Abstract
Several fisheries across the world are managed by a quota regime. These quotas can be set yearly, monthly, weekly or daily. However, for some fish species demand seasonality may occur, which should be taken into consideration in the establishment of the quota (especially in those fisheries managed by daily or monthly quotas). This would allow fishermen to catch more fish at times of the year with higher demand in detriment of periods when demand is low. The present work investigates the existence of demand seasonality for bivalves from the artisanal dredge fleet. This fleet operates along the entire coast of the Portugal mainland. The analysis of fleets' revenue efficiency is assessed with Data Envelopment Analysis models, and the monthly seasonality effects on the revenue efficiency were tested using a Tobit regression. The results revealed that on the South coast there is a strong demand in the summer whereas on the western coast (northwest and southwest fishing areas) demand increases during Christmas and New Year festivities. Since this fishery is managed by weekly/daily quotas, it is proposed that these quotas should be redistributed in order to adjust them to periods of higher demand, thereby increasing the profitability of the vessels. The approach followed could be applied to similar fisheries worldwide.

Abstract

Abstract
In Portugal the distribution of physicians is considered an appropriate proxy for the distribution of the actual hospital resources and additional information on hospital supply is mostly unavailable, while health care utilization data are also usually absent. A suitable method that can be used to analyze patients' access to hospital health care in countries with such characteristics is the two-step floating catchment area (2SFCA) method, since it requires only the number of physicians to represent supply and the population size to estimate demand. An improved version of the 2SFCA method is the kernel density 2SFCA (KD2SFCA) method. However, this method was not developed to analyze access to health care and it computes scores that express only the spatial access dimensions of proximity and availability. In this paper we present a new method, based on the KD2SFCA method, which improves health care access analysis and better identifies populations that are less empowered to use health care. We adapt the KD2SFCA method for the context of health care access analysis and extend it to capture additional access dimensions. We applied the extended method to the Portuguese hospital health care sector in a case study, and compared its results with those obtained with the KD2SFCA method. Our method was able to improve the identification of the less empowered populations and discovered that they represent 8.1% of the total population, instead of 4.6%, and reside in sixteen of the eighteen Portuguese districts, instead of in thirteen, as identified by the original KD2SFCA method. By improving the KD2SFCA method for the identification of the less empowered populations, our method can be a first step in an endeavor to identify opportunities to increase the health care supply or to redistribute supply resources, with the objective of increasing the access of those deprived populations.

Abstract
The rapid growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills to find the required information and more sophisticated tools that are able to generate apt recommendations. Markov Chains have been widely used to generate next-page recommendations; however, accuracy of such models is limited. Herein, we propose the novel Semantic Variable Length Markov Chain Model (SVLMC) that combines the fields of Web Usage Mining and Semantic Web by enriching the Markov transition probability matrix with rich semantic information extracted from Web pages. We show that the method is able to enhance the prediction accuracy relatively to usage-based higher order Markov models and to semantic higher order Markov models based on ontology of concepts. In addition, the proposed model is able to handle the problem of ambiguous predictions. An extensive experimental evaluation was conducted on two real-world data sets and on one partially generated data set. The results show that the proposed model is able to achieve 15-20% better accuracy than the usage-based Markov model, 8-15% better than the semantic ontology Markov model and 7-12% better than semantic-pruned Selective Markov Model. In summary, the SVLMC is the first work proposing the integration of a rich set of detailed semantic information into higher order Web usage Markov models and experimental results reveal that the inclusion of detailed semantic data enhances the prediction ability of Markov models.

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.

Abstract
In this paper, we presented a probabilistic framework to predict Bus Bunching (BB) occurrences in real-time. It uses both historical and real-time data to approximate the headway distributions on the further stops of a given route by employing both offline and online supervised learning techniques. Such approximations are incrementally calculated by reusing the latest prediction residuals to update the further ones. These update rules extend the Perceptron's delta rule by assuming an adaptive beta value based on the current context. These distributions are then used to compute the likelihood of forming a bus platoon on a further stop - which may trigger an threshold-based BB alarm. This framework was evaluated using real-world data about the trips of 3 bus lines throughout an year running on the city of Porto, Portugal. The results are promising.