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A new fermionic formula for the unrestricted Kostka polynomials of type $A_{n-1}^{(1)}$ is presented. This formula is different from the one given by Hatayama et al. and is valid for all crystal paths based on Kirillov-Reshetihkin modules, not just for the symmetric and anti-symmetric case. The fermionic formula can be interpreted in terms of a new set of unrestricted rigged configurations. For the proof a statistics preserving bijection from this new set of unrestricted rigged configurations to the set of unrestricted crystal paths is given which generalizes a bijection of Kirillov and Reshetikhin.Comment: 35 pages; reference adde

The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for predicting τ over a range of physical parameters of porous media and fluid that affect the flow. The data employed for training the ANN algorithm have been acquired from previous modeling studies. It is observed that ANN modeling can appropriately characterize the relationship between the changes in the media and fluid properties, thereby ensuring a reliable prediction of the dynamic coefficient as a function of water saturation. Our results indicate that a double-hidden-layer ANN network performs better in comparison to the single-hidden-layer ANN models for the majority of the performance tests carried out. While single-hidden-layer ANN models can reliably predict complex dynamic coefficients (e.g. water saturation relationships) at high water saturation content, the double-hidden-layer neural network model outperforms at low water saturation content. In all the cases, the single- and double-hidden-layer ANN models are better predictors in comparison to the regression models attempted in this work.

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The safety, mobility, environmental, energy, and economic benefits of transportation systems, which are the focus of recent Connected Vehicles (CVs) programs, are potentially dramatic. However, realization of these benefits largely hinges on the timely integration of the digital technology into the existing transportation infrastructure. CVs must be enabled to broadcast and receive data to and from other CVs (Vehicle-to-Vehicle, or V2V, communication), to and from infrastructure or V2I, communication) and to and from other road users, such as bicyclists or pedestrians (Vehicle-to-Other road users communication). Further, for V2I-focused applications, the infrastructure and the transportation agencies that manage it must be able to collect, process, distribute, and archive these data quickly, reliably, and securely. This paper focuses V2I applications, and studies current digital roadway infrastructure initiatives. It highlights the importance of including digital infrastructure investment alongside investment in more traditional transportation infrastructure to keep up with the auto industry's push towards connecting vehicles to other vehicles. By studying the current CV testbeds and Smart City initiatives, this paper identifies digital infrastructure components (i.e., communication options and computing infrastructure) being used by public agencies. It also examines public agencies' limited budgeting for digital infrastructure, and finds current expenditure is inadequate for realizing the potential benefits of V2I applications. Finally, the paper presents a set of recommendations, based on a review of current practices and future needs, designed to guide agencies responsible for transportation infrastructure. It stresses the importance of collaboration for establishing national and international platforms for the planning, deployment, and management of digital infrastructure to support connected transportation systems across political jurisdictions. potential for vehicles to "talk" to each other (via Vehicle-to-Vehicle communication, or V2V), to pedestrians (via Vehicle-to-Pedestrian communication, or V2P) as well as to transportation infrastructure (via Vehicle-to-Infrastructure communication, or V2I). Potential benefits from real-time communication between the elements of the transportation system are dramatic (Chang et al., 2015, He et al., 2012. For example, Connected Vehicles, or CVs (also referred to as "Vehicles with Connectivity"), which broadcast their data to infrastructure and other vehicles, could give drivers advance warning of impending collisions in time to avert dangerous circumstances, dramatically reducing crash damage, injuries, and fatalities. V2I connectivity between vehicles and "digital roadways," which feature roadside devices and backend computation infrastructure, could ensure safe and efficient traffic management in real time, which is not present on public roads today. CVs can benefit the environment with 9,400 tons of annual emission savings for an area covering 45 kilo-meters (28...

The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily 'transferable' as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in "learning" the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that the accuracy is much better than other methods.

ABSTRACT. NHS Trusts in England must adopt appropriate levels of continued investment in routine and backlog maintenance if they are to ensure critical backlog does not accumulate. This paper presents the current state of critical backlog maintenance within the National Health Service (NHS) in England through the statistical analyses of 115 Acute NHS Trusts. It aims to find empirical support for a causal relationship between building portfolio age and year-on-year increases in critical backlog. It makes recommendations for the use of building portfolio age in strategic asset management. The current trend across this sample of NHS Trusts may be typical of the whole NHS built asset portfolio and suggests that most Trusts need to invest between 0.5 and 1.5 per cent of income (depending upon current critical backlog levels and Trust age profile) to simply maintain critical backlog levels. More robust analytics for building age, condition and risk-adjusted backlog maintenance are required.

This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods.