Abstract
Business Continuity Management (BCM) encompasses effective planning to respond to business interruptions and relaunch business in the short term. This study follows the Design Science Research methodology and proposes a framework to systematise Business Continuity Management and streamline the Business Continuity Plan (BCP) design and implementation. The framework defines metrics providing strategical guidance and assessment of the Business Continuity Management initiatives. The framework provides a Business Continuity Management Model, an Implementation Guide, a Self-Assessment System, and a Measurement System. The model was developed based on a systematic literature review and guidelines from Business Continuity Management frameworks and standards. In the first iteration, we demonstrated and evaluated the framework through a Focus Group with experts in Business Continuity Management. In the second iteration, it was used and evaluated by professionals with responsibilities in Business Continuity Plan implementation, representing various business sectors. As a result, the framework is useful and complete, effective and enhances governance and is scalable and adaptable to organisations. This study concludes that the framework adds value to Business Continuity Management monitoring, gaps identification, and practitioner's guidance on what needs to be planned, done, checked and acted to manage continuity.

Abstract
The aim of this study is to analyze the typical process of the practical part of software development courses at universities and to evaluate whether the current process meets the expectations of students or whether the needs of students would be better met by the benefits of automated code feedback. A semiautomated survey was conducted in German, involving bachelor students from different universities who had attended an introductory programming class within the last 6 semesters. The results clearly show that the students would like to have individual assignments instead of the due to time constraints, usual group exercises as they see more advantages for their learning progress. The use of automated code feedback could not only solve this time problem but would also bring other benefits.

Abstract
Many working activities are carried out in office buildings that are located in urban areas. Several studies have shown that these workplaces are likely to present poor indoor environmental quality (IEQ) due to inadequate ventilation rates, compromised thermal comfort conditions, and/or high concentration of air pollutants, such as particulate matter. This study aimed to review ventilation conditions, based on carbon dioxide (CO2) concentrations, and indoor airborne particulate matter (PM2.5 and PM10) levels assessed in offices worldwide. The approach carried out in this work followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Six databases (Scopus, Web of Science, PubMed, Inspec, Science Direct, and Dimensions) were used to search for peer-reviewed articles on the subject of IEQ, in particular, those reporting data for the levels of CO2 and particulate matter in offices, published during the last decade. Firstly, 394 records were identified, resulting in 23 articles included in the review after the screening process and the implementation of eligibility criteria. Based on the results and considering the mean concentration reported, office environments present, in general, acceptable ventilation conditions (mean: 665 ppm). However, the few cases of studies that reported CO2 values exceeding 1000 ppm identified situations of high occupancy density and inadequate operation of heating, ventilation, and air conditioning (HVAC) systems as the unequivocal causative factors. In turn, PM2.5 and PM10 seemed to be IEQ parameters that are even more critical to be tackled in offices, with the reported overall mean values (36 and 63 mu g/m(3)) exceeding the current World Health Organization (WHO) guidelines (15 and 45 mu g/m(3)). The highest aerosol concentrations were typically found in naturally ventilated buildings and were mostly associated with the influence of high levels of particles introduced indoors through the outdoor air. Overall, measures for improving IEQ in offices toward promoting healthy and safe environments for workers include strategies to periodically control IEQ, ensure the adequate percentage of fresh air and maintenance of the mechanical ventilation systems (operation, maintenance, and air filtration efficiency), and adjust occupancy to the room dimensions and ventilation conditions.

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
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.