Moored current and temperature measurements in the Faroe Bank Channel overflow region, June 2012-June 2013

This data set includes measurements from moored instruments from the Faroe Bank Channel overflow region in the period between 28 May 2012 and 5 June 2013. The data set was collected under the project entitled "Faroe Bank Channel Overflow: Dynamics and Mixing Research", with an objective to describe the structure and variability of the dense oceanic overflow plume from the Faroe Bank Channel on daily to seasonal timescales. Mooring arrays were deployed in two sections: located 25 km downstream of the main sill, in the channel that geographically confines the overflow plume at both edges (section C), and 60 km further downstream, over the slope (section S). The measurements delivered with this data set include hourly-averaged data gridded on 5-m vertical separation, after accounting for mooring knock downs using a mooring dynamics model. Complete set of mooring drawings and detailed description can be found in the cruise report (Fer et al. 2016, PDF provided). The article by Ullgren et al. (2016) gives further details on processing of the data set and presents the data set.

Documentação automatizada de APIs com tutoriais gerados a partir do Stack Overflow

One of the most common forms of reuse is through API usage. However, one of the main challenges to effective usage is an accessible and easy to understand documentation. Several papers have proposed alternatives to make more understandable API documentation, or even more detailed. However, these studies have not taken into account the complexity of understanding of the examples to make these documentations adaptable to different levels of experience of developers. In this work we developed and evaluated four different methodologies to generate tutorials for APIs from the contents of Stack Overflow and organizing them according to the complexity of understanding. The methodologies were evaluated through tutorials generated for the Swing API. A survey was conducted to evaluate eight different features of the generated tutorials. The overall outcome of the tutorials was positive on several characteristics, showing the feasibility of the use of tutorials generated automatically. In addition, the use of criteria for presentation of tutorial elements in order of complexity, the separation of the tutorial in basic and advanced parts, the nature of tutorial to the selected posts and existence of didactic source had significantly different results regarding a chosen generation methodology. A second study compared the official documentation of the Android API and tutorial generated by the best methodology of the previous study. A controlled experiment was conducted with students who had a first contact with the Android development. In the experiment these students developed two tasks, one using the official documentation of Android and using the generated tutorial. The results of this experiment showed that in most cases, the students had the best performance in tasks when they used the tutorial proposed in this work. The main reasons for the poor performance of students in tasks using the official API documentation were due to lack of usage examples, as well as its difficult use.

Mentale Veerkracht als Buffer tegen Docenten Stress-Reactiviteit: een ESM-studie

Om meer inzicht te krijgen in factoren die positief bijdragen aan de stress-reactiviteit van de docent wordt in deze studie onderzocht in hoeverre mentale veerkracht en docenten eigen effectiviteitsverwachting (DEE) een bufferende werking hebben in de samenhang tussen dagelijkse gebeurtenissen met stress en het positief (PA) en negatief affect (NA). Deze kennis kan gebruikt worden op zowel preventief (selectie) als curatief niveau. Het onderzoek is gedaan onder 41 docenten (leeftijd M=40, SD=9.2) uit verschillende typen onderwijs. Middels de experience sample methode werden gedurende een week activiteitsgerelateerde, sociale en gebeurtenisgerelateerde stress en de emotionele reactiviteit, geobjectiveerd in veranderingen in PA en NA gemeten. Vooraf de onderzoeksweek vulden zij twee vragenlijsten in betreffende mentale veerkracht en DEE. Het meten van positief en negatief affect (PA en NA), activiteitsgerelateerde, sociale en gebeurtenisgerelateerde stress gebeurde door middel van de Experience Sample Methode, zowel via papieren boekje als digitaal (smartphone, tablet, computer). Mentale veerkracht is gemeten met de RS-NL, DEE door middel van een vertaalde vragenlijst van de Teacher Self-Efficacy Scale. Door middel van een multi-level regressieanalysie werden tussen de drie stressmaten en de afhankelijke variabelen PA en NA zijn significante samenhangen gevonden. Volgens de verwachting speelt mentale veerkracht een modererende rol in de samenhang tussen de stressmaten en NA. DEE speelt een modererende rol in de samenhang tussen sociale stress en PA.

Modelling the effectiveness of grass buffer strips in managing muddy floods under a changing climate

Muddy floods occur when rainfall generates runoff on agricultural land, detaching and transporting sediment into the surrounding natural and built environment. In the Belgian Loess Belt, muddy floods occur regularly and lead to considerable economic costs associated with damage to property and infrastructure. Mitigation measures designed to manage the problem have been tested in a pilot area within Flanders and were found to be cost-effective within three years. This study assesses whether these mitigation measures will remain effective under a changing climate. To test this, the Water Erosion Prediction Project (WEPP) model was used to examine muddy flooding diagnostics (precipitation, runoff, soil loss and sediment yield) for a case study hillslope in Flanders where grass buffer strips are currently used as a mitigation measure. The model was run for present day conditions and then under 33 future site-specific climate scenarios. These future scenarios were generated from three earth system models driven by four representative concentration pathways and downscaled using quantile mapping and the weather generator CLIGEN. Results reveal that under the majority of future scenarios, muddy flooding diagnostics are projected to increase, mostly as a consequence of large scale precipitation events rather than mean changes. The magnitude of muddy flood events for a given return period is also generally projected to increase. These findings indicate that present day mitigation measures may have a reduced capacity to manage muddy flooding given the changes imposed by a warming climate with an enhanced hydrological cycle. Revisions to the design of existing mitigation measures within existing policy frameworks are considered the most effective way to account for the impacts of climate change in future mitigation planning. 

Who will answer my question on Stack Overflow?

Stack Overflow is a highly successful Community Question Answering (CQA) service for software developers with more than three millions users and more than ten thousand posts per day. The large volume of questions makes it difficult for users to find questions that they are interested in answering. In this paper, we propose a number of approaches to predict who will answer a new question using the characteristics of the question (i.e. Topic) and users (i.e. Reputation), and the social network of Stack Overflow users (i.e. Interested in the same topic). Specifically, our approach aims to identify a group of users (candidates) who have the potential to answer a new question by using feature-based prediction approach and social network based prediction approach. We develop predictive models to predict whether an identified candidate answers a new question. This prediction helps motivate the knowledge exchanging in the community by routing relevant questions to potential answerers. The evaluation results demonstrate the effectiveness of our predictive models, achieving 44% precision, 59% recall, and 49% F-measure (average across all test sets). In addition, our candidate identification techniques can identify the answerers who actually answer questions up to 12.8% (average across all test sets).