A new study published in the journal Science analyzes the results of a randomized controlled trial (RCT) that showed that brick kiln owners in Bangladesh are willing and able to implement cleaner and more efficient business practices within their operations—without legal enforcement—if they receive the proper training and support, and if those changes are aligned with their profit motives. The study is the first to rigorously demonstrate successful strategies to improve efficiency within the traditional brick kiln industry.

A research team from AMOLF in Amsterdam created a soft robot that walks, hops, and swims — all without a brain, electronics, or AI. Just soft tubes, air, and some clever physics. 

Mercury is released by environmental and human-driven processes. And some forms, specifically methylmercury, are toxic to humans. Therefore, policies and regulations to limit mercury emissions have been implemented across the globe. And, according to research published in ACS ES&T Air, those efforts may be working. Researchers found that atmospheric mercury levels have decreased by almost 70% in the last 20 years, mainly because human-caused emissions have been reduced.

An interdisciplinary team led by University of Pennsylvania's School of Engineering and Applied Science’s Lee C. Bassett and Dani Bassett, also of the School of Arts & Sciences, have studied the mechanical properties of prions, the mysterious shape-shifting proteins that are infamous for mad cow disease yet essential for yeast survival. Their findings could offer key insights into neurological disorders such as Parkinson’s diseases and Creutzfeldt-Jakob disease and pave the way for novel drug delivery systems.

Researchers from Shanghai Institute of Applied Physics and Shanghai Advanced Research Institute utilized Bayesian neural networks (BNN) to achieve highly reliable fitting of photonuclear (γ,n) reaction cross-sections, significantly improving prediction accuracy and generalization capabilities. This advancement enhances the efficiency of experimental data usage and paves the way for future progress in nuclear astrophysics and radiation detection technologies.Researchers from Shanghai Institute of Applied Physics and Shanghai Advanced Research Institute utilized Bayesian neural networks (BNN) to achieve highly reliable fitting of photonuclear (γ,n) reaction cross-sections, significantly improving prediction accuracy and generalization capabilities. This advancement enhances the efficiency of experimental data usage and paves the way for future progress in nuclear astrophysics and radiation detection technologies.