The integration of artificial intelligence (AI)-grounded procedures and the Internet of Things (IoT) is very important in the advancement of smart and intelligent paradigms. These techniques can be applied very efficiently for the development of various sectors, including the solution of mental health issues among students, especially in sports education. The proposed article is a summary and analysis of existing intelligent approaches employed to safeguard against numerous mental health issues during the academic journey of a learner. With the utilization of smart methodologies, it is very feasible to compute the stress or depression level of a student and improve academic performance and skills. With innovative technologies, it is possible to accurately analyze behavioral features and recognize any unwanted pattern for the timely detection of mental health issues. To assist the learners in becoming responsible citizens, it is very efficient to utilize AI for the improvement of their psychological quality and mental performance by reducing anxiety and depression levels. Smart methods can be applied for the recognition of personnel in educational sectors who are facing difficulties in performing their duties and can be motivated to enhance their mental level. The investigation then gathered several significant qualities from the literature already in the field and chose the most prevalent ones. The analytical hierarchy process (AHP) is then used to execute the weighting of these attributes. The Multi-Objective Optimization on the Basis of Research Analysis (MOORA) approach was used to rank the options.
Flag leaf senescence is an important determinant of wheat yield, as leaf senescence occurs in a coordinated manner during grain filling. However, the biological process of early senescence of flag leaves post-anthesis is not clear. In this study, early senescence in wheat was investigated using a high-throughput RNA sequencing technique. A total of 4887 differentially expressed genes (DEGs) were identified, and any showing drastic expression changes were then linked to particular biological processes. A hierarchical cluster analysis implied potential relationships between NAC genes and post-anthesis senescence in the flag leaf. In addition, a large set of genes associated with the synthesis; transport; and signaling of multiple phytohormones (JA, ABA, IAA, ET, SA, BR, and CTK) were expressed differentially, and many DEGs related to ABA and IAA were identified. Our results provide insight into the molecular processes taking place during the early senescence of flag leaves, which may provide useful information in improving wheat yield in the future.
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