Machine Learning Maps Household Waterborne Disease Risk in Flood-Hit Bangladesh
A machine learning study of 501 households in flood-affected Noakhali and Feni, Bangladesh, identifies the key predictors of waterborne disease ...
A machine learning study of 501 households in flood-affected Noakhali and Feni, Bangladesh, identifies the key predictors of waterborne disease ...
A year-long study of 375 women using an online counseling platform in Bangladesh found that nearly 60 percent screened positive ...
Using Sentinel-5P satellite data and geographically weighted regression, researchers have mapped seasonal air pollution hotspots across four industrial cities in ...
A new theoretical paper argues that the global expansion of formal sustainability education structurally displaces informal community knowledge systems through ...
A qualitative study of 60 respondents in Cumilla City Corporation finds that corruption, weak enforcement, and fragmented coordination, rather than ...
A new postcolonial analysis argues that the neoliberal humanitarian-development nexus, exemplified by the controversial Bhasan Char relocation, has prolonged the ...
A new community-based risk assessment in Sylhet finds that hailstorms have overtaken floods and cyclones as the most significant perceived ...
A Bangladeshi study shows powdered eggshell waste can raise the friction angle of sand from 29 to 38 degrees at ...
A survey of 450 slum households in Khulna, Bangladesh, shows how pandemic job losses cascaded into income loss, shortages, depleted ...
A survey of 534 university students in Sylhet, Bangladesh, links gum health, tooth discoloration, orthodontic considerations and social media comparison ...
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© 2025 Scienmag - Science Magazine