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

Abstract
Purpose The purpose of this paper is to better understand the role that leadership plays in the success of Lean management (LM) implementation, by trying to identify what is the impact of the transactional, transformational, directive and empowering leadership styles on the success of such an implementation in Portuguese companies, and what are the most important leaders' attributes. Design/methodology/approach An on-line questionnaire was distributed to 65 manufacturing and services Portuguese organizations that have implemented LM. Findings The results suggest that the empowering leadership style has a positive impact on the success of LM implementation. Even though results do not allow concluding about the impact of the other styles, several leader's attributes were identified as having influence: individualized consideration, information sharing, skill development, intellectual stimulation, assigned goals and self-directed decision making. Originality/value Very few studies have addressed the role of leadership in the success of adopting LM and, to the best knowledge, only one paper studied the critical attributes of leaders in LM implementation. Moreover, the present study focuses in Portugal, country where this topic has rarely been investigated.

Abstract
Quantitative assessment of mitral valve (MV) morphology is important for diagnosing MV pathology and for planning of reparative procedures. Although this is typically done using 3D transesophageal echocardiography (TEE), recent advances in the spatiotemporal resolution of 3D transthoracic echocardiography (TTE) have enabled the use of this more patient friendly modality. However, manual data analysis is time consuming and operator dependent. In this study, a fully automatic method for MV segmentation and tracking in 3D TTE is proposed and validated. The proposed framework takes advantage of a previously proposed left ventricle (LV) segmentation framework to localize the MV and performs segmentation based on the B-spline Explicit Active Surfaces (BEAS) framework. The orientation of the MV is obtained and the MV surface is cropped to the mitral annulus (MA) and divided into posterior and anterior leaflets. The segmented MV at end diastole (ED) is propagated to end systole (ES) using localized anatomical affine optical flow (lAAOF). Because the orientation and leaflet division is known, relevant clinical parameters can then be extracted from the mesh at any time point. The proposed framework shows excellent segmentation results with a mean absolute distance (MAD) and Hausdorff distance (HD) of 1.19 +/- 0.25 mm and 5.79 +/- 1.25 mm at ED and 1.39 +/- 0.32 mm and 6.70 +/- 1.97 mm at ES against manual analysis. In conclusion, an automatic method for MV segmentation is proposed which could provide valuable clinical information in a more patient-friendly manner.

Abstract
Panoramic or aerial images can be acquired with some easiness and cover vast tracts of territory to be used in fire detection. The analysis of these images, in particular based on color and threshold indices, can be very interesting computationally when applied in real time systems and collected, for example, through drones or watchtowers. This paper presents a solution designated Color Algorithm for Flame Exposure (CAFE), which significantly improves an existing method (cf. Forest Fire Detection Index - FFDI) in flame detection, based on daylight images, in mixed Mediterranean landscape, containing vegetation, buildings, burning areas, land, etc. The CAFE approach, presented, adds a parameterizable transformation of the image into the Lab color space. This approach was tested in four distinct scenarios, significantly reducing false positives and maintaining an equivalent level of false negatives when compared to the FFDI approach.