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Background: There has been increasing interest in online professional learning networks in a variety of social media platforms, especially in Twitter. Twitter offers immediacy, personalization, and support of networks to increase professional knowledge and the sense of membership. Knowing the topics discussed in Twitter and the factors that affect the duration of a topic would help to sustain and reconstruct Twitter‐based professional learning activities. Objectives: The purpose of this study is to analyse the topics discussed and what factors affect the duration of a specific topic in 6 years within a virtual professional learning network (VPLN) using #Edchat in Twitter, based on media richness features. Methods: Internet‐mediated research and digital methods are used for data collection and analysis. Various text, natural language processing, and machine learning algorithms were used along with the quantitative multilevel models. This study examined 504,998 tweets posted by 72,342 unique users by using #Edchat. Results: There were 150 topics discussed over the 6 years and multilevel random intercept regression model revealed that a specific topic discussed in the #Edchat VPLN is discussed longer when it has more tweets, rather than retweets, posted by a high number of different users along with moderate text, high or moderate mentions with more hashtags. Takeaways: The study developed an automated social media richness feature extraction framework that can be adapted for other theoretical applications in educational context. Emergent topics discussed in Twitter among #Edchat VPLN members for professional development were identified. It extends the social media richness theory for educational context and explore factors that affect an online professional learning activity in Twitter. Lay Description: What is already known about this topic: Professional Learning Communities have started to use both synchronous and asynchronous Web 2.0 and social media platforms where they may exchange information and develop a sense of belonging with others who share common interests.Synchronous online activity in social networking sites, such as Twitter, can be an effective, informal and a free way to convey information and create and develop personalized networks.Twitter offers immediacy, personalization, and support of networks to increase professional knowledge and the sense of membership.There have been a great number of hashtags in Twitter for educational context and those hashtags are used by teachers to find information and resources and gain new perspectives and ideas from their colleagues or experts.The richness of a social media post varies according to how it is structured and constructed. What this paper adds: By using #edchat in Twitter, educators discussed 150 topics in 6 years.The most discussed topics are Creating, changing school culture; Classroom management, teaching methods; Classroom settings, Educational technologies; Support – Needs; Students' subject skills and interest; and School environment.A specific topic stays longer when it has more tweets, rather than retweets, posted by a high number of different users.Topics that have moderate text, high or moderate mentions with more hashtags are discussed longer on Twitter.The duration of a topic can be changed according to the educators' behaviours in a synchronous online chat. Implications for practice and/or policy: Importance of social media for professional learning and how to sustain a topic for better learning and understanding.Developing and applying an automated computational discourse analysis social media learning.Applying media richness theory to understand the affordances of social media learning.Quantifying the influence of various factors on the discourse in social media learning. [ABSTRACT FROM AUTHOR]

Background: Sleep disorders have an enormous impact on occupational health and are counterproductive from an economic perspective. However, the processes of causing sleep disorders from psychosocial aspects have not yet been known. The purpose of this study was to describe how sleep disorders develop among workers with respect to different psychosocial conditions. Methods: A conventional qualitative content analysis was conducted with a semi-structured interview among twenty-seven workers (14 males and 13 females) who were diagnosed with sleep disorders or had a self-reported history of sleep difficulties. Study participants were recruited from a specialized clinic and communities using snowball sampling. This paper adhered to the Standards for Reporting Qualitative Research (SRQR) checklist. Results: The results showed that there were four steps involved in the sleep disorders development process. Firstly, participants with sleep disorders developed 'early warning signs' with 11 categories of triggers; secondly, 'aggravating factors' on top of these early warning signs; thirdly, workers tried to 'cope with' their sleep disorders in the ways they thought would be effective. Finally, when coping failed to improve the quality of sleep, it led to the onset of sleep disorders. Conclusion: The development of sleep disorders and triggers of psychosocial factors were revealed. An occupational health nurse can bring these findings in practice for preventing worker's sleep disorders. [ABSTRACT FROM AUTHOR]

Background: People with severe mental illness (SMI) often have poorer physical health than the general population. A coproduced physical activity intervention to improve physical activity for people with SMI in Northern Ireland was evaluated by co‐researchers (researchers with lived experience of SMI) and academic researchers using a new approach to participatory data analysis called participatory theme elicitation (PTE). Objective: Co‐researchers and academic researchers analysed the data from the pilot study using PTE. This paper aimed to compare these analyses to validate the findings of the study and explore the validity of the PTE method in the context of the evaluation of a physical activity intervention for individuals with SMI. Results: There was alignment and congruence of some themes across groups. Important differences in the analyses across groups included the use of language, with the co‐researchers employing less academic and clinical language, and structure of themes generated, with the academic researchers including subthemes under some umbrella themes. Conclusions: The comparison of analyses supports the validity of the PTE approach, which is a meaningful way of involving people with lived experience in research. PTE addresses the power imbalances that are often present in the analysis process and was found to be acceptable by co‐researchers and academic researchers alike. [ABSTRACT FROM AUTHOR]

In the context of the VASelfCare project, we developed an application prototype of an intelligent anthropomorphic virtual assistant. Designed as a relational agent, the virtual assistant has the role of supporting older people with Type 2 Diabetes Mellitus (T2D) in medication adherence and lifestyle changes. Our paper has two goals: describing the essentials of this prototype, and reporting on usability evaluation. We describe the general architecture of the prototype, including the graphical component, and focus on its main feature: the incorporation, in the way the dialogue flows, of Behavior Change Techniques, identified through a theoretical framework, the Behaviour Change Wheel. Usability was experimentally evaluated in field tests in a purposive sample of 20 participants (11 older adults with T2D and 9 experts). The Portuguese version of the System Usability Scale was employed, supplemented with qualitative data from open questions, diaries, digital notes and telephone follow-ups. The aggregated mean SUS score was 73,75 (SD 13,31), which corresponds to a borderline rating of excellent. Textual data were content analyzed and will be prioritized to further improve usability. [ABSTRACT FROM AUTHOR]

In this paper, the problem of secure transmission of sensitive contents over the public network Internet is addressed by proposing a novel data hiding method in encrypted images with dual-level security. The secret information is divided into three blocks using a specific pattern, followed by an encryption mechanism based on the three-level encryption algorithm (TLEA). The input image is scrambled using a secret key, and the encrypted sub-message blocks are then embedded in the scrambled image by cyclic18 least significant bit (LSB) substitution method, utilizing LSBs and intermediate LSB planes. Furthermore, the cover image and its planes are rotated at different angles using a secret key prior to embedding, deceiving the attacker during data extraction. The usage of message blocks division, TLEA, image scrambling, and the cyclic18 LSB method results in an advanced security system, maintaining the visual transparency of resultant images and increasing the security of embedded data. In addition, employing various secret keys for image scrambling, data encryption, and data hiding using the cyclic18 LSB method makes the data recovery comparatively more challenging for attackers. Experimental results not only validate the effectiveness of the proposed framework in terms of visual quality and security compared to other state-of-the-art methods, but also suggest its feasibility for secure transmission of diagnostically important keyframes to healthcare centers and gastroenterologists during wireless capsule endoscopy. [ABSTRACT FROM AUTHOR]

Glaucoma is an eye disease where a loss of vision occurs as a result of progressive optic nerve damage usually associates with high intraocular pressure. A subtype of glaucoma called primary angle-closure glaucoma (PACG) has been observed to be the result of one or more mechanisms such as Pupil block, Plateau iris, Peripheral iris roll, and Lens in the anterior segment of the eye. Reliable features in anterior segment images are important for determining the specific mechanisms involved in PACG. In this paper, first the discriminant features are selected by several feature selection algorithms in the context of PACG detection based on anterior segment optical coherence tomography (AS-OCT) images, and then a novel criteria is proposed to further select more reliable features. Our approach is based on selecting the top-ranked features in each algorithm and its rank combination for selection of the best features. Compared with the features selected by the individual feature selection methods, the features selected by our method achieves the best performance in terms of the accuracy of classification of the four PACG mechanisms by using AdaBoost classifier. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) is a new imaging method that can be used to non-invasively measure the diffusion coefficient of water molecules in biological tissue structures in recent years. Since the DTI data is a tensor space, its segmentation is different from ordinary MRI images. Based on the existing deep learning model, an improved image semantic segmentation method based on super-pixels and conditional random field is proposed. Firstly, this paper uses the existing feature extraction model based on deep learning to obtain rough semantic segmentation results, including high-level semantic information of the image but lacking details of the image. In addition, the super-pixel segmentation algorithm is implemented to obtain super-pixels that carries more low-level information. Secondly, due to the lack of image details in rough segmentation results, the segmentation of the edge of the image is inaccurate. In this paper, a boundary optimization algorithm is proposed to optimize the edge segmentation accuracy of the rough results. Finally, the use of super-pixels for local boundary optimization can improve the segmentation accuracy. Experiments results show that this segment is a practical and effective method. [ABSTRACT FROM AUTHOR]