Abstract
The proper use of resources in an industrial environment is crucial for the sustainability of the industry and the planet. As the supply chain is important for improving companies' environmental performance, this article measures the impact of green supply chain management (GSCM) practices on the environmental performance of companies in the plastics industry in Portugal. Environmental culture, environmental uncertainty, competitive pressure, ecocentricity, social responsibility in procurement and value chain traceability were used to moderate the relationship between GSCM and environmental performance. Using seven regression models via partial least squares structural equation modeling, it was possible to test the six moderators referred to above. The results demonstrate that GSCM practices have a positive impact on companies' environmental performance; however, the moderating effects tested proved not to be statistically significant. The immediate consequences for firms are clear: it is mandatory for them to implement an environmental, ecocentric culture if they want to manage the socio-environmental challenges of procurement and the traceability of the supply chain; only then will they be able to deal with both environmental uncertainty and the competitive pressures of supply chain sustainability. The biggest current challenges lie in the rational use of plastic resources and their reuse, either by the industry or by the end user, in terms of conscientious consumption and correct routing for their reuse. According to the natural resource-based theory, it is possible to claim that firms have capabilities but lack the resources to cope with environmental challenges they are facing to properly internalize the changes and to implement them across the supply chain.

Abstract
Customer retention is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused on predictive analytics, neglecting the business domain. This work aims to present an actionable knowledge discovery based on specific, actionable attributes and measuring of their effects. It is common to use matching, and propensity score approaches in healthcare to evaluate causality. After performing matching using the actionable attributes in this analysis, the causal effect is quantified. This work concludes that the difference between having a yearly contract versus having a monthly contract affects the churn of around 34%.

Abstract
Accurate yield estimation is of utmost importance for the entire grape and wine production chain, yet it remains an extremely challenging process due to high spatial and temporal variability in vineyards. Recent research has focused on using image analysis for vineyard yield estimation, with one of the major obstacles being the high degree of occlusion of bunches by leaves. This work uses canopy features obtained from 2D images (canopy porosity and visible bunch area) as proxies for estimating the proportion of occluded bunches by leaves to enable automatic yield estimation on non-disturbed canopies. Data was collected from three grapevine varieties, and images were captured from 1 m segments at two phenological stages (veraison and full maturation) in non-defoliated and partially defoliated vines. Visible bunches (bunch exposure; BE) varied between 16 and 64 %. This percentage was estimated using a multiple regression model that includes canopy porosity and visible bunch area as predictors, yielding a R-2 between 0.70 and 0.84 on a training set composed of 70 % of all data, showing an explanatory power 10 to 43 % higher than when using the predictors individually. A model based on the combined data set (all varieties and phenological stages) was selected for BE estimation, achieving a R-2 = 0.80 on the validation set. This model did not show validation metrics differences when applied on data collected at veraison or full maturation, suggesting that BE can be accurately estimated at any stage. Bunch exposure was then used to estimate total bunch area (tBA), showing low errors (< 10 %) except for the variety Arinto, which presents specific morphological traits such as large leaves and bunches. Finally, yield estimation computed from estimated tBA presented a very low error (0.2 %) on the validation data set with pooled data. However, when performed on every single variety, the simplified approach of area-to-mass conversion was less accurate for the variety Syrah. The method demonstrated in this work is an important step towards a fully automated non-invasive yield estimation approach, as it offers a solution to estimate bunches that are not visible to imaging sensors.

Abstract
As the penetration levels of renewable energy sources increase and climatic changes produce more and more extreme weather conditions, the uncertainty of weather and power production forecasts can no longer be ignored for grid operation and electricity market bidding. In order to support the energy industry in the integration of uncertainty forecasts into their business practices, this work describes an experiment conducted with 105 participants from the energy industry. In the framework of an IEA Wind Task 36 workshop, the experiment aimed to investigate existing psychological barriers in the industry to adopt probabilistic forecasts and to better understand human decision processes. We designed and ran a 'decision game' to demonstrate the potential benefits of uncertainty forecasts in a realistic-although simplified-problem, where an energy trader had to decide whether to trade 100% or 50% of the energy of an offshore wind park on a given day based on deterministic and probabilistic uncertainty day-ahead forecasts. The focus thus was on a decision-making process dealing with extremes that can cause high costs in the form of security issues in the electric grid for system operators, or high monetary losses for traders, who have bid a power production into the market that failed to be produced due to high-speed shutdown of the wind turbines. This paper presents the obtained results, extracts behavioural conclusions and identifies how to overcome psychological barriers to the adoption of uncertainty forecasts in the energy industry.

Abstract
During the last decades, local, regional, and national governments promoted the development of smart cities, aiming the integration of traditional urban infrastructures and information technologies to provide high quality and sustainable urban services. Smart cities' implementations may change the way the individuals experience the urban spaces. Looking specifically to older adults, smart cities' applications have the potential of promoting their autonomy, independence, safety, well-being, social participation, and inclusion. This paper presents a survey of the scientific literature aiming to analyse current evidence related to smart cities' applications to support older adults and to identify issues for future research.

Abstract