Abstract
Purpose In the context of European consumers' experiences of austerity, this study aims to advance current resilience theory in marketing through developing persistent resilience from a context of austerity influenced consumption. Design/methodology/approach Following an interpretivist approach, 38 face to face, in-depth interviews were conducted with European consumers from Ireland, UK, Spain, Portugal, Italy and Greece who were affected in some way by the global financial crisis. Findings Building upon limited conceptual and empirical investigations in social geography, the analysis identifies the themes of persistent stressors and temporal orientation as constants, alongside day-to-day coping, relating and pragmatism, consumer adjustment, repertoires of resistance and transformation as key elements of persistent resilience within the consumption context of austerity. Research limitations/implications The study addresses the limited theoretical and empirical focus on persistent resilience and austerity and directly contributes to consumer behaviour and marketing theory in understanding persistent resilience and its implications. Practical implications Changes to behaviours as a result of persistent resilience included reducing and stopping consumption, discount shopping, alternative consumption in the form of growing or making and mindful consumption through wastage reduction and re-use. Social implications The study highlights the significant social impact of austerity while also identifying positive outcomes for social relations among family, friends and the wider community. Originality/value This study develops and extends Golubchikov's (2011) theory of persistent resilience through exploring European consumer responses to austerity, identifying key consumption characteristics relevant for marketing theory and practice.

Abstract
By means of a cross-cultural virtual teams project involving classrooms in Scotland, Germany, and Portugal, students were exposed to the challenges of collaborating internationally with the intention of increasing their intercultural competency. Intercultural sensitivity and intercultural communication competency were measured using responses to surveys before and after the 6-week project. Students reported, among other aspects, a heightened awareness of the difficulties of intercultural communication. Despite a general appreciation of the project and its outcomes, negative results, such as an increased dislike of intercultural interaction, emerged. Contradictory results warrant further investigation with data from future collaborations.

Abstract
This paper proposes the linear frequency cepstral coefficients as highly discriminative features for anomaly detection in ball bearings using vibration sensor data. These features are based on cepstral analysis and are capable of encoding the patterns of a spectral magnitude profile. Incipient damages on bearings can grow rapidly under normal use resulting in vibration and harsh noise. If left undetected, this damage will worsen, leading to high maintenance costs or even injury. Multiple interferences in an industrial environment contaminate the signal, making it a challenge to correctly identify the bearings' condition. Many studies have attempted to overcome this issue at the signal level. However, the discriminative capacity of the current vibration signal features is still vulnerable to interference, which motivates this work. In order to demonstrate the benefits of these features, we (1) show that they are computationally efficient and suitable for real-time incremental training; (2) conduct discriminative analysis by evaluating the separability performance and comparing it with the state of the art; and (3) test the robustness of the proposed features under noise interference, which is ideal for use in the harsh operating conditions of industrial machinery. The data was obtained from a laboratory workbench setting that reproduces bearing fault scenarios. Results show that the proposed features are fast, competitive when compared to state-of-the-art features, and resilient to high levels of interference. Despite the higher performance when using the quadratic model, the proposed features remain highly discriminative when used with several other discriminant function.

Abstract
The dynamic behavior of urban mobility patterns makes matching taxi supply with demand as one of the biggest challenges in this industry. Recently, the increasing availability of massive broadcast GPS data has encouraged the exploration of this issue under different perspectives. One possible solution is to build a data-driven real-time taxi-dispatching recommender system. However, existing systems are based on strong assumptions such as stationary demand distributions and finite training sets, which make them inadequate for modeling the dynamic nature of the network. In this paper, we propose BRIGHT: a drift-aware supervised learning framework which aims to provide accurate predictions for short-term horizon taxi demand quantities through a creative ensemble of time series analysis methods that handle distinct types of concept drift. A large experimental set-up which includes three real-world transportation networks and a synthetic test-bed with artificially inserted concept drifts, was employed to illustrate the advantages of BRIGHT when compared to S.o.A methods for this problem.

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient's attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features.

Abstract
Building upon the success of the first edition, we organize the second edition of the Text2Story Workshop on Narrative Extraction from Texts in conjunction with the 41 st European Conference on Information Retrieval (ECIR 2019) on April 14, 2019. Our objective is to further consolidate the efforts of the community and reflect upon the progress made since the last edition. Although the understanding of natural language has improved over the last couple of years – with research works emerging on the grounds of information extraction and text mining – the problem of constructing consistent narrative structures is yet to be solved. It is expected that the state-of-the-art has been advancing in pursuit of methods that automatically identify, interpret and relate the different elements of narratives which are often spread among different sources. In the second edition of the workshop, we foster the discussion of recent advances in the link between Information Retrieval (IR) and formal narrative representations from text. © Springer Nature Switzerland AG 2019.