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CO2 GRO Inc Selected by Canada s Bern, Hague and Madrid Trade Commissioners to Present on Sustainable Agriculture May 25, 2021
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Researchers at the Future Urban Mobility (FM) interdisciplinary research group atSingapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, have created a synthetic framework known as theory-based residual neural network (TB-ResNet), which combines discrete choice models (DCMs) and deep neural networks (DNNs), also known as deep learning, to improve individual decision-making analysis used in travel behavior research.
Transportation Research: Part B, SMART researchers explain their developed TB-ResNet framework and demonstrate the strength of combining the DCMs and DNNs methods, proving that they are highly complementary.
As machine learning is increasingly used in the field of transportation, the two disparate research concepts, DCMs and DNNs, have long been viewed as conflicting methods of research.
SMART Investigates The Science Behind Varying Performance Of Different Colored LEDs
The findings pave the way to develop more efficient next-gen LEDs that cover the entire visible spectrum.
Researchers from the Low Energy Electronic Systems (LEES) interdisciplinary research group at Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, together with MIT and National University of Singapore (NUS), have found a method to quantify the distribution of compositional fluctuations in the indium gallium nitride (InGaN) quantum wells at different indium concentrations.
InGaN light emitting diodes (LEDs) have revolutionized the field of solid-state lighting due to their high efficiencies and durability, and low costs. The color of the LED emission can be changed by varying the indium concentration in the InGaN compound, giving InGaN LEDs the potential to cover the entire visible spectrum. InGaN LEDs with relatively low amounts of indium compar
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