in the NDI, NPRS and GROC. Post-hoc pair-wise corrections were to be used in the event of significant interactions between treatments and groups. Statistical significant was set at p \u3c0.05. Results: Compared to pain-free subjects, the T-tests demonstrated that patients with mechanical neck pain had smaller values of gait velocity, stride length, and step length before any treatment was provided (p\u3c0.05). Prior to treatment, T-tests revealed no differences in cervical ROMs between persons with and without neck pain for the sagittal plane motion (P = 0.182); frontal plane motion (P = 0.347); and transverse plane (P = 0.181). The 2-way ANOVAs revealed a significant “group” main effect in gait velocity during normal walking (P=0.004), indicating participants with neck pain increased their velocity whereas participants without neck pain demonstrated decreased velocity regardless of intervention given. A separate independent t-test indicated that there was a significant interaction in GROC score changes between treatment and group (P =0.043). Conclusion: Our study indicated that patients with neck pain walked more slowly with shorter stride length and step length. . These gait characteristics observed might be strategies to compensate for gait instability, which involves proprioceptive deficits from the cervical spine. Additionally participants with neck pain increased their gait velocity post intervention whereas participants without neck pain demonstrated decreased velocity post intervention (manipulation/sham). While our results suggest TJM did improve gait velocity in those with neck pain post manipulation, we did not see significant changes in other gait parameters. This study suggests that clinicians should consider the assessment and management of gait performance, balance and risk of falling in patients with acute mechanical neck pain

From the Internet of Things to the web of things-enabling by sensing as-A service

© 2016 IEEE. Sensing as a Service (SenaaS) is emerging as a prominent element in the middleware linking together the Internet of Things (IoT) and the Web of Things (WoT) layers of future ubiquitous systems. An architecture framework is discussed in this paper whereby things are abstracted into services via embedded sensors which expose a thing as a service. The architecture acts as a blueprint to guide software architects realizing WoT applications. Web-enabled things are eventually appended into Web platforms such as Social Web platforms to drive data and services that are exposed by these things to interact with both other things and people, in order to materialize further the future social Web of Things. Research directions are discussed to illustrate the integration of SenaaS into the proposed WoT architectural framework

Extended Consumer-Brand Relationship Theory

This study examines online communities for two top brands: Apple iPhone and Blackberry smartphones. A number of online brand communities (online forums) such as the iPhone business affiliated website www.apple.com, iPhone consumers’ website www.everythingicafe.com, and the Blackberry consumers’ websites www.blackberry.com and www.crackberry.com were explored. This research extends the conceptual underpinnings of the existing consumer-brand relationship theory by incorporating the concept of sharing. This study offers insights for academia in the marketing and consumer behavior field as well as professionals in the high-technology industry

A Multi-Level Fit-Based Quality Improvement Initiative to Improve Colorectal Cancer Screening in a Managed Care Population.

IntroductionColorectal cancer (CRC) is a common but largely preventable disease with suboptimal screening rates despite national guidelines to screen individuals age 50-75. Single-component interventions aimed to improve screening uptake only modestly improve rates; data suggest that multi-modal approaches may be more effective.MethodsWe designed, implemented, and evaluated the impact of a multi-modal intervention on CRC screening uptake among unscreened patients in a large managed care population. Patient-level components included a mailed letter with education about screening options and pre-colonoscopy telephone counseling. For providers, we facilitated communication of screening test results and work-flow for abnormal results. System-level modifications included establishment of a patient navigator, expedited work-up for abnormal results, and stream-lined colonoscopy scheduling. We measured the rate of screening uptake overall, screening uptake by modality, change in the proportion of the population screened, and positive fecal immunochemical test (FIT) follow-up rates in the 1-year study period.ResultsThere were 5093 patients in the intervention cohort. Of these, 33.2% participated in FIT or colonoscopy screening within 1 year of the mailing. A total of 1078 (21.2%) participants completed a FIT and 611 (12.0%) completed a screening colonoscopy. The screening rate in the managed care population increased from 65.1 to 76.6%. Fifty-nine patients (5.5%) had a positive FIT, of which 30 (50.8%) completed a diagnostic colonoscopy.ConclusionMulti-modal interventions can result in substantial improvement in CRC screening uptake in large and diverse managed care populations.Translational impactHealth systems should shift their focus from single-level to multi-level interventions when addressing barriers to CRC screening

