Kenny Frisk (shovelbeaver8)

There has been an increase in school mental health research aimed at producing generalizable knowledge to address longstanding science-to-practice gaps to increase children's access to evidence-based mental health services. Successful dissemination and implementation are both important pieces to address science-to-practice gaps, but there is conceptual and semantic imprecision that creates confusion regarding where dissemination ends and implementation begins, as well as an imbalanced focus in research on implementation relative to dissemination. In this paper, we provide an enhanced operational definition of dissemination; offer a conceptual model that outlines elements of effective dissemination that can produce changes in awareness, knowledge, perceptions, and motivation across different stakeholder groups; and delineate guiding principles that can inform dissemination science and practice. The overarching goal of this paper is to stimulate future research that aims to advance dissemination science and practice in school mental health.Diabetic retinopathy (DR) is a significant reason for the global increase in visual loss. Studies show that timely treatment can significantly bring down such incidents. Hence, it is essential to distinguish the stages and severity of DR to recommend needed medical attention. In this view, this paper presents DRISTI (Diabetic Retinopathy classIfication by analySing reTinal Images), where a hybrid deep learning model composed of VGG16 and capsule network is proposed, which yields statistically significant performance improvement over the state of the art. To validate our claim, we have reported detailed experimental and ablation studies. We have also created an augmented dataset to increase the APTOS dataset's size and check how robust the model is. The five-class training and validation accuracy for the expanded dataset is 99.21 % and 75.50 % . The two-class training and validation accuracy on augmented APTOS is 99.96 % and 97.05 % . Extending the two-class model for the mixed dataset, we get a training and validation accuracy of 99.92 % and 91.43 % , respectively. We have also performed cross-dataset and mixed dataset testing to demonstrate the efficiency of DRISTI.Because of ethnic and cultural violence in Myanmar, approximately a million Rohingya fled to neighboring Bangladesh starting from August 2017, in what the UN has called a "textbook example of ethnic cleansing". Those arriving in Bangladesh were able to escape decade-long ethnic violence in Myanmar, but the Rohingya's immediate destination, Cox's Bazar district is one of the most climate-vulnerable and disaster-prone areas in Bangladesh. learn more Currently, they have been subjected to extreme rainfalls, landslides, and flashfloods. With the COVID-19 pandemic, they continue to face fear and further marginalization in resource-constrained Bangladesh, as well as increased vulnerability due to tropical cyclones, flashfloods, and landslides. The Rohingya in southeast Bangladesh are now at the epicenter of a humanitarian and sustainability crisis. However, their situation is not entirely unique. Millions of displaced, stateless or refugees around the world are facing multi-dimensional crises in various complex geopolitical, and climatic situations. Using the theoretical lens of political ecology and critical development studies, this paper analyzes the sustainability-peace nexus for millions of Rohingya in Myanmar and in Bangladesh. This paper is based on information from various sources, including three ethnographic field visits in recent years, which helped to get local insights into the current sustainability challenges in this humanitarian context. The core arguments of this paper suggest that sustainability-peace nexus will especially be compromised in climate-vulnerable resource-constrained conditions. To overcome this challenge, decolonizing Rohingya solutions would be critical, by engaging the Rohingya in the process of deve