As a new education product in the information age, Massive Open Online Courses (MOOCs) command momentous public attention for their unexpected rise and flexible application. However, the striking contrast between the high rate of registration and the low rate of completion has put their development into a bottleneck. In this paper, we present a semantic analysis model (SMA) to track the emotional tendencies of learners in order to analyze the acceptance of the courses based on big data from homework completion, comments, forums and other real-time update information on the MOOC platforms. Through emotional quantification and machine learning calculations, graduation probability can be predicted for different stages of learning in real time. Especially for learners with emotional tendencies, customized instruction could be made in order to improve completion and graduation rates. Furthermore, we classified the learners into four categories according to course participation time series and emotional states. In the experiments, we made a comprehensive evaluation of the students’ overall learning status by kinds of learners and emotional tendencies. Our proposed method can effectively recognize learners’ emotional tendencies by semantic analysis, providing an effective solution for MOOC personalized teaching, which can help achieve education for sustainable development.

The Institute for Health Metrics and Evaluation (IHME) has stated that over 1.1 billion people suffered from mental disorders globally in 2016, and the burden of mental disorders has continued to grow with impacts on social development. Despite the implementation of strategies for promotion and prevention in mental health WHO’s Comprehensive Mental Health Action Plan 2013–2020, the difficulty of diagnosis of mental disorders makes the objective “To provide comprehensive, integrated, and responsive mental health and social care services in community-based settings” hard to carry out. This paper presents a mental-disorder-aided diagnosis model (MDAD) to quantify the multipolarity sentiment affect intensity of users’ short texts in social networks in order to analyze the 11-dimensional sentiment distribution. We searched the five mental disorder topics and collected data based on Twitter hashtag. Through sentiment distribution similarity calculations and Stochastic Gradient Descent (SGD), people with a high probability of suffering from mental disorder can be detected in real time. In particular, mental health warnings can be made in time for users with an obvious emotional tendency in their tweets. In the experiments, we make a comprehensive evaluation of MDAD by five common adult mental disorders: depressive disorder, anxiety disorder, obsessive-compulsive disorder (OCD), bipolar disorder, and panic disorder. Our proposed model can effectively diagnose common mental disorders by sentiment multipolarity analysis, providing strong support for the prevention and diagnosis of mental disorders.

A variety of technologies that could enhance driving safety are being actively explored, with the aim of reducing traffic accidents by accurately recognizing the driver’s state. In this field, three mainstream detection methods have been widely applied, namely visual monitoring, physiological indicator monitoring and vehicle behavior analysis. In order to achieve more accurate driver state recognition, we adopted a multi-sensor fusion approach. We monitored driver physiological signals, electroencephalogram (EEG) signals and electrocardiogram (ECG) signals to determine fatigue state, while an in-vehicle camera observed driver behavior and provided more information for driver state assessment. In addition, an outside camera was used to monitor vehicle position to determine whether there were any driving deviations due to distraction or fatigue. After a series of experimental validations, our research results showed that our multi-sensor approach exhibited good performance for driver state recognition. This study could provide a solid foundation and development direction for future in-depth driver state recognition research, which is expected to further improve road safety.

Heart rate variability (HRV) serves as a significant physiological measure that mirrors the regulatory capacity of the cardiac autonomic nervous system. It not only indicates the extent of the autonomic nervous system’s influence on heart function but also unveils the connection between emotions and psychological disorders. Currently, in the field of emotion recognition using HRV, most methods focus on feature extraction through the comprehensive analysis of signal characteristics; however, these methods lack in-depth analysis of the local features in the HRV signal and cannot fully utilize the information of the HRV signal. Therefore, we propose the HRV Emotion Recognition (HER) method, utilizing the amplitude level quantization (ALQ) technique for feature extraction. First, we employ the emotion quantification analysis (EQA) technique to impartially assess the semantic resemblance of emotions within the domain of emotional arousal. Then, we use the ALQ method to extract rich local information features by analyzing the local information in each frequency range of the HRV signal. Finally, the extracted features are classified using a logistic regression (LR) classification algorithm, which can achieve efficient and accurate emotion recognition. According to the experiment findings, the approach surpasses existing techniques in emotion recognition accuracy, achieving an average accuracy rate of 84.3%. Therefore, the HER method proposed in this paper can effectively utilize the local features in HRV signals to achieve efficient and accurate emotion recognition. This will provide strong support for emotion research in psychology, medicine, and other fields.

Music therapy is increasingly being used to promote physical health. Emotion semantic recognition is more objective and provides direct awareness of the real emotional state based on electroencephalogram (EEG) signals. Therefore, we proposed a music therapy method to carry out emotion semantic matching between the EEG signal and music audio signal, which can improve the reliability of emotional judgments, and, furthermore, deeply mine the potential influence correlations between music and emotions. Our proposed EER model (EEG-based Emotion Recognition Model) could identify 20 types of emotions based on 32 EEG channels, and the average recognition accuracy was above 90% and 80%, respectively. Our proposed music-based emotion classification model (MEC model) could classify eight typical emotion types of music based on nine music feature combinations, and the average classification accuracy was above 90%. In addition, the semantic mapping was analyzed according to the influence of different music types on emotional changes from different perspectives based on the two models, and the results showed that the joy type of music video could improve fear, disgust, mania, and trust emotions into surprise or intimacy emotions, while the sad type of music video could reduce intimacy to the fear emotion.

Because of rapid economic development and urbanization, water shortage has become a serious problem in the arid region of China. To investigate urban water resource security, the supply demand pressure of water resources and the urban expansion index were analyzed under different developing scenarios in this paper. Based on the economic data of Urumqi, a typical inland city in the arid area, under the present development scenario from 2011 to 2030, a system dynamics model was constructed to simulate the water resource security. The results show that there will be great influence of urban expansion on water resource security in Urumqi in the future. Water resources are projected to become increasingly scarce if the urban expansion is left unchanged in terms of population, economic growth and water-use efficiency. To find a sustainable method for water resource use, four scenarios of urban expansion were set up based on the sensitive variables. Based on comparison of water consumption under the different scenarios, the harmonize scheme for urban water resource security is the best choice for the development of Urumqi. If the impact of urban expansion on urban water resource security alleviates in the future, the main parameters would have to reach a new standard of water use. Reducing the sewage and increasing the reuse proportion of wastewater are also very important for relieving the stress of water shortage. This research can serve as a reference for water resource allocation and urban planning in arid areas.