Abstract
The interrogation of optical fiber sensors (OFS) often relies on complex devices such as optical spectrum analyzers (OSAs) that are expensive with low portability and mainly suited to laboratory measurements or dedicated interrogation systems with limited spectral range. An interrogation unit was designed and fabricated using a photodetector combined with a micro-electromechanical system and a Fabry-Perot interferometer (MEMS-FPI) working as a tunable filter with a response in the range 1350-1650 nm. Deconvolution techniques were applied to mitigate the effect of the broadband response of the tunable filter on the measured signal. The performance of the unit was validated with the interrogation of long-period fiber gratings (LPFGs) as temperature, refractive index (RI), and relative humidity (RH) sensors. For the temperature, a sensitivity of 0.135 +/- 0.007 nm/degrees C was obtained, which showed a 4.9% relative error when compared to the same measurement with an OSA. For the RI, a sensitivity of 147 +/- 11 nm/RIU was obtained, which showed a relative error lower than 1% when compared to the OSA. For the humidity, sensitivities of 0.742 +/- 0.005 and 0.056 +/- 0.006 nm/%RH were obtained, with errors of 2.75% and 6.67%, respectively, when compared to a commercial dedicated interrogation system. The low relative error obtained when compared to commercial alternatives shows the potential of the system to be used in real-time applications that require portability, low cost, energy efficiency, and capacity for integration in dedicated systems.

Abstract

Abstract
The Digital Twin concept involves the transition to digital representations of factory floor equipment, the computerized simulation of processes and the visualization of data in real time. This type of digital transformations can be considered radical, encountering barriers in its implementation either due to resistance to change by the different elements that make up the industry or due to the disruption it can cause in the production process. The start of production on an assembly line is usually preceded by a checking procedure of parameters/conditions of the equipment present on the assembly line, using a sheet of paper containing the list of items to check and validate. In this article we describe the adoption of a paperless checklist to verify the configuration of assembly line equipment at production bootstrapping. A training program to coach the employees for a successful digital transition is also presented and discussed. Both the digital checklist and the training program are validated in a real-world industrial scenario. The results highlight the advantages of the digital approach given to the checklist with a multi-access viewing and maintenance of data for later analysis, with the training plan demonstrating effectiveness in breaking down barriers and resistance to the adoption of a new working method.

Abstract
This study intends to understand whether hotels should choose to surprise through a discount or a surprise gift. The experiment consisted in identifying whether there were differences in satisfaction and delight, according to the associated treatment (no surprise, surprise discount, or gift). With this purpose, a fictional hotel website was created for participants to simulate a reservation. Through the analysis of the experiment, the impact of surprise on customer satisfaction was confirmed. It was also found that, in the hospitality industry, a gift has a higher impact on satisfaction than a discount. When analyzing the guest delight, the results differ from what is stipulated in the literature (which points to the significant impact of surprise in this measure). It was concluded that between the two promotion tools, only the gift can significantly increase customer delight. This study demonstrates the importance of understanding the concept of surprise according to different industries. It also points to the importance of identifying the best methods to surprise customers, as different methods may lead to different results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
(1) Background: In this study on Robotic Process Automation (RPA), the feasibility of sustainable RPA implementation was investigated, considering user requirements in the context of this technology's stakeholders, with a strong emphasis on sustainability. (2) Methods: A multi-objective mathematical model was developed and the Weighted Sum and Tchebycheff methods were used to evaluate the efficiency of the implementation. An enterprise case study was utilized for data collection, employing investigation hypotheses, questionnaires, and brainstorming sessions with company stakeholders. (3) Results: The results underscore the significance of user requirements within the RPA landscape and demonstrate that integrating these requirements into the multi-objective model enhances the implementation assessment. Practical guidelines for RPA planning and management with a sustainability focus are provided. The analysis reveals a solution that reduces initial costs by 21.10% and allows for an efficient and equitable allocation of available resources. (4) Conclusion: This study advances our understanding of the interplay between user requirements and RPA feasibility, offering viable guidelines for the sustainable implementation of this technology.

Abstract
Extreme weather events, such as windstorms, hurricanes, and heat waves, exert a significant impact on global natural catastrophes and pose substantial challenges for weather forecasting systems. To enhance the accuracy and preparedness for extreme weather events, this study explores the potential of using expert crowdsourcing in storm forecasting research through the application of stigmergic collaboration. We present the development and implementation of an expert Crowdsourcing for Semantic Annotation of Atmospheric Phenomena (eCSAAP) system, designed to leverage the collective knowledge and experience of meteorological experts. Through a participatory co-creation process, we iteratively developed a web-based annotation tool capable of capturing multi-faceted insights from weather data and generating visualizations for expert crowdsourcing campaigns. In this context, this article investigates the intrinsic coordination among experts engaged in crowdsourcing tasks focused on the semantic annotation of extreme weather events. The study brings insights about the behavior of expert crowds by considering the cognitive biases and highlighting the impact of existing annotations on the quality of data gathered from the crowd and the collective knowledge generated. The insights regarding the crowdsourcing dynamics, particularly stigmergy, offer a promising starting point for utilizing stigmergic collaboration as an effective coordination mechanism for weather experts in crowdsourcing platforms but also in other domains requiring expertise-driven collective intelligence.