Abstract
Wildfire management has been struggling in recent years with escalating devastation, expenditures, and complexity. Given the copious factors involved and the complexity of their interactions, uncertainty in the outcomes is a prominent feature of wildfire management strategies, at both policy and operational levels. Improvements in risk handling and in risk-based decision support tools have therefore a key role in addressing these challenges. In this paper, we review key systems created to support wildfire management decision-making at different levels and scales, and describe their evolution from an initial focus on landscape-level fire growth simulation and burn probability assessment, to the incorporation of exposure and economic loss potential (allowing the translation of ignition likelihood, fire environment terrain, fuels, and weather and suppression efficacy into potential fire effects), the integration with forest management and planning, and more recently, to developments in the assessment of values at risk, including real-time assessment. This evolution is linked to a progressive widening of the scope of usage of these systems, from an initial more limited application to risk assessment, to the subsequent inclusion of functionality enabling their Utilization in the context of risk management, and more recently, to their explicit casting in the broader societal context of risks and decisions, from a risk governance perspective. This joint evolution can be seen as the result of a simultaneous pull from methodological progresses in risk handling, and push from technological progress in wildfire management decision support tool, as well as more broadly in computational power. We identify the key benefits and challenges in the development and adoption of these systems, as well as future plausible research trends.

Abstract
Entities that participate in the same supply chain or that can complement each other can benefit from reductions in their operational costs and increased operating efficiency when collaborating by sharing information and resources or engaging in joint planning. However, setting up successful collaborations is still an active research topic, particularly in the forest sector, due to the lack of a clearly defined road map for bringing collaboration into practice. This paper builds on a comprehensive literature review to propose a framework for developing inter-firm collaboration, particularly for agents of forest-based supply chains. The framework encompasses identification of collaboration opportunities with potential entities, design of the collaborative strategy, and tools for its implementation. The framework is used to discuss cases of collaboration found in the literature. The framework is also applied to developing a new collaborative process in the wood furniture industry.

Abstract
This work describes an in-fiber Michelson interferometer inclinometer which is sensitive to curvature applied in the tapered region. The performance of this inclinometer is evaluated by calculating the variation of the fringe visibility near the 1550 nm spectral range as a function of the tilt angle. It is presented the results of four experimental measurements and calculated the average and standard deviation of those measurements. The results indicate a good response of the sensor within the angular range between 3 degrees and 6 degrees. The average of those four measurements is around -0.15/degrees and the greatest standard deviation is about 5.5%.

Abstract
Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented. © 2014 IEEE.

Abstract
The present invention is directed to manufacturing, wherein each component of the outsourced process and system can be remotely controlled in decentralized manufacturing process, integrating resources and stakeholders in a global chain, that utilizes ubiquitous computing systems virtual and networked enterprises concepts, for anywhere-anytime control. Experiment involved 68 subjects, Serbian students that have used the interface for remote collaborative control to control CNC machine located in Portugal. Usability evaluation measures of the distributed remote user controlled manufacturing system, such as percentage of task completed, its accuracy and time to complete task, are measured on two types of "client" user interface ("Wall" and "Window"), in two modes of presentation (desktop and video beam), working individually or in small collaborative group consisted of two persons, results were statistically tested. Percentage of task completed is equal for both types of interfaces, while accuracy of task is significantly better for "Wall" interface. Time of task execution is longer for individuals when desktop is used comparing to video beam. There exist significant differences when work is conducted in groups via video beam and time is longer when desktop is used. Also, work time is longer with "Wall" interface. When working in group, work time is shorter than the time when working individually, whereby the working time on desktop is longer than on the video beam. Also, the time is longer when working individually using video beam, using "Wall" interface on desktop, and "Window" interface on video beam. These results show that group work consumes less time in most working options, giving the best results when working in collaborative small group on "Wall" interface via video beam. (C) 2015 The Authors. Published by Elsevier B.V.

Abstract
The linear inverted pendulum model has been used predominantly to generate balanced humanoid walking. This model assumes that the hip height is fixed during the walk. In this paper, generating a fast walk is studied with the main focus on the effect of hip height movement. Our approach is based on modeling the hip height movement and learning its parameters in order to generate a fast walk. The hip height trajectory is generated using Fourier basis functions. The generated trajectory is the input to programmable Central Pattern Generators (CPGs) in order to modulate generated trajectories smoothly. The inverted pendulum model is utilized to model a balanced walking. A numerical approach is presented to control inverted pendulum dynamics. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is employed to search for appropriate hip height trajectory and walking parameters that optimize walking speed. This approach has been tested not only to obtain fast forward walk but also a fast side walk. Experiments are conducted on both simulated and real NAO robots. The results show that the change from the learned forward walk to learned side walk is performed stably, which confirm the important role of using CPGs. The comparison of the results of the proposed gait model (and development approach) with those obtained using fixed hip height also shows that fixed height walking is slower than variable height walking.