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In order to solve the problems of traditional mental stress detection in college students that are time-consuming, random, and subjective, this paper proposes an intelligent perception-driven mental stress assessment method for college students. First, we analyze the factors in SRQ and SCL-90, which can be measured by intelligent sensing methods, including sleep, exercise, social interaction, and environment, and then perform feature extraction. Secondly, we use machine learning methods to build a mental stress assessment model. The Shapley additive explanations (SHAP) model is used to explain the training results. Experimental results show that the model proposed in this article can effectively assess the mental stress state of college students. This means that the collection of intelligent perception data based on the mental stress scale can effectively evaluate the mental stress state of college students and provide a new research idea for further developing a non-intrusive and real-time mental stress assessment for college students. [ABSTRACT FROM AUTHOR]

This study conducts an in-depth review of the Social Internet of Things (SIoT), a significant advancement from the conventional Internet of Things (IoT) via the integration of socialization principles akin to human interactions. We explore the architecture, trust management, relationship dynamics, and other crucial aspects of SIoT, with a particular focus on the relatively neglected areas of fault tolerance, cloud–fog computing, and clustering. Our systematic literature analysis, spanning research from 2011 to April 2023, uncovers critical gaps and establishes a detailed taxonomy of emerging SIoT themes. This paper not only sheds light on the current state of SIoT research but also charts a course for future exploration and development in this burgeoning field. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has brought to the forefront the importance of predicting and controlling an epidemic outbreak with policies such as vaccination or reducing social contacts. This paper studies an SIHR epidemic model characterized by susceptible (S), non-seriously infected (I), hospitalized (H), and recovered (R) subpopulations, and dynamic vaccination; vaccination itself and H are fed back, and its dynamics are also determined by a free-design time-dependent function and parameters. From a theoretical analysis, the well-posedness of the model is demonstrated; positivity and the disease-free ( P d f ) and endemic ( P e e ) equilibrium points are analyzed. The controlled reproduction number ( R c ) is proved to be a threshold for the local asymptotic stability of  P d f  and the existence  P e e ; when  R c < 1  ( R c > 1 ), then  P d f  is (not) locally asymptotically stable and  P e e  does not (does) exist. Simulations have been carried out with data concerning COVID-19 where the importance of keeping  R c < 1  to prevent the disease spreading and future deaths is highlighted. We design the control input, since it can be easily adapted to match the user specification, to obtain impulsive and regular vaccination and fulfill the condition  R c < 1 . [ABSTRACT FROM AUTHOR]

This review underscores the critical significance of incorporating networks science in epidemiology. Classic mathematical compartmental models (CMs) employed to describe epidemic spreading may fail to capture the intricacies of real disease dynamics. Rooted in the mean-field assumption, CMs oversimplify by assuming that every individual has the potential to "infect" any other, neglecting the inherent complexity of underlying network structures. Since social interactions follow a networked pattern with specific links between individuals based on social behaviors, joining classic CMs and network science in epidemiology becomes essential for a more authentic portrayal of epidemic spreading. This review delves into noteworthy research studies that, from various perspectives, elucidate how the synergy between networks and CMs can enhance the accuracy of epidemic descriptions. In conclusion, we explore research prospects aimed at further elevating the integration of networks within the realm of epidemiology, recognizing its pivotal role in refining our understanding of disease dynamics. [ABSTRACT FROM AUTHOR]

Existing sentiment prediction methods often only classify users' emotions into a few categories and cannot predict the variation of emotions under different topics. Meanwhile, network embedding methods that consider structural information often assume that links represent positive relationships, ignoring the possibility of negative relationships. To address these challenges, we present an innovative approach in sentiment analysis, focusing on the construction of a denser heterogeneous signed information network from sparse heterogeneous data. We explore the extraction of latent relationships between similar node types, integrating emotional reversal and meta-path similarity for relationship prediction. Our approach uniquely handles user-entity and topic-entity relationships, offering a tailored methodology for diverse entity types within heterogeneous networks. We contribute to a deeper understanding of emotional expressions and interactions in social networks, enhancing sentiment analysis techniques. Experimental results on four publicly available datasets demonstrate the superiority of our proposed model over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]