Abstract

Abstract
During the health crisis caused by COVID-19, virtual reality (VR) proved to be useful for the tourism industry, allowing this industry to continue working despite the restrictions imposed. However, it remains to be seen if the impact of this sanitary crisis in the tourism industry influenced managers' intention to adopt this technology in the post-pandemic period. To fill this gap, a qualitative methodological approach was adopted, using the MAXQDA20 software and interviews with managers of tourism enterprises. The results show that the willingness to invest in technology, the perception of VR as a business strategy, and the perception of the impact of the pandemic are factors that regulate the intention of companies to adopt VR. In addition, prior experience with VR and the perception of technical support are also important for its adoption. Thus, it was concluded that VR can be a valuable sustainable strategy for tourism companies to address the challenges imposed by the pandemic. However, adopting the technology depends on factors such as financial availability, business strategy, and previous experience with VR. Furthermore, tourism companies must also receive adequate technical support to ensure its correct implementation.

Abstract

Abstract
Measuring gas and liquid flow rate is paramount in various scientific and industrial applications. This work presents an optical fiber flowmeter based on a graphene oxide (GO) coated Michelson interferometer. The interferometer is fabricated using a long-period fiber grating (LPFG) followed by a GO-coated single-mode fiber (SMF). By radiating the GO coating, it experiences photothermic effect that induces local heating of the film. This results in a variation in the effective refractive index in the cladding modes, which induces a phase shift on the interferometer spectrum. When a gas flow is introduced near the coated fiber, the hot-wire region will experience a reduction in temperature proportional to the flow rate. The flowmeter exhibited a linear wavelength shift to the flow rate with an absolute sensitivity of 17.4 +/- 0.8 pm/(L.min-1) for gas flow rates between 2 and 8 L/ min. Furthermore, the dynamic response of the sensor was studied, attaining a maximum response time of 1.1 +/- 0.4 s

Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph is among the first-generation instruments for ESO’s 39m Extremely Large Telescope (ELT). It will provide diffraction-limited spectroscopy and imaging, including coronagraphic capabilities, in the thermal/mid-infrared wavelength domain (3 µm – 13.3 µm). Its Single Conjugate Adaptive Optics (SCAO) system will be used for all observing modes, with High Contrast Imaging imposing the most demanding requirements on its performance. The final design review of METIS took place in the fall of 2022; the development of the instrument, including its SCAO system, has since entered the Manufacturing, Assembly, Integration and Testing (MAIT) phase. Numerous challenging aspects of an ELT AO system are addressed in the mature designs for the SCAO control system and the SCAO hardware module: the complex interaction with the telescope entities that participate in the AO control, wavefront reconstruction with a fragmented and moving pupil, secondary control tasks to deal with differential image motion, non-common path aberrations and mis-registration. A K-band pyramid wavefront sensor and a GPU-based RTC, tailored to needs of METIS at the ELT, are core components. The implementation of the METIS SCAO system includes thorough testing at several levels before the installation at the telescope. These tests require elaborate setups to mimic the conditions at the telescope. This paper provides an overview of the design of METIS SCAO as it will be implemented, the main results of the extensive analyses performed to support the final design, and the next steps on the path towards commissioning.

Abstract
Strategic term re-writing and attribute grammars are two powerful programming techniques widely used in language engineering. The former relies on strategies to apply term re-write rules in defining large-scale language transformations, while the latter is suitable to express context-dependent language processing algorithms. These two techniques can be expressed and combined via a powerful navigation abstraction: generic zippers. This results in a concise zipper-based embedding offering the expressiveness of both techniques. Such elegant embedding has a severe limitation since it recomputes attribute values. This paper presents a proper and efficient embedding of both techniques. First, attribute values are memoized in the zipper data structure, thus avoiding their re-computation. Moreover, strategic zipper based functions are adapted to access such memoized values. We have implemented our memoized embedding as the Ztrategic library and we benchmarked it against the state-of-the-art Strafunski and Kiama libraries. Our first results show that we are competitive against those two well established libraries. © 2023 ACM.