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Pain and depression comorbidity (PD) among older adults in China is common and significantly affects their physical and mental health. The psychosocial factors may affect people's feelings, understanding and expression of pain and depression, leading to inaccurate assessment of this condition. Educational attainment is thought to be associated with either pain or depression. However, we do not yet know the relationship between educational attainment and PD. Using data from the 2018 China Health and Retirement Longitudinal Study in 2018, we analyzed various variables in 7742 individuals aged 60 years and older. Our results indicate significant differences between the PD and non-PD populations in terms of social, lifestyle, and behavioral factors. We observed a significant decrease in the incidence of PD among older adults with higher levels of education (p

The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R2 values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.

Abstract Background Few studies have investigated factors associated with anxiety and depression among patients with erectile dysfunction (ED). This study aimed to investigate associated factors and the prevalence of anxiety and depression in this special group in China. Methods Data from 511 patients with ED aged 18–60 years were collected between July 2021 and April 2022. The 5-item International Index of Erectile Function (IIEF-5) questionnaire, self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were used to evaluate erectile function, anxiety and depression, respectively. Univariate analysis and multivariate linear regression analyses were used to explore the associated factors of depression and anxiety. Results The prevalence of anxiety and depression among ED patients was 38.16% and 64.97%, respectively. The mean anxiety index score was 47.37 ± 6.69 points, and the mean depression index was 54.72 ± 9.10 points. Multiple linear regression analysis showed that worse ED, low education level, and smoking were positively associated with increased risk of anxiety and depression. In addition, younger age, longer onset time, and irregular sleep were positively associated with high risk of anxiety, and irregular exercise was associated with severe depression. Conclusions The prevalence of depression and anxiety in ED patients is high, and the severity of ED, age, education level, smoking, onset time, regular sleep, and exercise were associated with anxiety or depression. Reversible risk factors should be avoided and individualized psychological support services are necessary for ED patients.

Abstract In mining safety and other fields, similar material simulation is the main research method to study the movement and deformation of rock formation and ground surface. However, the inaccurate subsidence laws could be obtained because the strength of the composition materials like gypsum and lime is easily affected by moisture. Therefore, it is crucial to monitor the moisture content when carrying simulation experiments. This paper discussed the feasibility of indirectly measuring the moisture content of similar material models using the three-dimensional (3D) laser scanning reflection intensity through three experiments on similar material specimens. The results showed that the laser reflection intensity was sensitive to the moisture content, incidence angle, and distance with three different relationships and the influence of the two factors could be weakened through the established correction models. However, it was recommended restricting the incidence angle to less than 20° and setting the distance from 4 to 10 m to reduce the complexity of correction. The accuracy of this method reached 1.1% under the monitoring condition of 4 m and the normal incidence, which could meet the requirements for monitoring the moisture content of similar material models. The research results of the paper provide a new method to monitor the moisture content in similar material models.

Despite laws in the United States (e.g., Section 504 of the Rehabilitation Act of 1973, the Americans with Disabilities Act of 1990 and its 2008 Amendments), students with various disabilities continue to experience access barriers to instructional content and inclusion in course activities. Online learning environments can present especially challenging circumstances for disabled students despite the advantages they could potentially bring. In this article, we present the design and development of three self-paced e-learning modules following a three-phased design process to prepare instructional design students to create accessible online learning content. The instructional design planning and development process can provide rich experiences for learning. In this design case, the authors tell the stories of the design team to delineate the recursive three-phased design process, aiming to present (a) the ideation, design, creation, and implementation of the accessibility modules to teach novice instructional designers the importance and methods to create accessible online instructional content and (b) the lessons learned by the design team as a result of the design process.

The implementation of virtual reality (VR) has gained popularity in the organizational settings in responding to the digital transformation in the era of Industry 4.0. VR offers immersive and authentic experiences for learners through simulated real-life scenarios during training. While the affordances have been widely discussed, there is a need to take a systems thinking approach and look at the challenges and side effects associated with the implementation. This systematic review is an effort to comprehensively understand the challenges and side effects along with the exploration of contexts, affordances, and attitudes toward VR applications of the VR initiative for training in organizational contexts. Following a ground theory approach, we analyzed 50 articles published since 2011 that covered a wide range of industries and locations worldwide. The findings from our analysis revealed multiple affordances, challenges, and side effects of applying VR in training. We hope to promote a discussion on VR integration, especially to caution researchers and practitioners the challenges and side effects of this initiative in the design and implementation process, to derive maximum value of the immersive technologies in learning. The results contribute to a comprehensive understanding of applying VR in organizational settings and informs design considerations for successful VR implementation in training programs.

This study examines learner experiences in the Museum of Instructional Design (MID), a 3D Virtual Learning Environment (VLE) developed using Mozilla Spoke and Hubs, focusing on Instructional Design and Technology (IDT) education. The MID utilizes a museum theme as a fundamental design element and allows learners to interact, converse, and develop exhibits to represent the IDT field. Leveraging a multi-phase, multi-method approach, we analyzed the usability and learner experiences within the MID, gathering data via expert evaluations and student interviews. The results highlight the engaging and immersive nature of the MID, while also addressing the technical challenges and varying levels of comfort with technology among learners. The study further proposes several recommendations for course design in 3D immersive environments, underscoring the importance of learner autonomy, clear expectations, tiered challenges, and responsive instruction. The implications of these findings emphasize the potential of 3D VLEs in transforming IDT education and other fields.

As online learners are increasingly diverse, making online learning more inclusive becomes an urgent need. Universal design has been perceived as a viable framework to reach the widest possible range of learners. However, applying universal design to online education has been a challenge to instructors due to different barriers. It is also unclear what evidence-based strategies can be infused into online education. In this systematic review, we selected and analyzed relevant studies and synthesized their designs and findings to explore the trends, strategies, impacts, challenges, and instructors' and students' attitudes toward applying universal design in online environments. We discuss implications for research and practice.