Despite improved living standards, many individuals continue to engage in outdoor physical exercise despite air pollution. This study explores the psychological mechanisms that facilitate this... Show More

Despite improved living standards, many individuals continue to engage in outdoor physical exercise despite air pollution. This study explores the psychological mechanisms that facilitate this behavior in adverse environmental conditions. Using Social Cognitive Theory as a framework, we investigate the reasons individuals choose to exercise outdoors in polluted weather compared to those who prefer cleaner conditions. Our research includes a statistical analysis of demographic data and an examination of psychological and social factors influencing exercise habits. We distributed a structured questionnaire to 300 participants, receiving 290 responses, of which 280 were valid. By analyzing these responses, we aim to uncover the psychological drivers that sustain commitment to outdoor exercise despite pollution challenges. The findings provide valuable insights into the motivations and mechanisms that encourage individuals to maintain their physical activity in less-than-ideal conditions.

The service sector plays a crucial role in contributing to a nation's GDP, with the healthcare sector experiencing rapid economic growth. Customer satisfaction in healthcare centers is essential and... Show More

The service sector plays a crucial role in contributing to a nation's GDP, with the healthcare sector experiencing rapid economic growth. Customer satisfaction in healthcare centers is essential and expected to remain a priority. To enhance satisfaction, implementing marketing mix strategies across sectors is vital. This study explores the 7Ps' impact on customer satisfaction, considering gender and age factors. Using quantitative techniques and a questionnaire, correlations between satisfaction and variables like the 7Ps, gender, and age were examined. Significant factors identified include process, promotion, place, product, and people. Logistic regression revealed that people and process significantly influence satisfaction. The study underscores process as the primary factor in marketing strategies, offering a key element to improve service and satisfaction. Future research should expand by segmenting the 7Ps based on gender and age groups. These findings provide valuable insights for tailoring healthcare services to better meet diverse patient needs.

Current denoising methods are usually based on noise modeling or deep learning. In order to reduce the inherent noise in optical coherence tomography (OCT) images, a two-stage depth neural network... Show More

Current denoising methods are usually based on noise modeling or deep learning. In order to reduce the inherent noise in optical coherence tomography (OCT) images, a two-stage depth neural network is proposed, which combines the advantages of the two methods. The spatial noise distribution on an OCT image was represented by the gamma noise model. Res-Unet was used as the backbone network for both the noise estimation network and the denoising network. To better differentiate the foreground and background noise, a special context attention module was proposed to guide the network focusing onto the foreground retinal structure. A new edge-sensitive loss function was introduced to make the network preserve the sharp boundary between the retinal structure layers. The experimental results showed that the performance of our proposed method was superior to other methods. The noise was significantly reduced, with the retinal structure and the blood vessel details better preserved by our method. The proposed method can be greatly useful for the ophthalmic diagnosis by using OCT images.

The psychological development of college students is closely linked to their environmental contexts, which significantly impacts their mental health. Traditional mental health education often... Show More

The psychological development of college students is closely linked to their environmental contexts, which significantly impacts their mental health. Traditional mental health education often neglects this ecological perspective, resulting in limited methods and disconnection from students' real-life environments. This paper explores how ecological psychology can enhance mental health education. Using an integrated learning algorithm and the Xgboost model, the authors identify key factors influencing students' well-being. The findings inform a comprehensive, sustainable mental health education framework that integrates both horizontal and vertical strategies. This proposed system is designed to address the holistic needs of students by considering their ecological contexts, ultimately promoting resilience and improving overall psychological health.

Verbal communication with evaluative characters of different emotional valence occurs often in our daily social interaction. Generally, positive evaluative language involving praise and appreciation... Show More

Verbal communication with evaluative characters of different emotional valence occurs often in our daily social interaction. Generally, positive evaluative language involving praise and appreciation triggers social approach whereas negative evaluative language involving criticism and censure triggers social avoidance. Differently, individuals with depression may display distorted social behavior due to their excessive sensitivity to negative evaluative language but blunt to positive information. Based on the eye-tracking experiment, this study employs an evaluative movie watching task to investigate the influence of depression-induced negatively-biased processing for evaluative language on gaze to evaluation-givers' eyes. The results suggest that negative self-schema in depressed individuals contributes to their excessive sensitivity to negative evaluative language, which leads to their avoidance behavior in response to social criticism. The findings have great implications for understanding social dysfunctions in individuals with depressive symptoms.

With the development of Internet of Things and big data technology, mental health education in colleges and universities has ushered in new opportunities and challenges. This study proposes a way to... Show More

With the development of Internet of Things and big data technology, mental health education in colleges and universities has ushered in new opportunities and challenges. This study proposes a way to identify students' mental health problems at an early stage by constructing a decision support system based on data mining technology (such as a decision tree algorithm), and to integrate ideological and political education into this system, so as to promote the formation of mental health and correct values in students. Empirical research shows that this system can significantly improve the early warning capacity of psychological problems and provide a scientific basis for timely intervention. This advancement will not only help to improve the mental health of college students, but will also promote innovation in mental health education and support the all-around development of students.

Accurate emergency call demand forecasting is essential for optimizing resource allocation and response times in Emergency Medical Services (EMS). Time series forecasting, a cornerstone of machine... Show More

Accurate emergency call demand forecasting is essential for optimizing resource allocation and response times in Emergency Medical Services (EMS). Time series forecasting, a cornerstone of machine learning, plays a crucial role in predicting demand patterns. This research proposes a novel multi-series forecasting model for scenarios involving multiple independent time series, representing call data from distinct service areas. While previous research has explored multivariate time series and machine learning methods for EMS demand forecasting, this study focuses on comparing a simplified independent time series approach against the proposed multi-series model. By evaluating the accuracy of both methods in predicting future call volumes, the findings will provide insights into the most effective forecasting approach, ultimately contributing to improved resource allocation and enhanced patient care in EMS.