This research aims to test the effect that the implementation of green practices at a major sport tourism event, the Badminton World Championships in Huelva (Spain), has on the future intention of spectators to return to similar sport events. A total of 523 spectators who attended the event were randomly selected and self-administered in the presence of the interviewer. A confirmatory factor analysis of the model and a multi-group analysis were carried out. Sporting events have a great impact on the environment in which they are organised, mainly when they are linked to tourism, whether at an economic, social or environmental level. The results indicated that green practices indirectly influence spectators’ future intentions through emotions and satisfaction, direct antecedents. In addition, green practices directly affect both image and trust, and indirectly affect satisfaction. In conclusion, green practices are a variable to be taken into account when planning the organisation of a sporting event that aims to consolidate itself in the tourism and sports services market.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

In the present research work, we investigated the use of the image intensifier in the extraction of radiopaque foreign bodies in traumatology. First of all, it is necessary to clarify that this method constitutes an essential component of practically generalized use, in which low current level radiation is used, that is, fluoroscopic radiation, so that it can be applied for a considerably longer time than that of the longest radiographic exposure. This tool works with a tube intended for this purpose, which is known as fluoroscopy. The radiations from the tube pass through the patient and reach the serigraph, on which the image intensifier or fluoroscopic screen is mounted. In the latter case, this is where the chain ends, since it is on this screen that the image is formed and where the physician directly observes the region to be studied. It is also necessary to define that a foreign body is any element foreign to the body that enters it, either through the skin or through any natural orifice such as the eyes, nose, throat, preventing its normal functioning. It was possible to obtain as a result that the advantages of fluoroscopic navigation are the reduction of surgical time and the amount of irradiation, which goes from about 140 seconds without navigation to only 8 seconds, which is a substantial difference. Among the conclusions, it was possible to highlight that in the case of a radiopaque object, it is essential to have an image intensifier for localization of the foreign body during surgery; while in the case of a radiolucent foreign body, it is more advisable to locate it through the clinic, since these tend to form granulomas.

The micro staring hyperspectral imager can simultaneously acquire two spatial and one spectral images, and only record the external orientation elements of the entire hyperspectral image rather than the external orientation elements of each frame of the image, which avoids the geometric instability during scanning, effectively solves the problem of large geometric deformation of the small line scanning hyperspectral imager, and is suitable for the small UAV load platform with unstable attitude. At present, most of the research focuses on the radio-metric correction method of line scan hyperspectral imager. The application time of staring hyperspectral imager is short, and there is no mature data processing re-search at home and abroad, which hinders the application of UAV micro staring hyperspectral imaging system. In this paper, the calibration method of the linearity and variability of the radiation response of the micro staring hyperspectral imager on the UAV is studied, and the effectiveness of this method is quantitatively evaluated. The results show that the hyperspectral image has obvious vignetting effect and strip phenomenon before the correction of radiation response variability. After the correction, the radiation response variation coefficient of pixels in different bands decreases significantly, and the vignetting effect and image strip decrease significantly. In this paper, a multi-target radiometric calibration method is proposed, and the accuracy of radiometric calibration is verified by comparing the calibrated hyperspectral image spectrum with the measured ground object spectrum of the ground spectrometer. The results show that the calibration results of the multi-target radiometric calibration method show better results, especially for the near-infrared band, and the difference with the surface reflectance measured by the spectrometer is small.

The quality of indoor classroom conditions influences the well-being of its occupants, students and teachers. Especially the temperature, outside acceptable limits, can increase the risk of discomfort, illness, stress behaviors and cognitive processes. Assuming the importance of this, in this quantitative observational study, we investigated the relationship between two environmental variables, temperature and humidity, and students’ basic emotions. Data were collected over four weeks in a secondary school in Spain, with environmental variables recorded every 10 minutes using a monitoring kit installed in the classroom, and students’ emotions categorized using Emotion Recognition Technology (ERT). The results suggest that high recorded temperatures and humidity levels are associated with emotional responses among students. While linear regression models indicate that temperature and humidity may influence students’ emotional experiences in the classroom, the explanatory power of these models may be limited, suggesting that other factors could contribute to the observed variability in emotions. The implications and limitations of these findings for classroom conditions and student emotional well-being are discussed. Recognizing the influence of environmental conditions and monitoring them is a step toward establishing smart classrooms.