Abstract
The vast majority of studies in meta-learning uses only few performance measures when characterizing different machine learning algorithms. The measure Adjusted Ratios of Ratio (ARR) addresses the problem of how to evaluate the quality of a model based on the accuracy and training time. Unfortunately, this measure suffers from a shortcoming that is described in this paper. A new solution is proposed and it is shown that the proposed function satisfies the criterion of monotonicity, unlike ARR.

Abstract
Background: Transmission models can aid understanding of disease dynamics and are useful in testing the efficiency of control measures. The aim of this study was to formulate an appropriate stochastic Susceptible-Infectious-Resistant/ Carrier (SIR) model for Salmonella Typhimurium in pigs and thus estimate the transmission parameters between states. Results: The transmission parameters were estimated using data from a longitudinal study of three Danish farrow-to-finish pig herds known to be infected. A Bayesian model framework was proposed, which comprised Binomial components for the transition from susceptible to infectious and from infectious to carrier; and a Poisson component for carrier to infectious. Cohort random effects were incorporated into these models to allow for unobserved cohort-specific variables as well as unobserved sources of transmission, thus enabling a more realistic estimation of the transmission parameters. In the case of the transition from susceptible to infectious, the cohort random effects were also time varying. The number of infectious pigs not detected by the parallel testing was treated as unknown, and the probability of non-detection was estimated using information about the sensitivity and specificity of the bacteriological and serological tests. The estimate of the transmission rate from susceptible to infectious was 0.33 [0.06, 1.52], from infectious to carrier was 0.18 [0.14, 0.23] and from carrier to infectious was 0.01 [0.0001, 0.04]. The estimate for the basic reproduction ration (R-0) was 1.91 [0.78, 5.24]. The probability of non-detection was estimated to be 0.18 [0.12, 0.25]. Conclusions: The proposed framework for stochastic SIR models was successfully implemented to estimate transmission rate parameters for Salmonella Typhimurium in swine field data. R0 was 1.91, implying that there was dissemination of the infection within pigs of the same cohort. There was significant temporal-cohort variability, especially at the susceptible to infectious stage. The model adequately fitted the data, allowing for both observed and unobserved sources of uncertainty (cohort effects, diagnostic test sensitivity), so leading to more reliable estimates of transmission parameters.

Abstract
In traffic & transportation system analysis the way individuals make choices plays a paramount role as these will affect the general efficiency with which people can travel. Modifications on the system by means of policy intervention affect commuters' perspective impacting on the performance of the network and eventually on the society's welfare. The emergence of system's behaviour, as a result of decisions at individual level, provides the traffic manager with the opportunity to evaluate modifications that have been implemented on the system. However, there has been a slow advance in appropriately representing users and their behaviour in all social dimensions of intelligent transportation systems. This paper discusses on a social-oriented modelling & simulation framework for Artificial Transportation Systems, which accounts for different social dimensions of the system in the assessment and application of policy procedures. We illustrate how a social agent-based model can be a useful tool to test the appropriateness and efficiency of transportation policies.

Abstract
Mobility and commuting in metropolitan areas are very expensive, highly polluted and time wasting. The Four Step Model (FSM) is the key model to analyze a Transportation Network. However, being the FSM a combination of several models, combining them in one model have rarely been applied. To deal with this problem an Agent-Based Model (ABM) is proposed. An ABM uses the metaphor of autonomous agents and so, they can be a handful tool for combining different models in one. Therefore, this model can be used as a tool for simulation and integrate the FSM in one model. Here we present the preliminary results of this approach. (C) 2014 The Authors. Published by Elsevier B.V.

Abstract
Background: BioTextRetriever is a Web-based search tool for retrieving relevant literature in Molecular Biology and related domains from MEDLINE. The core of BioTextRetriever is the dynamic construction of a classifier capable of selecting relevant papers among the whole MEDLINE bibliographic database. “Relevant” papers, in this context, means papers related to a set of DNA or protein sequences provided as input to the tool by the user. Methods: Since the number of retrieved papers may be very large, BioTextRetriever uses a novel ranking algorithm to retrieve the most relevant papers first. We have developed a new methodology that enables the automation of the assessment process based on a multi-criteria ranking function. This function combines six factors: MeSH terms, paper’s number of citations, author’s h-index, journals impact factor, author number of publications and journal similarity function. Results: The best results highlight the number of citations and the h-index factors. Conclusions: We have developed and a multi-criteria ranking function, that contemplates six factors, and that seems appropriate to retrieve relevant papers out of a huge repository such as MEDLINE. © 2014, Gonçalves et al.; licensee Springer.

Abstract
Operational planning at public transport companies is a complex process that usually comprises several phases. In the planning phase, schedules are constructed considering that buses arrive and depart as scheduled. Obviously, several disruptions frequently occur, but their impact on the operating conditions is not easy to estimate. This difficulty arises mostly due to the impossibility of testing different solutions under the same conditions. Indeed, typically, the available data are a result of the current plan, while new proposed solutions have not produced real data yet. Along this chapter we discuss the assessment of the impact of changes in the operational planning on the real operating conditions, before their occurrence. We present a framework for such assessment, which includes two components: the impact on costs, and the impact on revenues. We believe that this framework will be useful in future works on operational planning of public transport companies. © Springer International Publishing Switzerland 2014.