Abstract
Ever since the arrival of streaming platforms, society has become more focused on understanding how to take the next step forward in terms of innovation. Streaming is now a new reality that is growing by the second, and businesses have begun to spend more money on streaming services in order to adapt and improve their products for this new era. This is also the case of Disney+, released at the end of 2019, which had an immediate unexpected growth upon its launch. In comparison to other streaming platforms, Disney+ had an easier debut because of its wide range of unique material available to watch. This study explores Disney+ as a brand, a streaming platform, its features and how they compare to others, as well as the company's business model and revenue, all of which contributed to Disney+ 's current position among other streaming platforms. Regarding Disney+, a survey with 84 responses was conducted, and the results were analysed using descriptive and inferential (chi-square independence test) statistics. As the value of the calculated test statistic (11.24) (chi-square test) is higher than the value in the chi-square table, we conclude that there is a statistically significant association between age and being influenced by advertising.

Abstract
This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

Abstract
In the published article, there was an error in Table 2 as published. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm when they should be in cm. The corrected Table 2 and its caption appear below. In the published article, there was an error in Table 3 as published. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm. The correct unit is cm. The corrected Table 3 and its caption appear below. In the published article, there was an error in Figure 3 as published. The units of the Total body center of mass sway in x-axis were shown in mm in the vertical axis of the plot. The correct unit is cm. The corrected Figure 3 and its caption appear below. In the published article, there was an error in Supplementary Table S.I. The units of the Total body center of mass sway in x-axis (TBCMx) and y-axis (TBCMy) were shown in mm. The correct unit is cm. The correct material statement appears below. In the published article, there was a mistake on the computation description of one of the assessed parameters (total body center of mass). A correction has been made to “Data Processing,” Paragraph 3: “For each gait cycle, we computed the 24 spatiotemporal and kinematic gait parameters listed in Table 2 and defined in (15). The total body center of mass (TBCM) sway was computed as the standard deviation of the distance (in the x/y-axis, i.e., medial-lateral and vertical directions) of the total body center of mass (TBCM), in relation to the RGBD sensor’s coordinate system, for all gait cycle frames. For each frame, TBCM’s position is the mean position of all body segments’ CM, which was obtained according to (21).” The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated. © 2022 Vilas-Boas, Rocha, Cardoso, Fernandes, Coelho and Cunha.

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.