Careful review of published evidence has led to the postulate that the degree of lumbar lordosis may possibly influence the development and progression of spinal osteoarthritis, just as misalignment does in other joints. Spinal degeneration can ensue from the asymmetrical distribution of loads. The resultant lesions lead to a domino- like breakdown of the normal morphology, degenerative instability and deviation from the correct configuration. The aim of this study is to investigate whether a relationship exists between the sagittal alignment of the lumbar spine, as it is expressed by lordosis, and the presence of radiographic osteoarthritis.

Responsible tourism practices promote the idea that responsibility refers to both visitors and local communities, ensuring the protection and conservation of the natural environment and cultural heritage and contributing to a better quality of life. The role of “sustainability” in a tourist development strategy is to protect and reveal what already exists (“reuse, recycle, renovate”) rather than consume. In these terms, cultural routes can serve as valuable tourism assets that can attract visitors and generate economic activity, while protecting the environmental and cultural heritage. This study is focused on Symi, a small and remote Greek island better known as a religious tourism destination, examining how cultural routes could be at the core of an economic development and social cohesion strategic plan. Symi served as a case study due to the island’s rich cultural and natural heritage, including historic buildings, natural attractions, monuments, and archaeological sites. The revealing of those assets is crucial for the advancement of an integrated local strategy for cultural tourism in terms of responsible tourism. The primary objective of this study is to analyze how the development of a cultural routes network, integrating monuments from diverse historical periods, significant religious sites, and areas of significant architectural and environmental value, can contribute to a responsible tourism approach and further a sustainable development that generates multiple benefits for the local community in an insular region.

The demand for a new concept of heritage, in which monuments and landscapes are considered active factors in creating a sense of history, is esteemed not only from a scientific and academic perspective, but as well as part of a more sensitive and efficient strategy to link cultural heritage and tourism, by bringing an integrative perspective to the forefront. Implementing such strategies is strictly correlated with the ability to support decision-makers and to increase people’s awareness towards a more comprehensive approach to heritage preservation. In the present work, a robust socioeconomic impact model is presented. Moreover, this work attempts to create an initial link between the economic impacts and natural hazards induced by the changes in the climatic conditions that cultural heritage sites face. The model’s novel socioeconomic impact analysis is the direct and indirect revenues related to the tourism use of a site, on which local economies are strongly correlated. The analysis indicated that cultural heritage sites provide a range of both market and non-market benefits to society. These benefits provide opportunities for policy interventions for the conservation of the cultural heritage sites and their promotion, but also to their protection against the impacts of climate change and natural disasters.

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of the climate crisis are being increasingly felt, the need for accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on meteorological variables using statistical techniques as well as artificial intelligence (AI) for a specific area of interest utilising data from an in situ meteorological station, and to produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location; in our case, in a hotel in the northern area of Crete, Greece. The temporal resolution of the measurements used was 3 h and the forecast horizon considered here was up to 2 days. As prediction techniques, seasonal autoregressive integrated moving average (SARIMA), AI techniques like the long short-term memory (LSTM) neural network and hybrid combinations of the two are used. Multiple meteorological variables are considered as input for the LSTM and hybrid methodologies, like temperature, relative humidity, atmospheric pressure and wind speed, unlike the SARIMA that has a single variable. Variables of interest are divided into those that present seasonality and patterns, such as temperature and humidity, and those that are more stochastic with no known seasonality and patterns, such as wind speed and direction. Two benchmark techniques are used for comparison and quantification of the added predictive ability, namely the climatological forecast and the persistence model, which shows a considerable amount of improvement over the naive prediction methods, especially in the 1-day forecasts. The results indicate that the examined hybrid methodology performs best at temperature and wind speed forecasts, closely followed by the SARIMA, whereas LSTM performs better overall at the humidity forecast, even after the correction of the hybrid to the SARIMA model. Lastly, different hybrid methodologies are discussed and introduced for further improvement of meteorological predictions.