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.

Abstract
Nowadays achieving sustainable development is a global concern. Economic and environmental sustainability can be driven by assessing and improving industrial production system’s performance. An evaluation that assesses if materials, energy and resources are used to their full potential is a powerful tool for improving economic and environmental performance, and consequently supports the identification of all types of waste and inefficiencies along the production system. The goal of this work is to assess overall production system’s efficiency and eco-efficiency using Multi Layer Stream Mapping (MSM). The outputs of this approach is used to scrutinize “where” and “how much” can a unit process and/or a production system improve its financial, environmental and overall efficiency, thereby being of great importance for decision-making and correct implementation of improvement actions. This paper highlights the results from the application of the MSM methodology in a real industrial case regarding a painting unit.

Abstract

Abstract
Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is amajor concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.