From neurons to epidemics: How trophic coherence affects spreading processes

Trophic coherence, a measure of the extent to which the nodes of a directed network are organised in levels, has recently been shown to be closely related to many structural and dynamical aspects of complex systems, including graph eigenspectra, the prevalence or absence of feed-back cycles, and linear stability. Furthermore, non-trivial trophic structures have been observed in networks of neurons, species, genes, metabolites, cellular signalling, concatenated words, P2P users, and world trade. Here we consider two simple yet apparently quite different dynamical models -- one a Susceptible-Infected-Susceptible (SIS) epidemic model adapted to include complex contagion, the other an Amari-Hopfield neural network -- and show that in both cases the related spreading processes are modulated in similar ways by the trophic coherence of the underlying networks. To do this, we propose a network assembly model which can generate structures with tunable trophic coherence, limiting in either perfectly stratified networks or random graphs. We find that trophic coherence can exert a qualitative change in spreading behaviour, determining whether a pulse of activity will percolate through the entire network or remain confined to a subset of nodes, and whether such activity will quickly die out or endure indefinitely. These results could be important for our understanding of phenomena such as epidemics, rumours, shocks to ecosystems, neuronal avalanches, and many other spreading processes

CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting

Crowdsensing using mobile phones is a novel addition to the Internet of Things applications suite. However, there are many challenges related to crowdsensing, including (1) the ability to manage a large number of mobile users with varying devices’ capabilities; (2) recruiting reliable users available in the location of interest at the right time; (3) handling various sensory data collected with different requirements and at different frequencies and scales; (4) brokering the relationship between data collectors and consumers in an efficient and scalable manner; and (5) automatically generating intelligence reports after processing the collected sensory data. No comprehensive end-to-end crowdsensing platform has been proposed despite a few attempts to address these challenges. In this work, we aim at filling this gap by proposing and describing the practical implementation of an end-to-end crowdsensing-as-a-service system dubbed CrowdPower. Our platform offers a standard interface for the management and brokerage of sensory data, enabling the transformation of raw sensory data into valuable smart city intelligence. Our solution includes a model for selecting participants for sensing campaigns based on the reliability and quality of sensors on users’ devices, then subsequently analysing the quality of the data provided using a clustering approach to predict user reputation and identify outliers. The platform also has an elaborate administration web portal developed to manage and visualize sensing activities. In addition to the architecture, design, and implementation of the backend platform capabilities, we also explain the creation of CrowdPower’s sensing mobile application that enables data collectors and consumers to participate in various sensing activities

A Novel Quality and Reliability-Based Approach for Participants\u27 Selection in Mobile Crowdsensing

© 2013 IEEE. With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants\u27 mobility, render the task of participants\u27 selection, a difficult task. In this paper, we propose a comprehensive and practical mobile crowdsensing recruitment model that offers reliability and quality-based approach for selecting the most reliable group of participants able to provide the best quality possible for the required sensory data. In our model, we adopt a group-based approach for the selection, in which a group of participants (gathered into sites) collaborate to achieve the sensing task using the combined capabilities of their smartphones. Our model was implemented using MATLAB and configured using realistic inputs such as benchmarked sensors\u27 quality scores, most widely used phone brands in different countries, and sensory data types associated with various events. Extensive testing was conducted to study the impact of various parameters on participants\u27 selection and gain an understanding of the compromises involved when deploying such process in practical MCS environments. The results obtained are very promising and provide important insights into the different aspects impacting the quality and reliability of the process of mobile crowdsensing participants\u27 selection