Abstract

Abstract
Keywords: family business; internationalization; SMEs; family SMEs; international expansion; family ownership

Abstract
In this work, a colossal enhancement of strain sensitivities through the push-pull deformation method in interferometry is reported for the first time. For the demonstration of the new method, two cascaded interferometers in a fiber loop mirror are used. Usually, strain is applied at the fiber end of the interferometers. In this work, we propose applying strain at the middle of the two cascaded interferometers whereas the fiber ends of the sensor are fixed. Strain is then applied in the fusion region between the two-cascaded interferometers in a push-pull configuration, thus ensuring simultaneously the extension of one interferometer and the compression of the other. Although the carrier signal is maintained constant, the proposed technique induces a colossal enhancement of sensitivity in the envelope signal. Strain sensitivities up to 10000 pm/ $\mu \varepsilon $ are achieved.

Abstract
With the increasing adoption of teledermatology, there is a need to improve the automatic organization of medical records, being dermatological image modality a key filter in this process. Although there has been considerable effort in the classification of medical imaging modalities, this has not been in the field of dermatology. Moreover, as various devices are used in teledermatological consultations, image acquisition conditions may differ. In this work, two models (VGG-16 and MobileNetV2) were used to classify dermatological images from the Portuguese National Health System according to their modality. Afterwards, four incremental learning strategies were applied to these models, namely naive, elastic weight consolidation, averaged gradient episodic memory, and experience replay, enabling their adaptation to new conditions while preserving previously acquired knowledge. The evaluation considered catastrophic forgetting, accuracy, and computational cost. The MobileNetV2 trained with the experience replay strategy, with 500 images in memory, achieved a global accuracy of 86.04% with only 0.0344 of forgetting, which is 6.98% less than the second-best strategy. Regarding efficiency, this strategy took 56 s per epoch longer than the baseline and required, on average, 4554 megabytes of RAM during training. Promising results were achieved, proving the effectiveness of the proposed approach.

Abstract
In the last years, developments in data collection, storing, processing and analysis technologies resulted in an unprecedented use of data by organizations. The volume and variety of data, combined with the velocity at which decisions must now be taken and the dynamism of business environments, pose new challenges to Machine Learning. Namely, algorithms must now deal with streaming data, concept drift, distributed datasets, among others. One common task nowadays is to update or re-train models when data changes, as opposed to traditional one-shot batch systems, in which the model is trained only once. This paper addresses the issue of when to update or re-train a model, by proposing an approach to predict the performance metrics of the model if it were trained at a given moment, with a specific set of data. We validate the proposed approach in an interactive Machine Learning system in the domain of fraud detection. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Open innovation is proved to be determinant in the rationalization of sustainable innovation ecosystems. Firms, universities, governments, user communities and the overall environment are called to contribute to this dynamic process. This study aims to contribute to a better understanding of the impact of open innovation on firms' performance and to empirically assess whether university-industry collaborations are complementary or substitutes for this activity. Primary data were collected from a survey encompassing 908 firms, and then combined with performance indicators from SABI (Spanish and Portuguese business information). Econometric estimations were run to evaluate the role of open innovation and university-industry collaboration in the firm innovative propensity and performance. Results highlight the importance of diversity in collaborations with the academia and inbound open innovation strategy as enhancers of firm performance. The two activities reinforce each other. By testing the impact of open innovation practices on company performance, the need for heterogeneity in terms of contact type and university is also demonstrated. Findings cast light on the need to reformulate existing policy packages, reinforcing the ties with academia as well as the promotion of open innovation strategies. The connection to the innovation ecosystem needs to be further encouraged as well as the promotion of persistent connections with the knowledge sources in an open and multilateral framework.