Abstract Background: To address the lack of social interaction and meaningful activities for persons with dementia (PWD) in nursing homes an artistic Photo-Activity was designed. The present study aims to develop a digital version of the Photo-Activity and to investigate its implementation and impact on nursing home residents with advanced dementia, and their (in)formal carers. Methods: First, within a user-participatory design, a digital-app version of the Photo-Activity will be developed and pilot-tested, in co-creation with (in)formal carers and PWD. Next, the feasibility and effectiveness of the Photo-Activity versus a control activity will be explored in a randomized controlled trial with nursing home residents (N=90), and their (in)formal carers. Residents will be offered the Photo- Activity or the control activity by (in)formal carers during one month. Measurements will be conducted by independent assessors at baseline (T0), after one month (T1) and at follow up, two weeks after T1 (T2). Qualitative and quantitative methods will be used to investigate the effects of the intervention on mood, social interaction and quality of life of the PWD, sense of competence of informal carers, empathy and personal attitude of the formal carers, and quality of the relationship between the PWD, and their (in)formal carers. In addition, a process evaluation will be carried out by means of semi-structured interviews with the participating residents and (in)formal carers. Finally, an implementation package based on the process evaluation will be developed, allowing the scaling up of the intervention to other care institutions. Discussion: Results of the trial will be available for dissemination by Spring 2023. The digital Photo-Activity is expected to promote meaningful connections between the resident with dementia, and their (in)formal carers through the facilitation of person-centered conversations. Trial registration: Netherlands Trial Register: NL9219; registered (21 January 2021); NTR (trialregister.nl)
An illustrative non-technical review was published on Towards Data Science regarding our recent Journal paper “Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning”.While new technologies have changed almost every aspect of our lives, the construction field seems to be struggling to catch up. Currently, the structural condition of a building is still predominantly manually inspected. In simple terms, even nowadays when a structure needs to be inspected for any damage, an engineer will manually check all the surfaces and take a bunch of photos while keeping notes of the position of any cracks. Then a few more hours need to be spent at the office to sort all the photos and notes trying to make a meaningful report out of it. Apparently this a laborious, costly, and subjective process. On top of that, safety concerns arise since there are parts of structures with access restrictions and difficult to reach. To give you an example, the Golden Gate Bridge needs to be periodically inspected. In other words, up to very recently there would be specially trained people who would climb across this picturesque structure and check every inch of it.
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In January 2008 the online photo-hosting site Flickr introduced a new section entitled The Commons. Its two key goals were to show the hidden treasures in the world’s public photography archives to the general public and to give Flickr community members the opportunity to contribute and describe these photos in order to enrich these collections. Surprisingly enough, little empirical research has been done on the actual usage of The Commons by the institutes and Flickr members. In our research we harvested a rich data sample over a 14-week period: 196,822 photos with user-generated content of 1.3 million tags, almost 130,000 comments and more than 22,000 notes. In total, 165,401 members from 188 different countries actively “did something” with the photos. This presentation will analyze this large data sample. In addition to the quantitative findings, we will discuss the qualitative findings regarding the content analysis of tags and comments.