Abstract
In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the MatSrn models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Abstract

Abstract

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
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
Production planning and scheduling in the process industry in general and in the pulp and paper (P&P) sector in particular can be very challenging. Most practitioners, however, address those activities relying only on spreadsheets, which is time-consuming and sub-optimal. The literature has reported some decision support systems (DSSs) that are far from the state-of-the-art with regard to optimization models and methods, and several research works that do not address industrial issues. We contribute to reduce that gap by developing and describing a DSS that resulted from several iterations with a P&P company and from a thorough review of the literature on process systems engineering. The DSS incorporates relevant industrial features (which motivated the development of a specific model), exhibits important technical details (such as the connection to existing systems and user-friendly interfaces) and shows how optimization can be integrated in real world applications, enhanced by key pre- and post-optimization procedures.