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
Among the innovative services provided by smart cities initiatives, emergencies management systems have stood out as a mean to prevent the occurrence of disasters in urban areas, detecting emergencies as soon as possible and triggering response actions. For that, such systems may rely on multiple emergencies detection units spread over a city, which will be used to detect abnormal situations and report them for further processing. Although the use of multi-sensors hardware units seems to be reasonable to detect a lot of emergency-related variables such as temperature, humidity, smoke, and toxic gases, cities may have different geographical zones concerning the potential negative impacts (risk) that an emergency may have until it is properly mitigated. Therefore, such risk associated to those zones should guide the deployment of emergencies detection units, but their computation is not straightforward and it may depend on different parameters. In this context, this paper proposes a mathematical model to compute mitigation zones in any city, taking as reference the availability of response centers retrieved from open geospatial databases, notably hospitals, fire departments, and police stations. An algorithm is defined to compute a critical index to each zone, which will be exploited to indicate the proportional number of detection units that should be allocated according to the total number of available units. Initial results for the city of Porto, Portugal, are presented, which are discussed when concerning the construction of practical emergencies management systems. © 2022 IEEE.

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.