Promoting the Sustainable Recovery of Hospitality in the Post-Pandemic Era: A Comparative Study to Optimize the Servicescapes.

As COVID-19 spread throughout the world, the hospitality and tourism sectors were hard hit as no other industry. For this reason, the UNWTO developed the One Planet Vision as a response to a sustainable recovery of the tourism sector. At present, when people are starting to travel and stay at hotels again, it is important to analyze what their expectations are of hotels to move forward in the post-pandemic era. For instance, empirical research has been developed to examine people's sentiments toward servicescapes, and a comparative study is presented between 2020 and 2022. Findings contribute to the research by identifying new servicescape attributes during a health crisis. These also lead to practical implications by proposing a scale to evaluate customers' perceptions and to increase their wellbeing and resilience. The current research is one of the first studies to collaborate with the One Planet Vision by empirically proposing improvements in the servicescapes of hotels for a responsible recovery.

Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires.

Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires.

Condiciones de trabajo en las paseras: Caso Denominación de Origen Montilla-Moriles (España) / Working conditions in "paseras" (raisin-making operations): A case study of Denomination of Origin Montilla-Moriles (Spain)

Se caracterizan las condiciones de trabajo y sus posibles efectos en la salud de trabajadores(as) en el proceso de pasificación de la uva blanca, en la zona de la Denominación de Origen de Montilla-Moriles, con énfasis en los aspectos ergonómicos y de la seguridad. Se desarrolló un estudio de campo de nivel descriptivo, bajo un enfoque de investigación cualitativa. De una población laboral de 50 personas (40 hombres y 10 mujeres) se escogió una muestra intencional de 31 trabajadores (29 hombres y 2 mujeres). Para la recogida de la información se recurrió a la observación no participante, la entrevista colectiva, el esquema corporal y la guía de observación modificada del “Método DÉPARIS”. Los aspectos considerados como peligrosos (“insatisfactorios”) por los hombres y las mujeres fueron la adecuación al trabajo, la iluminación, el ambiente térmico, los peligros químicos-biológicos y las posiciones de trabajo. La condición más exigente la constituyen las elevadas temperaturas al laborar a la intemperie. Los hombres además incorporaron los riesgos de accidentes, las herramientas y el material de trabajo y la manipulación de cargas. El trabajo en paseras es estacional, por lo tanto, los trabajadores no tienen estabilidad laboral. Los trabajadores manifestaron dolores músculo-esquelético y lumbar. En conclusión, el trabajo de las paseras se corresponde con una labor física pesada y rutinaria, pudiendo llegar a ser monótona; los trabajadores están expuestos a posturas incómodas susceptibles de ocasionarle daños crónicos o discapacitantes de la columna lumbar.

Working conditions of raisin-making and their possible effects on worker health were evaluated in the white grape raisin industry in the area of the Denomination of Origin of Montilla-Moriles (Spain), with an emphasis on ergonomics and safety. This was a descriptive study, focused on the use of qualitative methods. From a workforce of 50 persons (40 men and 10 women), we selected a purposive sample of 31 (29 men and 2 women). Information was collected using non-participant observation, collective interviews, body discomfort diagrams and a modified observation guide based on the Déparis method. Work aspects considered hazardous ("unsatisfactory") by men and women included lack of work adaptation, lighting conditions, heat extremes, chemical and biological hazards and certain jobs. The most demanding conditions were heat extremes and outdoor work. Men also mentioned accident risks, unsafe tools and work materials, and manual material handling. Raisin-making is a seasonal job, so workers have no job security. Workers described low back and other musculoskeletal pain. In summary, raisin-making is associated with routine and heavy physical labor, and can become monotonous. Workers are exposed to awkward postures that can cause chronic or disabling injury to the lumbar spine.