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Solar power plants are important alternatives to fossil fuel-based power plants because they reduce greenhouse gas emissions and mitigate the effects of climate change. The potential of harnessing solar energy is highly dependent on selecting the optimal locations for plant installation. This study primarily aims to select optimal sites for solar energy plant installation using the Analytic Hierarchy Process (AHP) of Multi-Criteria Decision Analysis (MCDA) in a Geographic Information System (GIS) environment. In the underlying processing phase, the AHP method was used to calculate the relative weights of the study area's physiographic, utility, and climate data. These weights were then used in the MCDA to identify the optimal locations for solar plants. The results of the MCDA were presented in the form of a solar plant suitability map, which showed that 44.59 % (66506.49 km2) of the study area in the south and southwest of Bangladesh is highly suitable for the development of utility-scale solar power plants. In contrast, there are substantial areas of about 31.18 % (46507.37 km2) of the country where the installation of any type of power plant is completely prohibited. This research can help authorities and stakeholders decide where to invest in solar energy. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods: Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results: Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions: Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings. [ABSTRACT FROM AUTHOR]

Background: There is a growing awareness that addressing chronic as well as acute health conditions may contribute importantly to the well-being of displaced populations, but eye care service has generally not been prioritized in crisis situations. We describe a replicable model of eye care provision as delivered by Orbis International and local partners to the Rohingya and host population in Cox's Bazar, Bangladesh, and characterize the burden of vision impairment and demand for sight-restoring services in this setting.Methods and Findings: Orbis International and local secondary facility Cox's Bazar Baitush Sharaf Hospital (CBBSH) provide eye care support to the Rohingya population and the host community of all ages in Cox's Bazar, Bangladesh, with fixed vision screening locations established in Camps 4 and 11 of the Kutupalong refugee settlement. Structured outreach targets these camps and four surrounding local subdistricts, with referrals made as needed for refraction (glasses measurement) and cataract surgery to CBBSH. Between February 2018 and March 2019, 48,105 displaced Rohingya (70.3%, among whom 71.6% were children and 46.5% women) and 20,357 local residents (29.7%, 88.5% children, 54.4% women) underwent vision screening. Displaced Rohingya sought services from a total of 12 surrounding camps, within which coverage was 17.3%, including 43.3% (27,027/62,424) of children aged 5-11 years and 60.0% (5,315/8,857) of adults ≥ 60 years old. The prevalence of blindness (presenting acuity < 3/60) among Rohingya patients exceeded that among local residents by 3- to 6-fold in each 10-year age group between 18 and 59 years (P < 0.001 comparing vision between the two groups in this age range), and the prevalence of cataract requiring surgery was also higher in Rohingya patients (18-29 years: 4.67% versus 1.80%, P = 0.0019; 30-39: 7.61% versus 2.39%, P < 0.001; and 40-49 years: 7.91% versus 3.77%, P = 0.0014). A limitation of the study is lack of data on population prevalence of eye disease.Conclusions: The burden of untreated eye disease is very high among the Rohingya, particularly those in their peak working years who could contribute most to the resiliency of their community. Demand for eye care service is also great among children and adults in this population with many competing healthcare priorities. Research is needed, building on strong evidence of benefit in settled populations, to explore the specific impact of vision care on the well-being of displaced populations. [ABSTRACT FROM AUTHOR]