Abstract
Linking an expert to his knowledge areas is still a challenging research problem. The task is usually divided into two steps: identifying the knowledge areas/topics in the text corpus and assign them to the experts. Common approaches for the expert profiling task are based on the Latent Dirichlet Allocation (LDA) algorithm. As a result, they require pre-defining the number of topics to be identified which is not ideal in most cases. Furthermore, LDA generates a list of independent topics without any kind of relationship between them. Expert profiles created using this kind of flat topic lists have been reported as highly redundant and many times either too specific or too general. In this paper we propose a methodology that addresses these limitations by creating hierarchical expert profiles, where the knowledge areas of a researcher are mapped along different granularity levels, from broad areas to more specific ones. For the purpose, we explore the rich structure and semantics of Heterogeneous Information Networks (HINs). Our strategy is divided into two parts. First, we introduce a novel algorithm that can fully use the rich content of an HIN to create a topical hierarchy, by discovering overlapping communities and ranking the nodes inside each community. We then present a strategy to map the knowledge areas of an expert along all the levels of the hierarchy, exploiting the information we have about the expert to obtain an hierarchical profile of topics. To test our proposed methodology, we used a computer science bibliographical dataset to create a star-schema HIN containing publications as star-nodes and authors, keywords and ISI fields as attribute-nodes. We use heterogeneous pointwise mutual information to demonstrate the quality and coherence of our created hierarchies. Furthermore, we use manually labelled data to serve as ground truth to evaluate our hierarchical expert profiles, showcasing how our strategy is capable of building accurate profiles.

Abstract
This paper gives a comprehensive and coherent view on permutability in the intuitionistic sequent calculus with cuts. Specifically we show that, once permutability is packaged into appropriate global reduction procedures, it organizes the internal structure of the system and determines fragments with computational interest, both for the computation-as-proof-normalization and the computation-as-proof-search paradigms. The vehicle of the study is a ?-calculus of multiary proof terms with generalized application, previously developed by the authors (the paper argues this system represents the simplest fragment of ordinary sequent calculus that does not fall into mere natural deduction). We start by adapting to our setting the concept of normal proof, developed by Mints, Dyckhoff, and Pinto, and by defining natural proofs, so that a proof is normal iff it is natural and cut-free. Natural proofs form a subsystem with a transparent Curry- Howard interpretation (a kind of formal vector notation for -terms with vectors consisting of lists of lists of arguments), while searching for normal proofs corresponds to a slight relaxation of focusing (in the sense of LJT). Next, we define a process of permutative conversion to natural form, and show that its combination with cut elimination gives a concept of normalization for the sequent calculus. We derive a systematic picture of the full system comprehending a rich set of reduction procedures (cut elimination, flattening, permutative conversion, normalization, focalization), organizing the relevant subsystems and the important subclasses of cut-free, normal, and focused proofs. © José Espírito Santo, Maria João Frade, and Luís Pinto; licensed under Creative Commons License CC-BY 22nd International Conference on Types for Proofs and Programs (TYPES 2016).

Abstract
In this work, fungi present in the grapevine's phyllosphere collected from the main demarcated wine regions of Portugal were identified, and their phylogenetic relationships were analysed. A total of 46 vine samples (leaves and berries) were collected from different parts of the country, being isolated a total of 117 fungal colonies that were identified to the genus level and sequenced in the following genetic regions: internal transcribed spacer region and 18S rRNA and -tubulin gene. Next, a phylogenetic tree reconstruction for each genetic region was built. The isolates retrieved from environmental samples belonged to the genera Alternaria (31%), Cladosporium (21%), Penicillium (19%), Aspergillus (7%) and Epicoccum (3%). No genetic signatures of exchange of genetic material were detected, and consequently, the reconstructed phylogenetic trees allowed to distinguish between these different species/genera. In the fungal composition of the Vitis vinifera phyllosphere, several potential pathogens were identified that can be associated with decreases in crop productivity. Knowledge of fungi identification and genetic diversity is pivotal for the development of more adequate crop management strategies. Furthermore, this information will provide guidelines for a more specific and wiser use of fungicides. Significance and Impact of the StudyThe knowledge on the composition of the phyllosphere microbial community is still limited, especially when fungi are concerned. These micro-organisms not only play a crucial role in crop health and productivity but also interact with the winemaking process, determining the safety and quality of grape and grape-derived products. The elucidation of the micro-organisms present in the phyllosphere will have a notorious impact on plant breeding and protection programmes and disease management strategies, allowing a better control of pesticide applications.

Abstract
The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a mediumsized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NOx, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from -15 to 49% for CO, -14 to 31% for VOC, -19 to 46% for NOx and -22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.

Abstract
This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), mostly intermittent in nature, might have on the reliability performance of distribution networks. A test distribution system based on a typical MV/LV urban distribution network in the UK is fully modelled and controlled to investigate the potential benefits that local renewables and energy storage can offer to the quality of power supply to customers. In this analysis, the conventional Monte Carlo method is further developed to include the time-variation of electricity demand profiles and failure rates of network components. Additionally, a theoretical interruption model is employed to assess more accurately the moment in time when interruptions to electricity customers are likely to occur. Accordingly, the impact of the spatio-temporal variation of DERs, with photovoltaic (PV) systems as key enablers, is quantified in terms of the effect of network outages. A range of smart grid functionalities is analysed and their benefits are assessed through standard reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions.

Abstract
This paper considers the problem of allocating vehicles to collect and distribute fruit to producer associations in Colombia. In particular, the problem seeks to determine the optimal allocation of vehicles for fruit collection minimising both total transportation costs and CO2 emissions. This problem has multiple objectives, and the well-known e-constraint method has been used as solution technique for the proposed mathematical models. The efficiency of the former methodology has been tested by using a case study involving the distribution of blackberry (Rubus glaucus) by an association of producers in Cundinamarca Department, Colombia. In particular, we considered 12 different scenarios related to supply levels, route outsourcing, and collection frequency. The results show the efficiency of the proposed methodology in solving vehicle allocation problems related to collection and distribution. The case study reveals that, in general, collecting fruit three days/week yields lower costs and fewer emissions than performing collections four days/week. Furthermore, increased supply leads to greater differences between costs and emissions.