Abstract
Virtual Reality (VR) has been evolving over the years, becoming more and more accessible, in a wide area of applications. One of these areas where VR can have a major impact is training and certification. Hydrogen vehicles are becoming a reality and first responders still lack proper tools and resources to train emergency responses for the purpose. VR can play here a crucial role in ensuring a proper hydrogen emergency response training due to the advantages associated with VR training programs such as resource optimization, repeatability, and replicability. This paper proposes using VR for hydrogen emergency response training by developing a solution composed of three components: tutorial mode, training mode, and certification mode. A usability study is further conducted to evaluate its usability and user satisfaction. The results show that the use of this application regards usability and user satisfaction were extremely positive.

Abstract
This manuscript presents a novel framework, the Multidimensional Design Assessment Model, which encompasses a multi-criteria approach to efficiency, eco-efficiency and costs assessment for a given design system in aeronautical industry production. The framework is established by adopting Design-for-X and Multi-Layer Stream Mapping approaches, based on Lean Thinking, for efficiency assessment and adopting modules of ecoPROSYS to eco-efficiency assessment. A real case study from aeronautical sector is given to demonstrate the approach, for the assembly of aircraft structure Horizontal Tale Plane, where different results are presented and discussed for each dimension of analysis and how improvement strategies can be designed.

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Abstract
Unmanned aerial vehicles have become a popular remote sensing platform for agricultural applications, with an emphasis on crop monitoring. Although there are several methods to detect vegetation through aerial imagery, these remain dependent of manual extraction of vegetation parameters. This article presents an automatic method that allows for individual tree detection and multi-temporal analysis, which is crucial in the detection of missing and new trees and monitoring their health conditions over time. The proposed method is based on the computation of vegetation indices (VIs), while using visible (RGB) and near-infrared (NIR) domain combination bands combined with the canopy height model. An overall segmentation accuracy above 95% was reached, even when RGB-based VIs were used. The proposed method is divided in three major steps: (1) segmentation and first clustering; (2) cluster isolation; and (3) feature extraction. This approach was applied to several chestnut plantations and some parameterssuch as the number of trees present in a plantation (accuracy above 97%), the canopy coverage (93% to 99% accuracy), the tree height (RMSE of 0.33 m and R-2 = 0.86), and the crown diameter (RMSE of 0.44 m and R-2 = 0.96)were automatically extracted. Therefore, by enabling the substitution of time-consuming and costly field campaigns, the proposed method represents a good contribution in managing chestnut plantations in a quicker and more sustainable way.

Abstract
Forecasting is an important task across several domains. Its generalised interest is related to the uncertainty and complex evolving structure of time series. Forecasting methods are typically designed to cope with temporal dependencies among observations, but it is widely accepted that none is universally applicable. Therefore, a common solution to these tasks is to combine the opinion of a diverse set of forecasts. In this paper we present an approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. We present an approach for retrieving out-of-bag predictions that significantly improves its data efficiency. Finally, since diversity is a fundamental component in ensemble methods, we propose a method for explicitly handling the inter-dependence between experts when aggregating their predictions. Results from extensive empirical experiments provide evidence of the method's competitiveness relative to state of the art approaches. The proposed method is publicly available in a software package.

Abstract