Abstract
The architectural pattern of micro-services is being increasingly adopted by developers, facilitating the maintenance and scalability of the systems’ code. The adoption and consumption of these micro-services are often seen on the front-end code of the Web applications. Nevertheless, this adoption obliges web designers/developers to know where to look for those web services, to read their documentation and to write the request/response code as well to control the corresponding UI rendering. This whole process is time-consuming and error-prone. This article introduces SeCoGen as an interactive code generator for Web service parsing and consumption. The generator benefits from an HTTP request template, a query normalizer and dynamic UI templates. In order, to validate the generator feasibility and usefulness, a REST API to search for countries is used. © Ricardo Queirós.

Abstract
In our previous editorial, we positioned our perspective and introduced the acronym “ROTRUS” to characterise the science of managing for innovation as – Real world, Observable, Testable, Replicable, Uncertain and Social. Specifically, we argued that methods that draw on point-in-time beliefs, perceptions and de-humanised data in a complex and evolving social setting of innovation management pose a challenge for replicability. We warned innovation researchers to avoid the pitfalls that might foster pseudoscience and generalised assumptions from information that is still in the proto-science stage. Drawing on longstanding understanding in psychology of the whole human, we discussed the need to explore methods that capture brain, mind and behaviour aspects in innovation management, spanning the analysis from individual to group and societal levels. In this editorial, we move the discussions forward by focusing on one plausible methodological approach to advance the science of managing for innovation – behavioural experiments. In the following sections, we explain our methodological stance or in other words our world view followed by a brief review of behavioural experiments and their relevance to innovation research. We conclude with a foreword on our final editorial in the series titled the science of managing for innovation. (...)

Abstract
Innovation capabilities are important for firms to compete in the market. However, the literature has rarely examined how exploitative and exploratory innovation influences the export performance of small and medium-sized firms (SMEs). As exploitative and exploratory innovation plays different roles in sustaining SMEs' competitive advantages, this article presents an analysis of how four specific firms' innovation capabilities (i.e. marketing, strategy, research and development and manufacturing capabilities) impact these SMEs' export performance. Moreover, this study analysed how exploitative and exploratory innovation capabilities mediate the relationship of the four firms' internal innovation capabilities and export performance. The results indicate that exploitative innovation positively influences SMEs' export performance, but exploratory innovation does not. Another interesting finding is that strategy and manufacturing capabilities are important antecedents of both exploratory and exploitative innovation. Furthermore, the results reveal that only manufacturing capabilities have a direct impact on export performance, whereas strategy and manufacturing capabilities are the antecedents that most influence exploitative innovation and export performance.

Abstract
Atmospheric profiling is a requirement for controlling wide-field adaptive optics (AO) instruments, analysing the AO performance with respect to the observing conditions and predicting the point spread function (PSF) spatial variations. We present PEPITO, a new concept for profiling atmospheric turbulence from post facto tip-tilt (TT) corrected shortexposure images. PEPITO utilizes the anisokinetism effect in the images between several stars separated from a reference star, and then produces the profile estimation using a model-fitting methodology, by fitting to the long-exposure TT-corrected PSF. PEPITO has a high sensitivity to bothC2 n(h) and L0(h) by relying on the full telescope aperture and a large field of view(FOV). It then obtains a high vertical resolution (1-400 m) configurable by the camera pixel scale, taking advantage of fast statistical convergence (of the order of tens of seconds). With only a short-exposure capable large format detector and a numerical complexity independent of the telescope diameter, PEPITO perfectly suits accurate profiling for night optical turbulence site characterization or AO instruments operations. We demonstrate, in simulation, that the C2 n(h) and L0(h) can be estimated to better than 1 per cent accuracy, from fitted PSFs of magnitude V = 11 on a D = 0.5m telescope with a 10 arcmin FOV.

Abstract
Researchers are currently encouraged by their institutions and the funding agencies to deposit data resulting from projects. Activities related to research data management, namely organization, description, and deposit, are not obvious for researchers due to the lack of knowledge on metadata and the limited data publication experience. Institutions are looking for solutions to help researchers organize their data and make them ready for publication. We consider here the deposit process for a CKAN-powered data repository managed as part of the IT services of a large research institute. A simplified data deposit process is illustrated here by means of a set of examples where researchers describe their data and complete the publication in the repository. The process is organised around a Dublin Core-based dataset deposit form, filled by the researchers as preparation for data deposit. The contacts with researchers provided the opportunity to gather feedback about the Dublin Core metadata and the overall experience. Reflections on the ongoing process highlight a few difficulties in data description, but also show that researchers are motivated to get involved in data publication activities.

Abstract
Advances in genomics have driven to the recognition that tumours are populated by different minor subclones of malignant cells that control the way the tumour progresses. However, the spatial and temporal genomic heterogeneity of tumours has been a hurdle in clinical oncology. This is mainly because the standard methodology for genomic analysis is the biopsy, that besides being an invasive technique, it does not capture the entire tumour spatial state in a single exam. Radiographic medical imaging opens new opportunities for genomic analysis by providing full state visualisation of a tumour at a macroscopic level, in a non-invasive way. Having in mind that mutational testing of EGFR and KRAS is a routine in lung cancer treatment, it was studied whether clinical and imaging data are valuable for predicting EGFR and KRAS mutations in a cohort of NSCLC patients. A reliable predictive model was found for EGFR (AUC = 0.96) using both a Multi-layer Perceptron model and a Random Forest model but not for KRAS (AUC = 0.56). A feature importance analysis using Random Forest reported that the presence of emphysema and lung parenchymal features have the highest correlation with EGFR mutation status. This study opens new opportunities for radiogenomics on predicting molecular properties in a more readily available and non-invasive way. © 2019, Springer Nature Switzerland AG.