Credit: Florida Inventors Hall of Fame
TAMPA, Fla. (April 29, 2021) - Seven Florida Inventors whose discoveries are saving lives, transforming the environment, and creating new technologies will be inducted as the 2021 Class of the Florida Inventors Hall of Fame. Among them are Dean Kamen, often referred to as the modern Thomas Edison due to the breadth and scope of his trailblazing inventions, including the Segway®; and Mark Dean, who holds three patents for the original IBM PC and is co-inventor of the ISA bus which revolutionized modern computing.
The 2021 class also includes Norma Alcantar, whose plant-based technologies have played a critical role in the global food-water-energy nexus and are breaking ground with new biomedical applications; Roberta Goode, whose valve designs for vascular catheters have saved countless lives; Susann Keohane, whose advances in Artificial Intelligence are transforming technology for people with disabilities and the aging population; David Kotic
UA student first Hispanic woman to be accepted to prestigious doctoral program
UA student first Hispanic woman to be accepted to prestigious doctoral program By Shaley Sanders | April 27, 2021 at 10:49 PM MST - Updated April 27 at 10:49 PM
TUCSON, Ariz. (KOLD News 13) - A senior at the University of Arizona just found out she will make history as the first Hispanic woman to be accepted to a prestigious doctoral program overseas.
Jocelyne Rivera hopes her story will inspire other women and people of color to pursue their passion in STEM.
Rivera is a biomedical engineering student at the University of Arizona.
“I encountered amazing people, amazing mentors. People motivated me to push beyond my limits and to further my career,” Rivera said.
Credits: Image courtesy of the Singapore-MIT Alliance for Research and Technology
Previous image
Next image
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 compared to gallium, such as the blue, green, and c
Paving the way for new light-powered devices By combining cutting-edge concepts from theoretical physics, researchers from Penn Engineering developed ‘sypersymmetric microlaser arrays’ that are both stable and have high power density.
The field of photonics involves the study of new ways to generate and harness light, akin to how many of the devices used in everyday life run on electric current. While photonic devices have the potential to transform the current technology paradigm through increased speed, efficiency, and information density, their broad application is limited by the size, strength, and stability of the light sources, often lasers, in these devices.
E-Mail
IMAGE: This visualization shows the deep-learning approach in predicting physical fields given different input geometries. The left figure shows a varying geometry of the composite in which the soft material is. view more
Credit: Courtesy of Zhenze Yang, Markus Buehler, et al
Isaac Newton may have met his match.
For centuries, engineers have relied on physical laws developed by Newton and others to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials.
MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time.