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.

To deal with problems of traditional geographic information collection, such as low real-time, poor authenticity of the data, and unclear description of detailed areas, a design scheme of remote sensing-based geographic information system is proposed. The system mainly consists of information collection, imaging processing, data storage management, scene control and data transmission module. By use of remote sensing technology, the reflected and radiated electromagnetic waves of the target area are collected from a long distance to form an image, and the hue–intensity–saturation (HIS) transformation method is used to enhance the image definition. Weighted fusion algorithm is adopted to process the details of the image. The spatial database stores and manages the text and image data respectively, and establishes the attribute self-correlation mechanism to render the ground objects in the picture with SketchUp software. Finally, using RS422 protocol to transmit information can achieve the effect of multi-purpose, and enhance the anti-interference of the system. The experimental results show that the practical experience of the proposed system is excellent, the geographic information image presented is clear, and the edge details are clearly visible, which can provide users with effective geographic information data.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.