19,147 research outputs found
First experiences with Personal Networks as an enabling platform for service providers
By developing demonstrators and performing small-scale user trials, we found various opportunities and pitfalls for deploying personal networks (PNs) on a commercial basis. The demonstrators were created using as many as possible legacy devices and proven technologies. They deal with applications in the health sector, home services, tourism, and the transportation sector. This paper describes the various architectures and our experiences with the end users and the technology. We conclude that context awareness, service discovery, and content management are very important in PNs and that a personal network provider role is necessary to realize these functions under the assumptions we made. The PNPay Travel demonstrator suggests that PN service platforms provide an opportunity to develop true trans-sector services
CMD: A Multi-Channel Coordination Scheme for Emergency Message Dissemination in IEEE 1609.4
In the IEEE 1609.4 legacy standard for multi-channel communications in
vehicular ad hoc networks(VANETs), the control channel (CCH) is dedicated to
broadcast safety messages while the service channels (SCH's) are dedicated to
transmit infotainment service content. However, the SCH can be used as an
alternative to transmit high priority safety messages in the event that they
are invoked during the service channel interval (SCHI). This implies that there
is a need to transmit safety messages across multiple available utilized
channels to ensure that all vehicles receive the safety message. Transmission
across multiple SCH's using the legacy IEEE 1609.4 requires multiple channel
switching and therefore introduces further end-to-end delays. Given that safety
messaging is a life critical application, it is important that optimal
end-to-end delay performance is derived in multi-channel VANET scenarios to
ensure reliable safety message dissemination. To tackle this challenge, three
primary contributions are in this article: first, a channel coordinator
selection approach based on the least average separation distance (LAD) to the
vehicles that expect to tune to other SCH's and operates during the control
channel interval (CCHI) is proposed. Second, a model to determine the optimal
time intervals in which CMD operates during the CCHI is proposed. Third, a
contention back-off mechanism for safety message transmission during the SCHI
is proposed. Computer simulations and mathematical analysis show that CMD
performs better than the legacy IEEE 1609.4 and a selected state-of-the-art
multi-channel message dissemination schemes in terms of end-to-end delay and
packet reception ratio.Comment: 15 pages, 10 figures, 7 table
Does hospital competition save lives? Evidence from the English NHS patient choice reforms
This paper examines whether or not hospital competition in a market with fixed reimbursement prices can prompt improvements in clinical quality. In January 2006, the British Government introduced a major extension of their market-based reforms to the English National Health Service. From January 2006 onwards, every patient in England could choose their hospital for secondary care and hospitals had to compete with each other to attract patients to secure their revenue. One of the central aims of this policy was to create financial incentives for providers to improve their clinical performance. This paper assesses whether this aim has been achieved and competition led to improvements in quality. For our estimation, we exploit the fact that choice-based reforms will create sharper financial incentives for hospitals in markets where choice is geographically feasible and that prior to 2006, in the absence of patient choice, hospitals had no direct financial incentive to improve performance in order to attract more patients. We use a modified difference-in-difference estimator to analyze whether quality improved more quickly in more competitive markets after the government introduced its new wave of market-based reforms. Using AMI mortality as a quality indicator, we find that mortality fell more quickly (i.e. quality improved) for patients living in more competitive markets after the introduction of hospital competition in January 2006. Our results suggest that hospital competition in markets with fixed prices can lead to improvements in clinical quality
Exploration of Adoption of Service Innovations Through Technology Road-Mapping: Case of Location Based Services
Exploration of Adoption of Service Innovations through Technology Road-Mapping: Case of Location Based Services: 10.4018/jssmet.2010040105: This paper utilizes a technology road-mapping approach to demonstrate how a traditional technology management process can be applied to improve plannin
Assessing Treatment Access for Adult Sex Offenders under Community Supervision in Kentucky: An Exploratory Study
A thesis presented to the faculty of the Caudill College of Humanities at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Arts by Lincoln Bradley Sloas on November 24, 2008
Prediction of User Throughput in the Mobile Network Along the Motorway and Trunk Road
The main goal of this research is to create a machine learning model for predicting user throughput in the mobile 4G network of the network provider M:tel Banja Luka, Bosnia and Herzegovina. The geographical area of the research is limited to the section of Motorway "9th January" (M9J) Banja Luka - Doboj, between the node Johovac and the town of Prnjavor (P-J section), and the area of the section of trunk road M17, between the node Johovac and the town of Doboj (J-D section). Based on the set of collected data, several models based on machine learning techniques were trained and tested together with the application of the Correlation-based Feature Selection (CFS) method to reduce the space of input variables. The test results showed that the models based on k-Nearest Neighbors (k-NN) have the lowest relative prediction error, for both sections, while the model created for the trunk road section has significantly better performance
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the methodology, a single decision tree, bootstrap-aggregated
(bagged) decision trees, random forest, boosted decision trees, and artificial
neural network for regression have been adapted and systematically compared
using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride hailing
service provider in China. In the current study, 199,584 time-slots describing
the spatio-temporal ride-hailing demand has been extracted with an
aggregated-time interval of 10 mins. All the methods are trained and validated
on the basis of two independent samples from this dataset. The results revealed
that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
while avoiding the risk of over-fitting, followed by artificial neural network
(20.09), random forest (23.50), bagged decision trees (24.29) and single
decision tree (33.55).Comment: Currently under review for journal publicatio
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