Abstract
Fiber Bragg grating is widely used in optical fiber applications as a filter or a sensor due to its compact size and high sensitivity to physical conditions, such as temperature and strain. The purpose of this chapter is to describe the implementation and characterization of two tuning methods for optical fiber Bragg gratings, varying the temperature or the length of the fiber. Among the methods using mechanical deformation, compression of the fiber by bending a flexible sheet aggregated with the Bragg grating has shown very interesting tuning results, reaching 19.0 nm with minimum reflection bandwidth variation over the entire tuning range. Stretching the fiber has presented several drawbacks, including breaking of the fiber and a lower tuning range of 4.9 nm. Temperature tuning technique presents good linearity between tuning range and temperature variation but at the cost of a low tuning range (0.4 nm) and a permanent high current electrical source.

Abstract
In this article the stage is set for storytelling, ethnographic narratives, and a look at the literature on innovators and anti-innovators. Envy is seen to be a central element in the discussion. Political skills, on the other hand, may diminish the negative effects which may have ensued, implemented by insecure leaders in view of subordinates with leadership potential. Hence, leadership, and national culture, as well as organizational culture play an essential role in the innovativeness of organizations. Extremely creative individuals - essential in the global marketplace - may be ostracized by co-workers who feel that they lack in abilities, by direct comparison. Being clever (or having a high intelligence quotient - IQ - being able to reason and solve diverse types of problems) in itself has no effect on political ability. Knowledge of oneself (capabilities and personality) is essential in combatting anti-innovators (note that one may change and improve, by learning, over time). Emotional intelligence (ability to self-manage oneself, emotionally, including how one communicates and empathizes with others), not surprisingly, is very important. Finally, showing and feeling anxious is bad in the fight against anti-innovators.

Abstract
Presentation attacks are recurrent threats to biometric systems, where impostors attempt to bypass these systems. Humans often use background information as contextual cues for their visual system. Yet, regarding face-based systems, the background is often discarded, since face presentation attack detection (PAD) models are mostly trained with face crops. This work presents a comparative study of face PAD models (including multi-task learning, adversarial training and dynamic frame selection) in two settings: with and without crops. The results show that the performance is consistently better when the background is present in the images. The proposed multi-task methodology beats the state-of-the-art results on the ROSE-Youtu dataset by a large margin with an equal error rate of 0.2%. Furthermore, we analyze the models' predictions with Grad-CAM++ with the aim to investigate to what extent the models focus on background elements that are known to be useful for human inspection. From this analysis we can conclude that the background cues are not relevant across all the attacks. Thus, showing the capability of the model to leverage the background information only when necessary.

Abstract
Despite the wide variety of literature on the impact of the COVID-19 pandemic in the service industry, there is still a lack of an integrated systematized view of these multiple impacts. This study contributes to service research by identifying a variety of academic and managerial perspectives about the influence of COVID-19. We pay attention to the service industry, but with an especial focus on the tourism and hospitality industries, which have been more severely affected. This paper presents two multi-approach studies blending a systematic literature review (SLR) and a focus group methodology. Hence, it integrates and synthesizes the main results of the two studies considered to assist researchers and practitioners. It offers a complete overview of the state of the art and identifies three key service trends that have been accelerated by COVID-19: (1) the increasingly digital and autonomous customer; (2) the growing potential of data-driven services versus privacy concerns, and (3) the evolution from firm-centric to customer-centric and networked business models. Finally, this study provides relevant theoretical implications where we suggest relevant theories, constructs, and methodologies for future research to advance the current knowledge, and useful guidelines for business managers to better understand how to respond to market changes.

Abstract
Many techniques have contributed to the advancement of automated program repair, such as: generate and validate approaches, constraint-based solvers and even neural machine translation. Simultaneously, artificial intelligence has allowed the creation of general-purpose pre-trained models that support several downstream tasks. In this paper, we describe a technique that takes advantage of a generative model - CodeGPT - to automatically repair buggy programs by making use of its code completion capabilities. We also elaborate on where to perform code completion in a buggy line and how we circumvent the open-ended nature of code generation to appropriately fit the new code in the original program. Furthermore, we validate our approach on the ManySStuBs4j dataset containing real-world open-source projects and show that our tool is able to fix 1739 programs out of 6415 - a 27% repair rate. The repaired programs range from single-line changes to multiple line modifications. In fact, our technique is able to fix programs which were missing relatively complex expressions prior to being analyzed. In the end, we present case studies that showcase different scenarios our technique was able to handle.

Abstract
The increased use of active power electronics for AC to DC conversion of electrical equipment causes emissions in the frequency range between 2-150kHz (Supraharmonic frequency range). Currently two different methods (IEC61000-4-30 and IEC61000-4-7) to measure supraharmonic emissions are used. In this study, the two methods are compared to each other by means of real measured signals. While other studies used synthesized signals, this study investigates real measured signals of a bi-directional electric vehicle (EV) charging station and a photovoltaic (PV) power plant. The signals indicate narrowband, wideband and time-varying emissions. The method described in IEC61000-4-30 Ed.3 offers benefits analyzing wideband emissions due to reduced data. Nevertheless, as the method is non-gapless, big deviations to IEC61000-4-7 and to a gapless method occur. Especially at time-varying signals the deviation reaches up to 50%. The method according to IEC61000-4-30 Ed.3 shows good results for constant level of emissions only. In reality, this is rarely the case, as emissions often get superimposed by other signals. The IEC 61000-4-7 method generates many data, but allows detailed investigations of narrowband emissions and as the measurement is gap-less time-varying emissions are detected properly. © 2022 IEEE.